Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Rohan Gupta, Devesh Srivastava, Mehar Sahu, Swati Tiwari, Rashmi K Ambasta, Pravir Kumar, Rohan Gupta, Devesh Srivastava, Mehar Sahu, Swati Tiwari, Rashmi K Ambasta, Pravir Kumar

Abstract

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Keywords: Artificial intelligence; Artificial neural networks; Computer-aided drug design; Deep learning; Drug design and discovery; Drug repurposing; Machine learning; Quantitative structure–activity relationship; Virtual screening.

Conflict of interest statement

There is no conflict of interest declared by the authors.

© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Figures

Fig. 1
Fig. 1
a History of artificial intelligence in healthcare: the first breakthrough of artificial intelligence in healthcare comes in 1950 with the development of turning tests. Later on, in 1975, the first research resource on computers in medicines was developed, followed by NIH's first central AIM workshop marked the importance of artificial intelligence in healthcare. With the development of deep learning in the 2000s and the introduction of DeepQA in 2007, the scope of artificial intelligence in healthcare has increased. Further, in 2010 CAD was applied to endoscopy for the first time, whereas, in 2015, the first Pharmbot was developed. In 2017, the first FDA-approved cloud-based DL application was introduced, which also marked the implementation of artificial intelligence in healthcare. From 2018 to 2020 several AI trials in gastroenterology were performed. b Classification of artificial intelligence: there are seven classifications of artificial intelligence, which are reasoning and problem solving, knowledge representation, planning and social intelligence, perception, machine learning, robotics: motion and manipulation, and natural language processing, as discussed by Russel and Norvig in their book “Artificial Intelligence: A Modern Approach.” Machine learning is further divided into three significant subsets: supervised learning, unsupervised learning, and deep learning, whereas vision is divided into two subsets, such as image recognition and machine vision. Similarly, speech is divided into two subsets: speech to text and text to speech, whereas natural language processing is classified into five main subsets, including classification, machine translation, question answering, text generation, and content extraction. c Artificial intelligence in the healthcare and pharmaceutical industry has five significant applications, which change the entire scenario. These applications include research and discovery, clinical development, manufacturing and supply chain, patient surveillance, and post-market surveillance
Fig. 2
Fig. 2
Application of big data for drug designing and discovery: with the increase in biological and chemical data from the literature, in vitro, in vivo, clinical studies, genomics studies, proteomics studies, metabolomics studies, gene ontology studies, and molecular pathway data, different data repositories have been developed. For instance, ChemSpider, ChEMBL, ZINC, BindingDB, and PubChem are the essential databases for compound synthesis and screening in the drug designing and discovery process. The data stored in the above-said databases were curated and screened out for pharmacological and physicochemical properties of compound necessary for the drug discovery process instead of quantum mechanical calculations such as solvation energy and proton affinity the wave function, atomic forces, and transition state. The high-throughput screened data were subject to filtration based on drug-likeness, PAINS calculation, ADMET analysis, and toxicity. The filtered compounds were subject to artificial intelligence models such as deep learning, random forest, classification and regression, and neural networks for further analysis. These compounds were then subjected to quantitative-structure activity relationship and pharmacophore models followed by molecular docking and molecular dynamics simulations studies. Afterward, the final predicted compounds were visualized for binding energy calculations and active site identification. Thus, the final compound was identified and underwent in vitro and in vivo experimental studies for validation. However, quantum mechanical properties play a crucial role in the process of drug discovery and designing, but these properties cannot directly hamper the process of drug designing. QM methods include ab initio density functional theory and semi-empirical calculations, where accurate calculations use electron correlation methods. QM will become a more prominent tool in the repertoire of the computational medicinal chemist. Therefore, modern QM approaches will play a more direct role in informing and streamlining the drug-discovery process
Fig. 3
Fig. 3
Artificial intelligence in primary and secondary drug screening: in drug discovery and designing pipeline, screening of potential lead is crucial, and artificial intelligence plays a great role in identifying novel and potential lead compounds. There are approximately 106 million chemical structure presents in chemical space from different studies such as OMIC studies, clinical and pre-clinical studies, in vivo assays, and microarray analysis. With machine learning models such as reinforcement models, logistic models, regression models, and generative models, these chemical structures are screened out based on active sites, structure, and target binding ability. The complete drug discovery process through artificial intelligence will take about 14–18 years, which is comparatively less than the traditional drug discovery process. The first step in the drug discovery process is lead identification, in which disease-modifying target protein is identified through reverse docking, bioinformatics analysis, and computational chemical biology. In the second step, primary screening of compounds is done to select potential lead compounds, which can inhibit target protein. This can be done through virtual screening and de novo designing. The next step in the drug discovery process includes lead optimization and lead compound identification through focused library design, drug-like analysis, drug-target reproducibility, and computational biology. Afterward, secondary screening of compounds is performed, followed by pre-clinical trials. The drug discovery process's final step is clinical development through cell-culture analysis, animal model experimentation, and patient analysis
Fig. 4
Fig. 4
a Ligand-based virtual screening: in the drug design and discovery process, ligand-based virtual screening is the most crucial step, which comprises different steps as shown in the figure. The initial step consists of database screening and the 3-D structural model's prediction through the active site for a special target and X-ray structure of complexes. Later on, pharmacophore modeling of selected compounds with selected features is performed, followed by pharmacophore and docking-based virtual screening of compounds. The screened compounds are subjected to different toxicity and physiochemical properties for further analysis. Finally, the lead compounds are subjected to in vitro and in vivo bioassays for validation. b structure-based virtual screening: it is another type of virtual screening applied in the drug discovery process, where target structure preparation and chemical compound library preparation are initial steps. Afterward, structural analysis and binding site prediction are done, followed by molecular docking of compounds with the selected target. Later on, molecular dynamics simulation studies are carried out to validate the screened compounds in silico, followed by experimental validation through bioassays
Fig. 5
Fig. 5
a Quantitative structure–activity relationship workflow: the initial step comprises of data set compilation, where data from public database and literature database are accumulated and compiled, which further divided into different subsets for investigation. Afterward, data set processing is performed, where data pre-processing and curation followed by calculation of molecular descriptors are done. After description calculation, data set processing normalization of data and splitting of data into different sets are performed. In the third step, model construction is performed, where data sets such as internal data and external data are accumulated, and learning algorithms are applied for QSAR modeling. Finally, the statistical calculation is done to measure the model robustness. The final step in the quantitative-structure activity relationship is model evaluation, where the model is evaluated by comparison from previous benchmark models, identifying characteristics features, performance evaluation, and interpretation of essential features. b Drug repurposing or repositioning workflow: the first step is collection of data and data pre-processing followed by computational model generation. The models generated are support vector machines, logistic regression, random forest, deep learning, and matrix factorization. Afterward, the generation of proof-of-concept from a literature source is performed. Later on, evaluation of repositioning models through cross-validation, case analysis, and evaluation metrics is performed. Finally, validation of repurposed drugs is carried out through clinical trials, in vitro studies, and in vivo studies

References

    1. Lipinski CF, Maltarollo VG, Oliveira PR, et al. Advances and perspectives in applying deep learning for drug design and discovery. Front Robot AI. 2019 doi: 10.3389/frobt.2019.00108.
    1. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 doi: 10.1016/j.metabol.2017.01.011.
    1. Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019 doi: 10.1016/j.addr.2019.05.001.
    1. Duch W, Swaminathan K, Meller J. Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des. 2007 doi: 10.2174/138161207780765954.
    1. Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today. 2017 doi: 10.1016/j.drudis.2017.08.010.
    1. Jordan AM. Artificial intelligence in drug design–the storm before the calm? ACS Med Chem Lett. 2018 doi: 10.1021/acsmedchemlett.8b00500.
    1. Goel AK, Davies J (2019) Artificial intelligence. In: The Cambridge Handbook of Intelligence. Cambridge
    1. Harrer S, Shah P, Antony B, Hu J. Artificial Intelligence for Clinical Trial Design. Trends Pharmacol: Sci; 2019.
    1. Zhong F, Xing J, Li X, et al. Artificial intelligence in drug design. Sci China Life Sci. 2018 doi: 10.1007/s11427-018-9342-2.
    1. Brown N, Ertl P, Lewis R, et al. Artificial intelligence in chemistry and drug design. J Comput Aided Mol Des. 2020 doi: 10.1007/s10822-020-00317-x.
    1. Badillo S, Banfai B, Birzele F, et al. An introduction to machine learning. Clin Pharmacol Ther. 2020 doi: 10.1002/cpt.1796.
    1. Dutta Majumdar D. Trends in pattern recognition and machine learning. Def Sci J. 1985 doi: 10.14429/dsj.35.6027.
    1. Kubat M (2017) An Introduction to Machine Learning
    1. Aggarwal M, Murty MN (2021) Deep Learning. In: SpringerBriefs in Applied Sciences and Technology. 10.1007/978-981-33-4022-0_3
    1. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015 doi: 10.1016/j.neunet.2014.09.003.
    1. Hu YH, Hwang JN (2001) Introduction to neural networks for signal processing. In: Handbook of Neural Network Signal Processing. CRC Press, pp 12–41
    1. Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016 doi: 10.15252/msb.20156651.
    1. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 1943;5:115–133. doi: 10.1007/BF02478259.
    1. Turing AM. Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Netherlands: Springer; 2009. Computing machinery and intelligence; pp. 23–65.
    1. Samuel AL. Some studies in machine learning using the game of checkers. IBM J Res Dev. 1959;3:210–229. doi: 10.1147/rd.33.0210.
    1. Rosenblatt F (1957) The Perceptron: A Perceiving and Recognizing Automaton, Report 85–60–1
    1. KELLEY HJ, Gradient theory of optimal flight paths. ARS J. 1960;30:947–954. doi: 10.2514/8.5282.
    1. Dreyfus S. The numerical solution of variational problems. J Math Anal Appl. 1962;5:30–45. doi: 10.1016/0022-247X(62)90004-5.
    1. Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern. 1980;36:193–202. doi: 10.1007/BF00344251.
    1. Fukushima K. Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Netw. 1988;1(2):119–130. doi: 10.1016/0893-6080(88)90014-7.
    1. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323:533–536. doi: 10.1038/323533a0.
    1. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1:541–551. doi: 10.1162/neco.1989.1.4.541.
    1. Watkins CJCH, Dayan P. Q-learning. Mach Learn. 1992;8:279–292. doi: 10.1007/bf00992698.
    1. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–297. doi: 10.1023/A:1022627411411.
    1. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735–1780. doi: 10.1162/neco.1997.9.8.1735.
    1. Ilievski A, Zdraveski V, Gusev M (2018) How CUDA Powers the machine learning revolution. 2018 26th Telecommun Forum, TELFOR 2018 - Proc 420–425.
    1. Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database. Inst Electric Electron Eng IEEE. 2010 doi: 10.1109/CVPR.2009.5206848.
    1. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1
    1. Le Q V, Ranzato M’ A, Monga R, et al (2012) Building High-level Features Using Large Scale Unsupervised Learning.
    1. Jorda M, Valero-Lara P, Pena AJ. Performance evaluation of cuDNN convolution algorithms on NVIDIA volta GPUs. IEEE Access. 2019;7:70461–70473. doi: 10.1109/ACCESS.2019.2918851.
    1. Taigman Y, Yang M, Ranzato M, Wolf L (2014) DeepFace: Closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp 1701–1708
    1. Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Networks. Commun ACM. 2020 doi: 10.1145/3422622.
    1. Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manage. 2015;35:137–144. doi: 10.1016/j.ijinfomgt.2014.10.007.
    1. Brazma A, Kapushesky M, Parkinson H, et al. [20] Data Storage and Analysis in ArrayExpress. Methods Enzymol. 2006;411:370–86. doi: 10.1016/S0076-6879(06)11020-4.
    1. Lo Y-C, Ren G, Honda H, L. Davis K (2020) Artificial Intelligence-Based Drug Design and Discovery. In: Cheminformatics and its Applications: 10.5772/intechopen.89012
    1. Edgar R, Domrachev M, Lash AE. 2002. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res.
    1. Wang Z, Jensen MA, Zenklusen JC (2016) A practical guide to The Cancer Genome Atlas (TCGA). In: Methods in Molecular Biology 1418:111–41: 10.1007/978-1-4939-3578-9_6
    1. Parkinson H, Kapushesky M, Shojatalab M, 2007. ArrayExpress-a public database of microarray experiments and gene expression profiles. Nucleic Acids Res.
    1. van IJzendoorn DGP, Szuhai K, Briaire-De Bruijn IH,, et al. Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLoS Comput Biol. 2019;15:1–19. doi: 10.1371/journal.pcbi.1006826.
    1. Lau A, So HC. Turning genome-wide association study findings into opportunities for drug repositioning. Comput Struct Biotechnol J. 2020;18:1639–1650. doi: 10.1016/j.csbj.2020.06.015.
    1. Beck T, Hastings RK, Gollapudi S, et al. GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur J Hum Genet. 2014 doi: 10.1038/ejhg.2013.274.
    1. Buniello A, Macarthur JAL, Cerezo M, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019 doi: 10.1093/nar/gky1120.
    1. Li J, Yuan X, March ME, et al. Identification of target genes at juvenile idiopathic arthritis GWAS loci in human neutrophils. Front Genet. 2019 doi: 10.3389/fgene.2019.00181.
    1. Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2011 doi: 10.1093/nar/gkq1019.
    1. Jensen MA, Ferretti V, Grossman RL, Staudt LM. The NCI genomic data commons as an engine for precision medicine. Blood. 2017;130(4):453–459. doi: 10.1182/blood-2017-03-735654.
    1. Han Y, Yang J, Qian X, et al. DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies. Nucleic Acids Res. 2019 doi: 10.1093/nar/gkz096.
    1. Guillaume JC. PubMed. Ann Dermatol Venereol. 1998 doi: 10.1002/9783527678679.dg10319.
    1. Canese K, Weis S (2013) PubMed: The bibliographic database. NCBI Handb
    1. Kim S, Chen J, Cheng T, et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021 doi: 10.1093/nar/gkaa971.
    1. Kim S, Chen J, Cheng T, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019 doi: 10.1093/nar/gky1033.
    1. Mendez D, Gaulton A, Bento AP, 2019. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res.
    1. Bento AP, Gaulton A, Hersey A, 2014. The ChEMBL bioactivity database: an update. Nucleic Acids Res.
    1. Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008 doi: 10.1093/nar/gkm958.
    1. Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018 doi: 10.1093/nar/gkx1037.
    1. Keenan AB, Jenkins SL, Jagodnik KM, et al. The library of integrated network-based cellular signatures NIH program: system-level cataloging of human cells response to perturbations. Cell Syst. 2018;6(1):13–24. doi: 10.1016/j.cels.2017.11.001.
    1. Duan Q, Reid SP, Clark NR, et al. L1000CDS2: LINCS L1000 characteristic direction signatures search engine. npj Syst Biol Appl. 2016;2:1–12. doi: 10.1038/npjsba.2016.15.
    1. Rose PW, Prlić A, Altunkaya A, et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res. 2017 doi: 10.1093/nar/gkw1000.
    1. Burley SK, Berman HM, Bhikadiya C, et al. RCSB Protein data bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res. 2019 doi: 10.1093/nar/gky1004.
    1. Xu Z, Yang L, Zhang X, et al. Discovery of potential flavonoid inhibitors against COVID-19 3CL proteinase based on virtual screening strategy. Front Mol Biosci. 2020;7:1–8. doi: 10.3389/fmolb.2020.556481.
    1. Fan Y, Zhang Y, Hua Y, et al. Investigation of machine intelligence in compound cell activity classification. Mol Pharm. 2019 doi: 10.1021/acs.molpharmaceut.9b00558.
    1. Chi CT, Lee MH, Weng CF, Leong MK. In silico prediction of PAMPA effective permeability using a two-QSAR approach. Int J Mol Sci. 2019 doi: 10.3390/ijms20133170.
    1. He S, Zhang X, Lu S, et al. A computational toxicology approach to screen the hepatotoxic ingredients in traditional chinese medicines: polygonum multiflorum thunb as a case study. Biomolecules. 2019 doi: 10.3390/biom9100577.
    1. He S, Zhang C, Zhou P, et al. Herb-induced liver injury: Phylogenetic relationship, structure-toxicity relationship, and herb-ingredient network analysis. Int. J Mol Sci. 2019;20(15):3633. doi: 10.3390/ijms20153633.
    1. Zhang D, hai, Wu K lun, Zhang X,, et al. In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus. J Integr Med. 2020 doi: 10.1016/j.joim.2020.02.005.
    1. Baldi A. Computational approaches for drug design and discovery: an overview. Syst Rev Pharm. 2010;1(1):99. doi: 10.4103/0975-8453.59519.
    1. Lavecchia A, Cerchia C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today. 2016;21(2):288–298. doi: 10.1016/j.drudis.2015.12.007.
    1. Smith JS, Roitberg AE, Isayev O. Transforming computational drug discovery with machine learning and AI. ACS Med Chem Lett. 2018;9(11):1065–1069. doi: 10.1021/acsmedchemlett.8b00437.
    1. Jing Y, Bian Y, Hu Z, et al. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J. 2018;20(3):58. doi: 10.1208/s12248-018-0210-0.
    1. Powles J, Hodson H. Google deepmind and healthcare in an age of algorithms. Health Technol (Berl) 2017 doi: 10.1007/s12553-017-0179-1.
    1. Senior AW, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577:706–710. doi: 10.1038/s41586-019-1923-7.
    1. AlQuraishi M. End-to-End differentiable learning of protein structure. Cell Syst. 2019;8:292–301.e3. doi: 10.1016/j.cels.2019.03.006.
    1. Kalaiarasi C, Manjula S, Kumaradhas P. Combined quantum mechanics/molecular mechanics (QM/MM) methods to understand the charge density distribution of estrogens in the active site of estrogen receptors. RSC Adv. 2019 doi: 10.1039/c9ra08607b.
    1. Schütt KT, Gastegger M, Tkatchenko A, et al. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nat Commun. 2019 doi: 10.1038/s41467-019-12875-2.
    1. Gastegger M, McSloy A, Luya M, et al. A deep neural network for molecular wave functions in quasi-atomic minimal basis representation. J Chem Phys. DOI. 2020 doi: 10.1063/5.0012911.
    1. De Vivo M, Masetti M, Bottegoni G, Cavalli A. Role of molecular dynamics and related methods in drug discovery. J Med Chem. 2016;59(9):4035–4061. doi: 10.1021/acs.jmedchem.5b01684.
    1. Bennett WFD, He S, Bilodeau CL, et al. Predicting small molecule transfer free energies by combining molecular dynamics simulations and deep learning. J Chem Inf Model. 2020 doi: 10.1021/acs.jcim.0c00318.
    1. Bai Q, Tan S, Xu T, et al. MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Brief Bioinform. 2020;00:1–12. doi: 10.1093/bib/bbaa161.
    1. Sterling T, Irwin JJ. ZINC 15-ligand discovery for everyone. J Chem Inf Model. 2015 doi: 10.1021/acs.jcim.5b00559.
    1. Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv. 2018;4:1–15. doi: 10.1126/sciadv.aap7885.
    1. Grzybowski BA, Szymkuć S, Gajewska EP, et al. Chematica: a story of computer code that started to think like a chemist. Chem. 2018;4:390–398. doi: 10.1016/j.chempr.2018.02.024.
    1. Genheden S, Thakkar A, Chadimová V, et al. AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J Cheminform. 2020;12:1–9. doi: 10.1186/s13321-020-00472-1.
    1. Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018;555:604–610. doi: 10.1038/nature25978.
    1. Bøgevig A, Federsel HJ, Huerta F, et al. Route design in the 21st century: the IC SYNTH software tool as an idea generator for synthesis prediction. Org Process Res Dev. 2015;19:357–368. doi: 10.1021/op500373e.
    1. Jang G, Lee T, Hwang S, et al. PISTON: predicting drug indications and side effects using topic modeling and natural language processing. J Biomed Inform. 2018;87:96–107. doi: 10.1016/j.jbi.2018.09.015.
    1. Piñero J, Bravo Á, Queralt-Rosinach N, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017 doi: 10.1093/nar/gkw943.
    1. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019 doi: 10.1093/nar/gky1131.
    1. Szklarczyk D, Santos A, Von Mering C, et al. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44:D380–D384. doi: 10.1093/nar/gkv1277.
    1. Davenport TH, Ronanki R (2018) Artificial intelligence for the real world. Harv Bus Rev
    1. Zhavoronkov A, Vanhaelen Q, Oprea TI. Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology? Clin Pharmacol Ther. 2020 doi: 10.1002/cpt.1795.
    1. Watson O, Cortes-Ciriano I, Taylor A, Watson JA (2018) A decision theoretic approach to model evaluation in computational drug discovery. arXiv.
    1. Tripathy RK, Mahanta S, Paul S. Artificial intelligence-based classification of breast cancer using cellular images. RSC Adv. 2014;4:9349–9355. doi: 10.1039/c3ra47489e.
    1. Samui P, Kothari DP. Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Sci Iran. 2011;18:53–58. doi: 10.1016/j.scient.2011.03.007.
    1. Chan HCS, Shan H, Dahoun T, et al. Advancing Drug Discovery via Artificial Intelligence. Trends Pharmacol Sci. 2019;40:592–604. doi: 10.1016/j.tips.2019.06.004.
    1. Ho CWL, Soon D, Caals K, Kapur J. Governance of automated image analysis and artificial intelligence analytics in healthcare. Clin Radiol. 2019;74:329–337. doi: 10.1016/j.crad.2019.02.005.
    1. Andrysek T (2003) Impact of physical properties of formulations on bioavailability of active substance: Current and novel drugs with cyclosporine. In: Molecular Immunology; 39(17–18):1061–5. 10.1016/s0161-5890(03)00077-4.
    1. Elton DC, Boukouvalas Z, Butrico MS, et al. Applying machine learning techniques to predict the properties of energetic materials. Sci Rep. 2018;8:9059. doi: 10.1038/s41598-018-27344-x.
    1. Tyrchan C, Evertsson E. Matched molecular pair analysis in short: algorithms, applications and limitations. Comput Struct Biotechnol J. 2017;15:86–90. doi: 10.1016/j.csbj.2016.12.003.
    1. Turk S, Merget B, Rippmann F, Fulle S. Coupling matched molecular pairs with machine learning for virtual compound optimization. J Chem Inf Model. 2017;57:3079–3085. doi: 10.1021/acs.jcim.7b00298.
    1. Carpenter KA, Huang X. Machine learning-based virtual screening and its applications to Alzheimer’s drug discovery: a review. Curr Pharm Des. 2018;24:3347–3358. doi: 10.2174/1381612824666180607124038.
    1. Schyman P, Liu R, Desai V, Wallqvist A. vNN web server for ADMET predictions. Front Pharmacol. 2017;8:889. doi: 10.3389/fphar.2017.00889.
    1. Álvarez-Machancoses Ó, Fernández-Martínez JL. Using artificial intelligence methods to speed up drug discovery. Expert Opin Drug Discov. 2019;14(8):769–777. doi: 10.1080/17460441.2019.1621284.
    1. Fleming N. How artificial intelligence is changing drug discovery. Nature. 2018 doi: 10.1038/d41586-018-05267-x.
    1. Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci. 2018 doi: 10.1021/acscentsci.7b00512.
    1. Bruno BJ, Miller GD, Lim CS. Basics and recent advances in peptide and protein drug delivery. Ther. Deliv. 2013;4(11):1443–67. doi: 10.4155/tde.13.104.
    1. Yan J, Bhadra P, Li A, et al. Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning. Mol Ther-Nucleic Acids. 2020;20:882–894. doi: 10.1016/j.omtn.2020.05.006.
    1. Plisson F, Ramírez-Sánchez O, Martínez-Hernández C. Machine learning-guided discovery and design of non-hemolytic peptides. Sci Rep. 2020;10:1–19. doi: 10.1038/s41598-020-73644-6.
    1. Kavousi K, Bagheri M, Behrouzi S, et al. IAMPE: NMR-assisted computational prediction of antimicrobial peptides. J Chem Inf Model. 2020;60:4691–4701. doi: 10.1021/acs.jcim.0c00841.
    1. Yi HC, You ZH, Zhou X, et al. ACP-DL: a deep learning long short-term memory model to predict anticancer peptides using high-efficiency feature representation. Mol Ther-Nucleic Acids. 2019;17:1–9. doi: 10.1016/j.omtn.2019.04.025.
    1. Yu L, Jing R, Liu F, et al. DeepACP: a novel computational approach for accurate identification of anticancer peptides by deep learning algorithm. Mol Ther-Nucleic Acids. 2020;22:862–870. doi: 10.1016/j.omtn.2020.10.005.
    1. Tyagi A, Kapoor P, Kumar R, et al. In silico models for designing and discovering novel anticancer peptides. Sci Rep. 2013;3:1–8. doi: 10.1038/srep02984.
    1. Rao B, Zhang L, Zhang G. ACP-GCN: the identification of anticancer peptides based on graph convolution networks. IEEE Access. 2020;8:176005–176011. doi: 10.1109/access.2020.3023800.
    1. Wu C, Gao R, Zhang Y, De Marinis Y. PTPD: predicting therapeutic peptides by deep learning and word2vec. BMC Bioinformatics. 2019;20:1–8. doi: 10.1186/s12859-019-3006-z.
    1. Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37:1038–1040. doi: 10.1038/s41587-019-0224-x.
    1. McCloskey K, Sigel EA, Kearnes S, et al. Machine learning on DNA-encoded libraries: a new paradigm for hit finding. J Med Chem. 2020;63:8857–8866. doi: 10.1021/acs.jmedchem.0c00452.
    1. Xing G, Liang L, Deng C, et al. Activity prediction of small molecule inhibitors for antirheumatoid arthritis targets based on artificial intelligence. ACS Comb Sci. 2020 doi: 10.1021/acscombsci.0c00169.
    1. Dimmitt S, Stampfer H, Martin JH. When less is more–efficacy with less toxicity at the ED50. Br J Clin Pharmacol. 2017;83(7):1365–1368. doi: 10.1111/bcp.13281.
    1. Shen Y, Liu T, Chen J, et al. Harnessing artificial intelligence to optimize long-term maintenance dosing for antiretroviral-naive adults with HIV-1 Infection. Adv Ther. 2020;3:1900114. doi: 10.1002/adtp.201900114.
    1. Pantuck AJ, Lee D-K, Kee T, et al. Modulating BET bromodomain inhibitor ZEN-3694 and Enzalutamide combination dosing in a metastatic prostate cancer patient using an artificial intelligence platform. Adv Ther. 2018 doi: 10.1002/adtp.201800104.
    1. Julkunen H, Cichonska A, Gautam P, et al. Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nat Commun. 2020 doi: 10.1038/s41467-020-19950-z.
    1. Sharabiani A, Bress A, Douzali E, Darabi H. Revisiting warfarin dosing using machine learning techniques. Comput Math Methods Med. 2015 doi: 10.1155/2015/560108.
    1. Nemati S, Ghassemi MM, Clifford GD. Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. 2016 doi: 10.1109/EMBC.2016.7591355.
    1. Tang J, Liu R, Zhang YL, et al. Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients. Sci Rep. 2017 doi: 10.1038/srep42192.
    1. Hu YH, Tai CT, Tsai CF, Huang MW. Improvement of adequate digoxin dosage: an application of machine learning approach. J Healthc Eng. 2018 doi: 10.1155/2018/3948245.
    1. Imai S, Takekuma Y, Miyai T, Sugawara M. A new algorithm optimized for initial dose settings of vancomycin using machine learning. Biol Pharm Bull. 2020;43:188–193. doi: 10.1248/bpb.b19-00729.
    1. Rollinger JM, Stuppner H, Langer T. Virtual screening for the discovery of bioactive natural products. Prog Drug Res. 2008;65:212–249. doi: 10.1007/978-3-7643-8117-2_6.
    1. Schuster D, Maurer EM, Laggner C, et al. The discovery of new 11β-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening. J Med Chem. 2006;49:3454–3466. doi: 10.1021/jm0600794.
    1. Wu J, Zhang Q, Wu W, et al. WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest. Bioinformatics. 2018;34:2271–2282. doi: 10.1093/bioinformatics/bty070.
    1. Cichonska A, Pahikkala T, Szedmak S, et al. Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinformatics. 2018;34:i509–i518. doi: 10.1093/bioinformatics/bty277.
    1. Babajide Mustapha I, Saeed F. Bioactive molecule prediction using extreme gradient boosting. Molecules. 2016;21:1–11. doi: 10.3390/molecules21080983.
    1. Merget B, Turk S, Eid S, et al. Profiling prediction of kinase inhibitors: toward the virtual assay. J Med Chem. 2017;60:474–485. doi: 10.1021/acs.jmedchem.6b01611.
    1. Arshadi AK, Salem M, Collins J, et al. Deepmalaria: artificial intelligence driven discovery of potent antiplasmodials. Front Pharmacol. 2020 doi: 10.3389/fphar.2019.01526.
    1. Sugaya N. Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins. J Chem Inf Model. 2014;54:2751–2763. doi: 10.1021/ci5003262.
    1. Afolabi LT, Saeed F, Hashim H, Petinrin OO. Ensemble learning method for the prediction of new bioactive molecules. PLoS ONE. 2018;13:1–14. doi: 10.1371/journal.pone.0189538.
    1. Petinrin OO, Saeed F. Bioactive molecule prediction using majority voting-based ensemble method. J Intell Fuzzy Syst. 2018;35:383–392. doi: 10.3233/JIFS-169596.
    1. Liu X, Gao Y, Peng J, et al. TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds. Bioinformatics. 2015 doi: 10.1093/bioinformatics/btv099.
    1. Liu M, Wu Y, Chen Y, et al. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J Am Med Informatics Assoc. 2012;19:28–35. doi: 10.1136/amiajnl-2011-000699.
    1. Jamal S, Goyal S, Shanker A, Grover A. Predicting neurological adverse drug reactions based on biological, chemical and phenotypic properties of drugs using machine learning models. Sci Rep. 2017;7:1–12. doi: 10.1038/s41598-017-00908-z.
    1. Xue R, Liao J, Shao X, et al. Prediction of adverse drug reactions by combining biomedical tripartite network and graph representation model. Chem Res Toxicol. 2020;33:202–210. doi: 10.1021/acs.chemrestox.9b00238.
    1. Raja K, Patrick M, Elder JT, Tsoi LC. Machine learning workflow to enhance predictions of adverse drug reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Sci Rep. 2017;7:1–11. doi: 10.1038/s41598-017-03914-3.
    1. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017 doi: 10.1038/srep42717.
    1. Rost B, Liu J, Nair R, et al. Automatic prediction of protein function. Cell Mol Life Sci. 2003;60:2637–2650. doi: 10.1007/s00018-003-3114-8.
    1. Browne F, Zheng H, Wang H, Azuaje F. From experimental approaches to computational techniques: a review on the prediction of protein-protein interactions. Adv Artif Intell. 2010 doi: 10.1155/2010/924529.
    1. Hale WH. American association for the advancement of science. Sci Am. 1913;75:34–34. doi: 10.1038/scientificamerican01181913-34supp.
    1. Troyanskaya OG, Dolinski K, Owen AB, et al. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae) Proc Natl Acad Sci U S A. 2003;100:8348–8353. doi: 10.1073/pnas.0832373100.
    1. You ZH, Lei YK, Zhu L, et al. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics. 2013;14:1–11. doi: 10.1186/1471-2105-14-S8-S10.
    1. Du X, Sun S, Hu C, et al. DeepPPI: boosting prediction of protein-protein interactions with deep neural networks. J Chem Inf Model. 2017;57:1499–1510. doi: 10.1021/acs.jcim.7b00028.
    1. Cunningham JM, Koytiger G, Sorger PK, AlQuraishi M. Biophysical prediction of protein–peptide interactions and signaling networks using machine learning. Nat Methods. 2020;17:175–183. doi: 10.1038/s41592-019-0687-1.
    1. Chatterjee P, Basu S, Kundu M, et al. PPI_SVM: prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables. Cell Mol Biol Lett. 2011;16:264–278. doi: 10.2478/s11658-011-0008-x.
    1. Lu L, Lu H, Skolnick J. Multiprospector: an algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins Struct Funct Genet. 2002;49:350–364. doi: 10.1002/prot.10222.
    1. Singh R, Park D, Xu J, et al. Struct2Net: a web service to predict protein-protein interactions using a structure-based approach. Nucleic Acids Res. 2010;38:508–515. doi: 10.1093/nar/gkq481.
    1. Dandekar T, Snel B, Huynen M, Bork P. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci. 1998;23:324–328. doi: 10.1016/S0968-0004(98)01274-2.
    1. Keskin O, Tuncbag N, Gursoy A. Predicting protein-protein interactions from the molecular to the proteome level. Chem Rev. 2016;116:4884–4909. doi: 10.1021/acs.chemrev.5b00683.
    1. Lavecchia A, Giovanni C. Virtual screening strategies in drug discovery: a critical review. Curr Med Chem. 2013 doi: 10.2174/09298673113209990001.
    1. Gonczarek A, Tomczak JM, Zaręba S, et al. Interaction prediction in structure-based virtual screening using deep learning. Comput Biol Med. 2018 doi: 10.1016/j.compbiomed.2017.09.007.
    1. Goh GB, Hodas NO, Vishnu A. Deep learning for computational chemistry. J Comput Chem. 2017;38(16):1291–1307. doi: 10.1002/jcc.24764.
    1. Yang X, Wang Y, Byrne R, et al. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 2019;119(18):10520–10594. doi: 10.1021/acs.chemrev.8b00728.
    1. Arciniega M, Lange OF. Improvement of virtual screening results by docking data feature analysis. J Chem Inf Model. 2014 doi: 10.1021/ci500028u.
    1. Feinstein WP, Brylinski M. Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. J Cheminform. 2015 doi: 10.1186/s13321-015-0067-5.
    1. Gazgalis D, Zaka M, Zaka M, et al. Protein binding pocket optimization for virtual high-throughput screening (vHTS) drug discovery. ACS Omega. 2020 doi: 10.1021/acsomega.0c00522.
    1. Carpenter KA, Huang X. Machine learning-based virtual screening and its applications to Alzheimer’s drug discovery: a review. Curr Pharm Des. 2018 doi: 10.2174/1381612824666180607124038.
    1. Serafim MSM, Kronenberger T, Oliveira PR, et al. The application of machine learning techniques to innovative antibacterial discovery and development. Expert Opin Drug Discov. 2020 doi: 10.1080/17460441.2020.1776696.
    1. Melville J, Burke E, Hirst J. Machine learning in virtual screening. Comb Chem High Throughput Screen. 2009 doi: 10.2174/138620709788167980.
    1. Wójcikowski M, Ballester PJ, Siedlecki P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep. 2017 doi: 10.1038/srep46710.
    1. Carpenter KA, Cohen DS, Jarrell JT, Huang X. Deep learning and virtual drug screening. Future Med Chem. 2018;10(21):2557–2567. doi: 10.4155/fmc-2018-0314.
    1. Labbé CM, Rey J, Lagorce D, et al. MTiOpenScreen: a web server for structure-based virtual screening. Nucleic Acids Res. 2015 doi: 10.1093/nar/gkv306.
    1. Schellhammer I, Rarey M. FlexX-Scan: Fast, structure-based virtual screening. Proteins Struct Funct Bioinforma. 2004;57:504–517. doi: 10.1002/prot.20217.
    1. Perez-Castillo Y, Sotomayor-Burneo S, Jimenes-Vargas K, et al. CompScore: boosting structure-based virtual screening performance by incorporating docking scoring function components into consensus scoring. J Chem Inf Model. 2019 doi: 10.1021/acs.jcim.9b00343.
    1. Skalic M, Martínez-Rosell G, Jiménez J, De Fabritiis G. PlayMolecule bindscope: large scale CNN-based virtual screening on the web. Bioinformatics. 2019 doi: 10.1093/bioinformatics/bty758.
    1. Fang Y, Ding Y, Feinstein WP, et al. GeauxDock: accelerating structure-based virtual screening with heterogeneous computing. PLoS ONE. 2016 doi: 10.1371/journal.pone.0158898.
    1. Pires DEV, Veloso WNP, Myung YC, et al. EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa480.
    1. Ibrahim TM, Bauer MR, Boeckler FM. Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization. J Cheminform. 2015 doi: 10.1186/s13321-015-0074-6.
    1. Shin WH, Christoffer CW, Wang J, Kihara D. PL-PatchSurfer2: improved local surface matching-based virtual screening method that is tolerant to target and ligand structure variation. J Chem Inf Model. 2016 doi: 10.1021/acs.jcim.6b00163.
    1. Litfin T, Zhou Y, Yang Y. SPOT-ligand 2: improving structure-based virtual screening by binding-homology search on an expanded structural template library. Bioinformatics. 2017 doi: 10.1093/bioinformatics/btw829.
    1. Ropp PJ, Spiegel JO, Walker JL, et al. GypSUm-DL: An open-source program for preparing small-molecule libraries for structure-based virtual screening. J Cheminform. 2019 doi: 10.1186/s13321-019-0358-3.
    1. Akbar R, Jusoh SA, Amaro RE, Helms V. ENRI: a tool for selecting structure-based virtual screening target conformations. Chem Biol Drug Des. 2017 doi: 10.1111/cbdd.12900.
    1. Kellenberger E, Springael JY, Parmentier M, et al. Identification of nonpeptide CCR5 receptor agonists by structure-based virtual screening. J Med Chem. 2007 doi: 10.1021/jm061389p.
    1. De Graaf C, Rognan D. Selective structure-based virtual screening for full and partial agonists of the β2 adrenergic receptor. J Med Chem. 2008 doi: 10.1021/jm800710x.
    1. Vidler LR, Filippakopoulos P, Fedorov O, et al. Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening. J Med Chem. 2013 doi: 10.1021/jm4011302.
    1. Liu LJ, Leung KH, Chan DSH, et al. Identification of a natural product-like STAT3 dimerization inhibitor by structure-based virtual screening. Cell Death Dis. 2014 doi: 10.1038/cddis.2014.250.
    1. Yang C, Wang W, Chen L, et al. Discovery of a VHL and HIF1α interaction inhibitor with: in vivo angiogenic activity via structure-based virtual screening. Chem Commun. 2016 doi: 10.1039/c6cc04938a.
    1. Zhuang C, Narayanapillai S, Zhang W, et al. Rapid identification of Keap1-Nrf2 small-molecule inhibitors through structure-based virtual screening and hit-based substructure search. J Med Chem. 2014 doi: 10.1021/jm4017174.
    1. Dou X, Jiang L, Wang Y, et al. Discovery of new GSK-3β inhibitors through structure-based virtual screening. Bioorganic Med Chem Lett. 2018 doi: 10.1016/j.bmcl.2017.11.036.
    1. Liu Y, Ren Y, Cao Y, et al. Discovery of a low toxicity O-GlcNAc Transferase (OGT) inhibitor by structure-based virtual screening of natural products. Sci Rep. 2017 doi: 10.1038/s41598-017-12522-0.
    1. Wang Y, Dou X, Jiang L, et al. Discovery of novel glycogen synthase kinase-3α inhibitors: Structure-based virtual screening, preliminary SAR and biological evaluation for treatment of acute myeloid leukemia. Eur J Med Chem. 2019 doi: 10.1016/j.ejmech.2019.03.039.
    1. Wang Q, Xu J, Li Y, et al. Identification of a novel protein arginine methyltransferase 5 inhibitor in non-small cell lung cancer by structure-based virtual screening. Front Pharmacol. 2018 doi: 10.3389/fphar.2018.00173.
    1. Sharma K, Patidar K, Ali MA, et al. Structure-based virtual screening for the identification of high affinity compounds as potent vegfr2 inhibitors for the treatment of renal cell carcinoma. Curr Top Med Chem. 2018 doi: 10.2174/1568026619666181130142237.
    1. Yousuf Z, Iman K, Iftikhar N, Mirza MU. Structure-based virtual screening and molecular docking for the identification of potential multi-targeted inhibitors against breast cancer. Breast Cancer Targets Ther. 2017 doi: 10.2147/BCTT.S132074.
    1. Leão M, Pereira C, Bisio A, et al. Discovery of a new small-molecule inhibitor of p53-MDM2 interaction using a yeast-based approach. Biochem Pharmacol. 2013 doi: 10.1016/j.bcp.2013.01.032.
    1. Gahlawat A, Kumar N, Kumar R, et al. Structure-based virtual screening to discover potential lead molecules for the SARS-CoV-2 main protease. J Chem Inf Model. 2020 doi: 10.1021/acs.jcim.0c00546.
    1. Selvaraj C, Dinesh DC, Panwar U, et al. Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19. J Biomol Struct Dyn. 2020 doi: 10.1080/07391102.2020.1778535.
    1. Cruz JV, Neto MFA, Silva LB, et al. Identification of novel protein kinase receptor type 2 inhibitors using pharmacophore and structure-based virtual screening. Molecules. 2018 doi: 10.3390/molecules23020453.
    1. Kannan S, Melesina J, Hauser AT, et al. Discovery of inhibitors of schistosoma mansoni hdac8 by combining homology modeling, virtual screening, and in vitro validation. J Chem Inf Model. 2014 doi: 10.1021/ci5004653.
    1. Zoete V, Daina A, Bovigny C, Michielin O. SwissSimilarity: a web tool for low to ultra high throughput ligand-based virtual screening. J Chem Inf Model. 2016 doi: 10.1021/acs.jcim.6b00174.
    1. Imbernón B, Cecilia JM, Pérez-Sánchez H, Giménez D. METADOCK: a parallel metaheuristic schema for virtual screening methods. Int J High Perform Comput Appl. 2018 doi: 10.1177/1094342017697471.
    1. Riniker S, Landrum GA. Open-source platform to benchmark fingerprints for ligand-based virtual screening. J Cheminform. 2013 doi: 10.1186/1758-2946-5-26.
    1. Li H, Leung KS, Wong MH, Ballester PJ. USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques. Nucleic Acids Res. 2016 doi: 10.1093/nar/gkw320.
    1. Suzuki SD, Ohue M, Akiyama Y. PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM. Artif Life Robot. 2018 doi: 10.1007/s10015-017-0416-8.
    1. Patel H, Brinkjost T, Koch O. PyGOLD: a python based API for docking based virtual screening workflow generation. Bioinformatics. 2017 doi: 10.1093/bioinformatics/btx197.
    1. Banegas-Luna AJ, Cerón-Carrasco JP, Puertas-Martín S, Pérez-Sánchez H. BRUSELAS: HPC generic and customizable software architecture for 3D ligand-based virtual screening of large molecular databases. J Chem Inf Model. 2019 doi: 10.1021/acs.jcim.9b00279.
    1. Wang L, Pang X, Li Y, et al. RADER: a rapid decoy retriever to facilitate decoy based assessment of virtual screening. Bioinformatics. 2017 doi: 10.1093/bioinformatics/btw783.
    1. Mochizuki M, Suzuki SD, Yanagisawa K, et al. QEX: target-specific druglikeness filter enhances ligand-based virtual screening. Mol Divers. 2019 doi: 10.1007/s11030-018-9842-3.
    1. Zhang H, Liao L, Cai Y, et al. IVS2vec: a tool of inverse virtual screening based on word2vec and deep learning techniques. Methods. 2019 doi: 10.1016/j.ymeth.2019.03.012.
    1. Arcon JP, Modenutti CP, Avendaño D, et al. AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions. Bioinformatics. 2019 doi: 10.1093/bioinformatics/btz152.
    1. Ebejer JP, Finn PW, Wong WK, et al. Ligity: a non-superpositional, knowledge-based approach to virtual screening. J Chem Inf Model. 2019 doi: 10.1021/acs.jcim.8b00779.
    1. Zhu Z, Wang X, Yang Y, et al. D3Similarity: a ligand-based approach for predicting drug targets and for virtual screening of active compounds against COVID-19. ChemRxiv. 2020 doi: 10.26434/chemrxiv.11959323.v1.
    1. Bharti DR, Hemrom AJ, Lynn AM. GCAC: Galaxy workflow system for predictive model building for virtual screening. BMC Bioinformatics. 2019 doi: 10.1186/s12859-018-2492-8.
    1. Kong Y, Bender A, Yan A. Identification of Novel Aurora Kinase A (AURKA) Inhibitors via Hierarchical Ligand-Based Virtual Screening. J Chem Inf Model. 2018 doi: 10.1021/acs.jcim.7b00300.
    1. Musumeci D, Amato J, Zizza P, et al. Tandem application of ligand-based virtual screening and G4-OAS assay to identify novel G-quadruplex-targeting chemotypes. Biochim Biophys Acta - Gen Subj. 2017 doi: 10.1016/j.bbagen.2017.01.024.
    1. Yu M, Gu Q, Xu J. Discovering new PI3Kα inhibitors with a strategy of combining ligand-based and structure-based virtual screening. J Comput Aided Mol Des. 2018 doi: 10.1007/s10822-017-0092-8.
    1. Halim SA, Khan S, Khan A, et al. Targeting dengue virus NS-3 Helicase by Ligand based Pharmacophore Modeling and structure based virtual screening. Front Chem. 2017 doi: 10.3389/fchem.2017.00088.
    1. Debnath S, Debnath T, Bhaumik S, et al. Discovery of novel potential selective HDAC8 inhibitors by combine ligand-based, structure-based virtual screening and in-vitro biological evaluation. Sci Rep. 2019 doi: 10.1038/s41598-019-53376-y.
    1. Fu Y, Sun YN, Yi KH, et al. 3D pharmacophore-based virtual screening and docking approaches toward the discovery of novel HPPD inhibitors. Molecules. 2017 doi: 10.3390/molecules22060959.
    1. Krishna S, Shukla S, Lakra AD, et al. Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach. J Mol Graph Model. 2017 doi: 10.1016/j.jmgm.2017.05.014.
    1. Pérez-Nueno VI, Pettersson S, Ritchie DW, et al. Discovery of novel HIV entry inhibitors for the CXCR4 receptor by prospective virtual screening. J Chem Inf Model. 2009 doi: 10.1021/ci800468q.
    1. Hofmarcher M, Mayr A, Rumetshofer E, et al. Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. SSRN Electron J. 2020 doi: 10.2139/ssrn.3561442.
    1. Amin SA, Ghosh K, Gayen S, Jha T. Chemical-informatics approach to COVID-19 drug discovery: monte carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors. J Biomol Struct Dyn. 2020 doi: 10.1080/07391102.2020.1780946.
    1. Ferraz WR, Gomes RA, Novaes ALS, Goulart Trossini GH. Ligand and structure-based virtual screening applied to the SARS-CoV-2 main protease: an in silico repurposing study. Future Med Chem. 2020 doi: 10.4155/fmc-2020-0165.
    1. Choudhary S, Malik YS, Tomar S. Identification of SARS-CoV-2 Cell entry inhibitors by drug repurposing using in silico structure-based virtual screening approach. Front Immunol. 2020 doi: 10.3389/fimmu.2020.01664.
    1. Xiao T, Qi X, Chen Y, Jiang Y. Development of Ligand-based big data deep neural network models for virtual screening of large compound libraries. Mol Inform. 2018 doi: 10.1002/minf.201800031.
    1. Hu J, Liu Z, Yu DJ, Zhang Y (2018) LS-align: An atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening. In: Bioinformatics 34(13): 2209–2218;
    1. Ha EJ, Lwin CT, Durrant JD. LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates. J Cheminform. 2020 doi: 10.1186/s13321-020-00471-2.
    1. Spiegel JO, Durrant JD. AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. J Cheminform. 2020 doi: 10.1186/s13321-020-00429-4.
    1. Chen P, Ke Y, Lu Y, et al. Dligand2: an improved knowledge-based energy function for protein–ligand interactions using the distance-scaled, finite, ideal-gas reference state. J Cheminform. 2019 doi: 10.1186/s13321-019-0373-4.
    1. Gattani S, Mishra A, Hoque MT. StackCBPred: a stacking based prediction of protein-carbohydrate binding sites from sequence. Carbohydr Res. 2019 doi: 10.1016/j.carres.2019.107857.
    1. Li X, Yan X, Yang Y, et al. LSA: a local-weighted structural alignment tool for pharmaceutical virtual screening. RSC Adv. 2019 doi: 10.1039/c8ra08915a.
    1. Seifert MHJ. ProPose: steered virtual screening by simultaneous protein-ligand docking and ligand-ligand alignment. J Chem Inf Model. 2005 doi: 10.1021/ci0496393.
    1. Schellhammer I, Rarey M. TrixX: Structure-based molecule indexing for large-scale virtual screening in sublinear time. J Comput Aided Mol Des. 2007 doi: 10.1007/s10822-007-9103-5.
    1. Lagarde N, Goldwaser E, Pencheva T, et al. A free web-based protocol to assist structure-based virtual screening experiments. Int J Mol Sci. 2019 doi: 10.3390/ijms20184648.
    1. Rifaioglu AS, Nalbat E, Atalay V, et al. DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chem Sci. 2020 doi: 10.1039/c9sc03414e.
    1. Obrezanova O, Segall MD. Gaussian processes for classification: QSAR modeling of ADMET and target activity. J Chem Inf Model. 2010 doi: 10.1021/ci900406x.
    1. Wu Z, Zhu M, Kang Y, et al. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets. Brief Bioinform. 2020 doi: 10.1093/bib/bbaa321.
    1. Obrezanova O, Csányi G, Gola JMR, Segall MD. Gaussian processes: a method for automatic QSAR modeling of ADME properties. J Chem Inf Model. 2007 doi: 10.1021/ci7000633.
    1. Benfenati E, Manganaro A, Gini G (2013) VEGA-QSAR: AI inside a platform for predictive toxicology. In: CEUR Workshop Proceedings
    1. Ambure P, Halder AK, González Díaz H, Cordeiro MNDS. QSAR-Co: an open source software for developing robust multitasking or multitarget classification-based QSAR models. J Chem Inf Model. 2019 doi: 10.1021/acs.jcim.9b00295.
    1. Chen S, Xue D, Chuai G, et al. FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa1006.
    1. Olier I, Sadawi N, Bickerton GR, et al. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Mach Learn. 2018 doi: 10.1007/s10994-017-5685-x.
    1. Soufan O, Ba-Alawi W, Magana-Mora A, et al. DPubChem: a web tool for QSAR modeling and high-throughput virtual screening. Sci Rep. 2018 doi: 10.1038/s41598-018-27495-x.
    1. Karpov P, Godin G, Tetko IV. Transformer-CNN: swiss knife for QSAR modeling and interpretation. J Cheminform. 2020 doi: 10.1186/s13321-020-00423-w.
    1. Wang Y-L, Wang F, Shi X-X, et al. Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery. Brief Bioinform. 2020 doi: 10.1093/bib/bbaa276.
    1. Goh GB, Siegel C, Vishnu A, et al (2017) Chemception: A deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models. arXiv
    1. Reis J, Cagide F, Chavarria D, et al. Discovery of new chemical entities for old targets: insights on the lead optimization of chromone-based monoamine oxidase B (MAO-B) inhibitors. J Med Chem. 2016 doi: 10.1021/acs.jmedchem.6b00527.
    1. Hoelz L, Horta B, Araújo J, et al. Quantitative structure-activity relationships of antioxidant phenolic compounds. J Chem Pharm Res. 2010;2(5):291–306.
    1. Zhang Y, Han Z, Gao Q, et al. Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches. Curr Pharm Des. 2019 doi: 10.2174/1381612825666191107092214.
    1. Halder AK, Giri AK, Dias Soeiro Cordeiro MN. Multi-target chemometric modelling, fragment analysis and virtual screening with ERK inhibitors as potential anticancer agents. Molecules. 2019 doi: 10.3390/molecules24213909.
    1. Halder AK, Cordeiro MNDS. Development of multi-target chemometric models for the inhibition of class I PI3K enzyme isoforms: a case study using QSAR-Co tool. Int J Mol Sci. 2019 doi: 10.3390/ijms20174191.
    1. Kim S, Cho KH. PyQSAR: a fast QSAR modeling platform using machine learning and jupyter notebook. Bull Korean Chem Soc. 2019 doi: 10.1002/bkcs.11638.
    1. Ben Geoffrey AS, Christian Prasana J, Muthu S. Structure-activity relationship of Quercetin and its tumor necrosis factor alpha inhibition activity by computational and machine learning methods. Mater Today Proc. 2020 doi: 10.1016/j.matpr.2020.07.464.
    1. Ben Geoffrey A S, Rafal Madaj, Akhil Sanker, Mario Sergio Valdés Tresanco, Host Antony Davidd, Gitanjali Roy, Rinnu Sarah Saji, Abdulbasit Haliru Yakubu BM Automated In Silico Identification of Drug Candidates for Coronavirus Through a Novel Programmatic Tool and Extensive Computational (MD, DFT) Studies of Select Drug Candidatesl;
    1. Žuvela P, David J, Wong MW. Interpretation of ANN-based QSAR models for prediction of antioxidant activity of flavonoids. J Comput Chem. 2018 doi: 10.1002/jcc.25168.
    1. Ding Q, Hou S, Zu S, et al. VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa187.
    1. Gadaleta D, Manganelli S, Roncaglioni A, et al. QSAR modeling of ToxCast assays relevant to the molecular initiating events of AOPs leading to hepatic steatosis. J Chem Inf Model. 2018 doi: 10.1021/acs.jcim.8b00297.
    1. Hermansyah O, Bustamam A, Yanuar A (2020) Virtual Screening of DPP-4 Inhibitors Using QSAR-Based Artificial Intelligence and Molecular Docking of Hit Compounds to DPP-8 and DPP-9 Enzymes. 10.21203/rs.2.22282/v1
    1. Tian Y, Zhang S, Yin H, Yan A. Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods. Chemom Intell Lab Syst. 2020 doi: 10.1016/j.chemolab.2019.103888.
    1. Wei Y, Li W, Du T, et al. Targeting HIV/HCV coinfection using a machine learning-based multiple quantitative structure-Activity Relationships (Multiple QSAR) Method. Int J Mol Sci. 2019 doi: 10.3390/ijms20143572.
    1. Michel Kana (2020) Handling Missing Data For Advanced Machine Learning
    1. Kumar S (2020) 7 Ways to Handle Missing Values in Machine Learning | by Satyam Kumar | Towards Data Science
    1. Gad SC. QSAR. In: Third E, editor. Wexler PBT- Encyclopedia of Toxicology. Oxford: Academic Press; 2014. pp. 1–9.
    1. Neves BJ, Braga RC, Melo-Filho CC, et al. QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery. Front Pharmacol. 2018;9:1275. doi: 10.3389/fphar.2018.01275.
    1. Roy K, Kar S, Das RN (2015) Chapter 9 - Newer QSAR Techniques. In: Roy K, Kar S, Das RN, Book Title- Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment (eds). Academic Press, Boston,
    1. Kwon S, Bae H, Jo J, Yoon S. Comprehensive ensemble in QSAR prediction for drug discovery. BMC Bioinformatics. 2019;20:521. doi: 10.1186/s12859-019-3135-4.
    1. Roy K, Kar S, Das RN (2015) Chapter 12 - Future Avenues. In: Roy K, Kar S, Das RN, Book Title- Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment (eds). Academic Press, Boston, pp 455–462.
    1. Paolini GV, Shapland RHB, Van Hoorn WP, et al. Global mapping of pharmacological space. Nat Biotechnol. 2006 doi: 10.1038/nbt1228.
    1. Koch U, Hamacher M, Nussbaumer P (2014) Cheminformatics at the interface of medicinal chemistry and proteomics. Biochim Biophys Acta-Proteins Proteomics 1844(1):156–61; 10.1016/j.bbapap.2013.05.010
    1. Makhouri FR, Ghasemi JB. Combating diseases with computational strategies used for drug design and discovery. Curr Top Med Chem. 2019 doi: 10.2174/1568026619666190121125106.
    1. Würth R, Thellung S, Bajetto A, et al. Drug-repositioning opportunities for cancer therapy: novel molecular targets for known compounds. Drug Discov Today. 2016;21(1):190–199. doi: 10.1016/j.drudis.2015.09.017.
    1. Joachim Haupt V, Schroeder M. Old friends in new guise: repositioning of known drugs with structural bioinformatics. Brief Bioinform. 2011 doi: 10.1093/bib/bbr011.
    1. Butcher EC. Can cell systems biology rescue drug discovery? Nat Rev Drug Discov. 2005 doi: 10.1038/nrd1754.
    1. Iyengar R, Zhao S, Chung SW, et al. Merging systems biology with pharmacodynamics. Sci Transl Med. 2012;4(126):126ps7. doi: 10.1126/scitranslmed.3003563.
    1. Martínez V, Navarro C, Cano C, et al. DrugNet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med. 2015 doi: 10.1016/j.artmed.2014.11.003.
    1. Zhang W, Xu H, Li X, et al. DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa062.
    1. Luo H, Zhang P, Cao XH, et al. DPDR-CPI, a server that predicts drug positioning and drug repositioning via chemical-protein interactome. Sci Rep. 2016 doi: 10.1038/srep35996.
    1. Zhu Q, Tao C, Shen F, Chute CG (2014) Exploring the pharmacogenomics knowledge base (pharmgkb) for repositioning breast cancer drugs by leveraging Web ontology language (owl) and cheminformatics approaches. In: Pacific Symposium on Biocomputing
    1. Gallo K, Goede A, Eckert A, et al. PROMISCUOUS 2.0: a resource for drug-repositioning. Nucleic Acids Res. 2020 doi: 10.1093/nar/gkaa1061.
    1. Luo H, Li M, Wang S, et al. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics. 2018 doi: 10.1093/bioinformatics/bty013.
    1. Yella JK, Jegga AG. MGATRx: discovering drug repositioning candidates using multi-view graph attention. biorxiv. 2020 doi: 10.1101/2020.06.29.171876.
    1. Yan CK, Wang WX, Zhang G, et al. BiRWDDA: a novel drug repositioning method based on multisimilarity fusion. J Comput Biol. 2019 doi: 10.1089/cmb.2019.0063.
    1. Fahimian G, Zahiri J, Arab SS, Sajedi RH. RepCOOL: computational drug repositioning via integrating heterogeneous biological networks. biorxiv. 2019 doi: 10.1101/817882.
    1. Li Z, Yao Y, Cheng X, et al (2020) A Computational Framework of Host-Based Drug Repositioning for Broad-Spectrum Antivirals against RNA Viruses. 10.26434/chemrxiv.12927260.v1
    1. Wu D, Gao W, Li X, et al. Dr AFC: drug repositioning through anti-fibrosis characteristic. Brief Bioinform. 2020 doi: 10.1093/bib/bbaa115.
    1. Hooshmand SA, Zarei Ghobadi M, Hooshmand SE, et al. A multimodal deep learning-based drug repurposing approach for treatment of COVID-19. Mol Divers. 2020 doi: 10.1007/s11030-020-10144-9.
    1. Zhou Y, Hou Y, Shen J, et al. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov. 2020 doi: 10.1038/s41421-020-0153-3.
    1. Zheng X, He S, Song X, et al (2018) DTI-RCNN: New efficient hybrid neural network model to predict drug–target interactions. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    1. Jarada TN, Rokne JG, Alhajj R. SNF–CVAE: computational method to predict drug–disease interactions using similarity network fusion and collective variational autoencoder. Knowledge-Based Syst. 2020 doi: 10.1016/j.knosys.2020.106585.
    1. Xu R, Wang QQ. PhenoPredict: a disease phenome-wide drug repositioning approach towards schizophrenia drug discovery. J Biomed Inform. 2015 doi: 10.1016/j.jbi.2015.06.027.
    1. Wu Z, Cheng F, Li J, et al. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform. 2017 doi: 10.1093/bib/bbw012.
    1. Zeng X, Zhu S, Liu X, et al. DeepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics. 2019 doi: 10.1093/bioinformatics/btz418.
    1. Chen H, Cheng F, Li J. IDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding. PLoS Comput Biol. 2020 doi: 10.1371/journal.pcbi.1008040.
    1. Li B, Dai C, Wang L, et al. A novel drug repurposing approach for non-small cell lung cancer using deep learning. PLoS ONE. 2020 doi: 10.1371/journal.pone.0233112.
    1. Kuenzi BM, Park J, Fong SH, et al. Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell. 2020 doi: 10.1016/j.ccell.2020.09.014.
    1. Wang Z, Zhou M, Arnold C. Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa437.
    1. Pinzi L, Rastelli G. Molecular docking: Shifting paradigms in drug discovery. Int J Mol Sci. 2019 doi: 10.3390/ijms20184331.
    1. Muhammed MT, Aki-Yalcin E. Homology modeling in drug discovery: overview, current applications, and future perspectives. Chem Biol Drug Des. 2019;93:12–20. doi: 10.1111/cbdd.13388.
    1. Lynch SR, Bothwell T, Campbell L, et al. A comparison of physical properties, screening procedures and a human efficacy trial for predicting the bioavailability of commercial elemental iron powders used for food fortification. Int J Vitam Nutr Res. 2007 doi: 10.1024/0300-9831.77.2.107.
    1. Schneider P, Walters WP, Plowright AT, et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov. 2020;19:353–364. doi: 10.1038/s41573-019-0050-3.
    1. Chen H, Engkvist O, Wang Y, et al. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–1250. doi: 10.1016/j.drudis.2018.01.039.
    1. Lusci A, Pollastri G, Baldi P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model. 2013 doi: 10.1021/ci400187y.
    1. Kumar R, Sharma A, Siddiqui MH, Tiwari RK. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Curr Drug Discov Technol. 2017 doi: 10.2174/1570163814666170404160911.
    1. Zang Q, Mansouri K, Williams AJ, et al. In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. J Chem Inf Model. 2017 doi: 10.1021/acs.jcim.6b00625.
    1. Tetko IV, Gasteiger J, Todeschini R, et al. Virtual computational chemistry laboratory-design and description. J Comput Aided Mol Des. 2005 doi: 10.1007/s10822-005-8694-y.
    1. Radchenko E V, Palyulin VA, Zefirov NS (2002) Virtual computational chemistry laboratory. System
    1. Royal Society of Chemistry . ChemSpider. Chem: Search and Share Chemistry. R. Soc; 2015.
    1. Kucukdereli H, Allen NJ, Lee AT, et al. Control of excitatory CNS synaptogenesis by astrocyte-secreted proteins hevin and SPARC. Proc Natl Acad Sci U S A. 2011 doi: 10.1073/pnas.1104977108.
    1. Ayati A, Falahati M, Irannejad H, Emami S. Synthesis, in vitro antifungal evaluation and in silico study of 3-azolyl-4-chromanone phenylhydrazones. DARU, J Pharm Sci. 2012 doi: 10.1186/2008-2231-20-46.
    1. Rashid M. Design, synthesis and ADMET prediction of bis-benzimidazole as anticancer agent. Bioorg Chem. 2020 doi: 10.1016/j.bioorg.2020.103576.
    1. Puratchikody A, Sriram D, Umamaheswari A, Irfan N. 3-D structural interactions and quantitative structural toxicity studies of tyrosine derivatives intended for safe potent inflammation treatment. Chem Cent J. 2016 doi: 10.1186/s13065-016-0169-9.
    1. Nascimento ACA, Prudêncio RBC, Costa IG. A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinformatics. 2016 doi: 10.1186/s12859-016-0890-3.
    1. Öztürk H, Özgür A, Ozkirimli E (2018) A chemical language based approach for protein-Ligand interaction prediction. arXiv 10.1002/minf.202000212
    1. Nascimento ACA, Prudêncio RBC, Costa IG. A drug-target network-based supervised machine learning repurposing method allowing the use of multiple heterogeneous information sources. Methods Mol Biol. 2019;1903:281–289. doi: 10.1007/978-1-4939-8955-3_17.
    1. Öztürk H, Özgür A, Ozkirimli E. DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics. 2018;34(17):i821–i829. doi: 10.1093/bioinformatics/bty593.
    1. Feng Q, Dueva E, Cherkasov A, Ester M (2018) PADME: A deep learning-based framework for drug-target interaction prediction. arXiv
    1. Beck BR, Shin B, Choi Y, et al. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J. 2020 doi: 10.1016/j.csbj.2020.03.025.
    1. Lee H, Kim W. Comparison of target features for predicting drug-target interactions by deep neural network based on large-scale drug-induced transcriptome data. Pharmaceutics. 2019 doi: 10.3390/pharmaceutics11080377.
    1. Karimi M, Wu D, Wang Z, Shen Y. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics. 2019 doi: 10.1093/bioinformatics/btz111.
    1. Born J, Manica M, Cadow J, et al (2020) PaccMannRL on SARS-CoV-2: Designing antiviral candidates with conditional generative models. arXiv
    1. Jiang M, Li Z, Bian Y, Wei Z. A novel protein descriptor for the prediction of drug binding sites. BMC Bioinformatics. 2019 doi: 10.1186/s12859-019-3058-0.
    1. Cañada A, Capella-Gutierrez S, Rabal O, et al. LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes. Nucleic Acids Res. 2017 doi: 10.1093/nar/gkx462.
    1. Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015 doi: 10.1021/acs.jmedchem.5b00104.
    1. Cheng F, Li W, Zhou Y, et al. AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model. 2012 doi: 10.1021/ci300367a.
    1. Patlewicz G, Jeliazkova N, Safford RJ, et al. An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res. 2008 doi: 10.1080/10629360802083871.
    1. Uygun MT, Amudi K, Turaçlı İD, Menges N. A new synthetic approach for pyrazolo[1,5-a]pyrazine-4(5H)-one derivatives and their antiproliferative effects on lung adenocarcinoma cell line. Mol Divers. 2021 doi: 10.1007/s11030-020-10161-8.
    1. Srivastava A, Siddiqui S, Ahmad R, et al. Exploring nature’s bounty: identification of Withania somnifera as a promising source of therapeutic agents against COVID-19 by virtual screening and in silico evaluation. J Biomol Struct Dyn. 2020 doi: 10.1080/07391102.2020.1835725.
    1. Attene-Ramos MS, Miller N, Huang R, et al. The Tox21 robotic platform for the assessment of environmental chemicals-From vision to reality. Drug Discov. Today. 2013;18(15–16):716–23. doi: 10.1016/j.drudis.2013.05.015.
    1. Wang Z, Liang L, Yin Z, Lin J. Improving chemical similarity ensemble approach in target prediction. J Cheminform. 2016 doi: 10.1186/s13321-016-0130-x.
    1. Pu L, Naderi M, Liu T, et al. eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates. BMC Pharmacol Toxicol. 2019 doi: 10.1186/s40360-018-0282-6.
    1. Lysenko A, Sharma A, Boroevich KA, Tsunoda T. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance. 2018 doi: 10.26508/lsa.201800098.
    1. Zhou B, Sun Q, Kong DX. Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach. Oncotarget. 2016 doi: 10.18632/oncotarget.8716.
    1. Huang R, Xia M, Sakamuru S, et al. Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization. Nat Commun. 2016 doi: 10.1038/ncomms10425.
    1. Gupta VK, Rana PS. Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model. J Bioinform Comput Biol. 2019 doi: 10.1142/S0219720019500331.
    1. Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: toxicity prediction using deep learning. Front Environ Sci. 2016 doi: 10.3389/fenvs.2015.00080.
    1. Gayvert KM, Madhukar NS, Elemento O. A data-driven approach to predicting successes and failures of clinical trials. Cell Chem Biol. 2016 doi: 10.1016/j.chembiol.2016.07.023.
    1. Gilvary C, Elkhader J, Madhukar N, et al. A machine learning and network framework to discover new indications for small molecules. PLoS Comput Biol. 2020 doi: 10.1371/JOURNAL.PCBI.1008098.
    1. Robledo-Cadena DX, Gallardo-Pérez JC, Dávila-Borja V, et al. Non-steroidal anti-inflammatory drugs increase cisplatin, paclitaxel, and doxorubicin efficacy against human cervix cancer cells. Pharmaceuticals (Basel) 2020 doi: 10.3390/ph13120463.
    1. Simm J, Klambauer G, Arany A, et al. Repurposing high-throughput image assays enables biological activity prediction for drug discovery. Cell Chem Biol. 2018 doi: 10.1016/j.chembiol.2018.01.015.
    1. Goh GB, Siegel C, Hodas N, Vishnu A (2017) SMILES2vec: An interpretable general-purpose deep neural network for predicting chemical properties. arXiv
    1. Preuer K, Lewis RPI, Hochreiter S, et al. Deepsynergy: predicting anti-cancer drug synergy with deep learning. Bioinformatics. 2018 doi: 10.1093/bioinformatics/btx806.
    1. Xu Y, Pei J, Lai L. Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction. J Chem Inf Model. 2017 doi: 10.1021/acs.jcim.7b00244.
    1. Rodrigues T, Werner M, Roth J, et al. Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor. Chem Sci. 2018 doi: 10.1039/c8sc02634c.
    1. Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci. 2018 doi: 10.1093/toxsci/kfy152.
    1. Zhang C, Cheng F, Li W, et al. In silico prediction of drug induced liver toxicity using substructure pattern recognition method. Mol Inform. 2016 doi: 10.1002/minf.201500055.
    1. Lei T, Li Y, Song Y, et al. ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. J Cheminform. 2016 doi: 10.1186/s13321-016-0117-7.
    1. Lei T, Chen F, Liu H, et al. ADMET evaluation in drug discovery. Part 17: development of quantitative and qualitative prediction models for chemical-induced respiratory toxicity. Mol Pharm. 2017 doi: 10.1021/acs.molpharmaceut.7b00317.
    1. Lei T, Sun H, Kang Y, et al. ADMET evaluation in drug discovery. 18 reliable prediction of chemical-induced urinary tract toxicity by boosting machine learning approaches. Mol Pharm. 2017 doi: 10.1021/acs.molpharmaceut.7b00631.
    1. Pandya R, Pandya J. C5.0 algorithm to improved decision tree with feature selection and reduced error pruning. Int J Comput Appl. 2015 doi: 10.5120/20639-3318.
    1. Jenwitheesuk E, Horst JA, Rivas KL, et al. Novel paradigms for drug discovery: computational multitarget screening. Trends Pharmacol Sci. 2008 doi: 10.1016/j.tips.2007.11.007.
    1. Gu S, Lai L, hua, Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach. Acta Pharmacol Sin. 2020;41:432–438. doi: 10.1038/s41401-019-0306-9.
    1. Chen YT, Xie JY, Sun Q, Mo WJ. Novel drug candidates for treating esophageal carcinoma: a study on differentially expressed genes, using connectivity mapping and molecular docking. Int J Oncol. 2019;54:152–166. doi: 10.3892/ijo.2018.4618.
    1. Taha KF, Khalil M, Abubakr MS, Shawky E. Identifying cancerrelated molecular targets of Nandina domestica Thunb. by network pharmacologybased analysis in combination with chemical profiling and molecular docking studies. J Ethnopharmacol. 2020;249:112413. doi: 10.1016/j.jep.2019.112413.
    1. Anighoro A, Bajorath J, Rastelli G. Polypharmacology: Challenges and opportunities in drug discovery. J Med Chem. 2014;57(19):7874–87. doi: 10.1021/jm5006463.
    1. Zhang W, Pei J, Lai L. Computational multitarget drug design. J Chem Inf Model. 2017;57(3):403–412. doi: 10.1021/acs.jcim.6b00491.
    1. Proschak E, Stark H, Merk D. Polypharmacology by design: a medicinal chemist’s perspective on multitargeting compounds. J Med Chem. 2019;62(2):420–444. doi: 10.1021/acs.jmedchem.8b00760.
    1. Awale M, Reymond JL. 2017. The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data. J Cheminform.
    1. Reker D, Rodrigues T, Schneider P, Schneider G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc Natl Acad Sci U S A. 2014 doi: 10.1073/pnas.1320001111.
    1. Wang L, Ma C, Wipf P, et al. Targethunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J. 2013 doi: 10.1208/s12248-012-9449-z.
    1. Xia W, Chenxu P, Honglin L (2016) PharmMapper. In: Enhancing Enrich. Pharmacophore-Based Target Predict. Polypharmacological Profiles Drugs 56(6):1175–83. 10.1021/acs.jcim.5b00690
    1. Gong J, Cai C, Liu X, et al. ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics. 2013 doi: 10.1093/bioinformatics/btt270.
    1. Gfeller D, Grosdidier A, Wirth M, et al. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014 doi: 10.1093/nar/gku293.
    1. Poirier M, Awale M, Roelli MA, et al. Identifying lysophosphatidic acid acyltransferase β (LPAAT-β) as the target of a nanomolar angiogenesis inhibitor from a phenotypic screen using the polypharmacology browser PPB2. ChemMedChem. 2019 doi: 10.1002/cmdc.201800554.
    1. Ozhathil LC, Delalande C, Bianchi B, et al. Identification of potent and selective small molecule inhibitors of the cation channel TRPM4. Br J Pharmacol. 2018 doi: 10.1111/bph.14220.
    1. Ratnawati DE, Marjono M, Anam S (2018) Prediction of active compounds from SMILES codes using backpropagation algorithm. In: AIP Conference Proceedings
    1. Van Vleet TR, Liguori MJ, Lynch JJ, et al. Screening strategies and methods for better offtarget liability prediction and identification of small-molecule pharmaceuticals. SLAS Discov. 2019;24(1):1–24. doi: 10.1177/2472555218799713.
    1. Yue SJ, Liu J, Feng WW, et al. System pharmacology-based dissection of the synergistic mechanism of huangqi and huanglian for diabetes mellitus. Front Pharmacol. 2017 doi: 10.3389/fphar.2017.00694.
    1. Shi XQ, Yue SJ, Tang YP, et al. A network pharmacology approach to investigate the blood enriching mechanism of Danggui buxue Decoction. J Ethnopharmacol. 2019 doi: 10.1016/j.jep.2019.01.027.
    1. Liu X, Wu J, Zhang D, et al. A network pharmacology approach to uncover the multiple mechanisms of hedyotis diffusa willd on colorectal cancer. EvidenceBased Complem Altern Med. 2018 doi: 10.1155/2018/6517034.
    1. Wang J, Luo C, Shan C, et al. Inhibition of human copper trafficking by a small molecule significantly attenuates cancer cell proliferation. Nat Chem. 2015 doi: 10.1038/nchem.2381.
    1. Fang J, Li Y, Liu R, et al. Discovery of multitarget-directed ligands against Alzheimer’s disease through systematic prediction of chemical-protein interactions. J Chem Inf Model. 2015 doi: 10.1021/ci500574n.
    1. Gao L, Wang KX, Zhou YZ, et al. Uncovering the anticancer mechanism of compound Kushen Injection against HCC by integrating quantitative analysis, network analysis and experimental validation. Sci Rep. 2018 doi: 10.1038/s41598-017-18325-7.
    1. Zhou W, Liu X, Tu Z, et al. Discovery of pteridin-7(8H)-one-based irreversible inhibitors targeting the epidermal growth factor receptor (EGFR) kinase T790M/L858R Mutant. J Med Chem. 2013;56:7821–7837. doi: 10.1021/jm401045n.
    1. Wang Q, Feng YH, Huang JC, et al. A novel framework for the identification of drug target proteins: combining stacked auto-encoders with a biased support vector machine. PLoS ONE. 2017 doi: 10.1371/journal.pone.0176486.
    1. Carvalho-Silva D, Pierleoni A, Pignatelli M, et al. Open targets platform: new developments and updates two years on. Nucleic Acids Res. 2019 doi: 10.1093/nar/gky1133.
    1. López-Cortés A, Paz-y-Miño C, Guerrero S, et al. Pharmacogenomics, biomarker network, and allele frequencies in colorectal cancer. Pharmacogenomics J. 2020;20(1):136–158. doi: 10.1038/s41397-019-0102-4.
    1. Nabirotchkin S, Peluffo AE, Bouaziz J, Cohen D (2020) Focusing on the unfolded protein response and autophagy related pathways to reposition common approved drugs against COVID-19. Preprints
    1. López-Isac E, Acosta-Herrera M, Kerick M, et al. GWAS for systemic sclerosis identifies multiple risk loci and highlights fibrotic and vasculopathy pathways. Nat Commun. 2019 doi: 10.1038/s41467-019-12760-y.
    1. Martin P, Ding J, Duffus K, et al. Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases. Ann Rheum Dis. 2019 doi: 10.1136/annrheumdis-2018-214649.
    1. Dong J, Cao DS, Miao HY, et al. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminform. 2015 doi: 10.1186/s13321-015-0109-z.
    1. Angelo RM, Io AK, Almeida MP, et al (2020) OntoQSAR: An ontology for interpreting chemical and biological data in quantitative structure-activity relationship studies. In: Proceedings-14th IEEE International Conference on Semantic Computing, ICSC 2020
    1. Oldenhof M, Arany A, Moreau Y, Simm J. Chemgrapher: optical graph recognition of chemical compounds by deep learning. J Chem Inf Model. 2020 doi: 10.1021/acs.jcim.0c00459.
    1. Dong J, Yao ZJ, Zhu MF, et al. ChemSAR: An online pipelining platform for molecular SAR modeling. J Cheminform. 2017 doi: 10.1186/s13321-017-0215-1.
    1. Buyukbingol E, Sisman A, Akyildiz M, et al. Adaptive neuro-fuzzy inference system (ANFIS): a new approach to predictive modeling in QSAR applications: a study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists. Bioorg Med Chem. 2007;15:4265–4282. doi: 10.1016/j.bmc.2007.03.065.
    1. Jiang HJ, Huang YA, You ZH. Predicting drug-disease associations via using Gaussian interaction profile and kernel-based autoencoder. Biomed Res Int. 2019 doi: 10.1155/2019/2426958.
    1. Wang YY, Cui C, Qi L, et al. DrPOCS: drug repositioning based on projection onto convex sets. IEEE/ACM Trans Comput Biol Bioinforma. 2019 doi: 10.1109/TCBB.2018.2830384.
    1. Xuan P, Cui H, Shen T, et al. HeteroDualNet: a dual convolutional neural network with heterogeneous layers for drug-disease association prediction via chou’s five-step rule. Front Pharmacol. 2019 doi: 10.3389/fphar.2019.01301.
    1. Sadeghi SS, Keyvanpour M (2019) RCDR: A Recommender Based Method for Computational Drug Repurposing. In: 2019 IEEE 5th Conference on Knowledge Based Engineering and Innovation, KBEI 2019
    1. Zhu Q, Luo J, Ding P, Xiao Q (2018) GRTR: Drug-disease association prediction based on graph regularized transductive regression on heterogeneous network. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    1. Jiang HJ, Huang YA, You ZH. SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network. Sci Rep. 2020 doi: 10.1038/s41598-020-61616-9.
    1. Wang MN, You ZH, Li LP, et al (2020) WGMFDDA: A Novel Weighted-Based Graph Regularized Matrix Factorization for Predicting Drug-Disease Associations. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
    1. Liu H, Zhang W, Song Y, et al. HNet-DNN: inferring new drug-disease associations with deep neural network based on heterogeneous network features. J Chem Inf Model. 2020 doi: 10.1021/acs.jcim.9b01008.
    1. Lee I, Keum J, Nam H. DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol. 2019 doi: 10.1371/journal.pcbi.1007129.
    1. Abdel-Basset M, Hawash H, Elhoseny M, et al. DeepH-DTA: deep learning for predicting drug-target interactions: a case study of COVID-19 drug repurposing. IEEE Access. 2020 doi: 10.1109/access.2020.3024238.
    1. Yang J, He S, Zhang Z, Bo X. NegStacking: drug-target interaction prediction based on ensemble learning and logistic regression. IEEE/ACM Trans Comput Biol Bioinforma. 2020 doi: 10.1109/TCBB.2020.2968025.
    1. King MD, Long T, Pfalmer DL, et al. SPIDR: small-molecule peptide-influenced drug repurposing. BMC Bioinformatics. 2018 doi: 10.1186/s12859-018-2153-y.
    1. Huang K, Fu T, Glass LM, et al. DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa1005.
    1. Chu Y, Kaushik AC, Wang X, et al. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Brief Bioinform. 2019 doi: 10.1093/bib/bbz152.
    1. Shar PA, Tao W, Gao S, et al. Pred-binding: large-scale protein–ligand binding affinity prediction. J Enzyme Inhib Med Chem. 2016;31:1443–1450. doi: 10.3109/14756366.2016.1144594.
    1. Capuzzi SJ, Kim ISJ, Lam WI, et al. Chembench: a publicly accessible, integrated cheminformatics portal. J Chem Inf Model. 2017 doi: 10.1021/acs.jcim.6b00462.
    1. Pires DEV, Blundell TL, Ascher DB. MCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistance. Sci Rep. 2016 doi: 10.1038/srep29575.
    1. Pires DEV, Ascher DB. CSM-lig: a web server for assessing and comparing protein-small molecule affinities. Nucleic Acids Res. 2016 doi: 10.1093/nar/gkw390.
    1. Pires DEV, Ascher DB. mCSM-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res. 2016 doi: 10.1093/nar/gkw458.
    1. Kaminskas LM, Pires DEV, Ascher DB. dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction. Sci Rep. 2019 doi: 10.1038/s41598-019-51789-3.
    1. Patel RD, Prasanth Kumar S, Pandya HA, Solanki HA. MDCKpred: a web-tool to calculate MDCK permeability coefficient of small molecule using membrane-interaction chemical features. Toxicol Mech Methods. 2018 doi: 10.1080/15376516.2018.1499840.
    1. Montanari F, Knasmüller B, Kohlbacher S, et al. Vienna LiverTox workspace—a set of machine learning models for prediction of interactions profiles of small molecules with transporters relevant for regulatory agencies. Front Chem. 2020 doi: 10.3389/fchem.2019.00899.
    1. Kochev N, Avramova S, Jeliazkova N. Ambit-SMIRKS: a software module for reaction representation, reaction search and structure transformation. J Cheminform. 2018 doi: 10.1186/s13321-018-0295-6.
    1. Hornig M, Klamt A. COSMOfrag: a novel tool for high-throughput ADME property prediction and similarity screening based on quantum chemistry. J Chem Inf Model. 2005 doi: 10.1021/ci0501948.
    1. Hassan-Harrirou H, Zhang C, Lemmin T. RosENet: improving binding affinity prediction by leveraging molecular mechanics energies with an ensemble of 3D convolutional neural networks. J Chem Inf Model. 2020 doi: 10.1021/acs.jcim.0c00075.
    1. Rifaioglu AS, Cetin Atalay R, Cansen Kahraman D, et al. MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa858.
    1. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018 doi: 10.1093/nar/gky318.
    1. Dong J, Wang NN, Yao ZJ, et al. Admetlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminform. 2018 doi: 10.1186/s13321-018-0283-x.
    1. Maunz A, Gütlein M, Rautenberg M, et al. Lazar: a modular predictive toxicology framework. Front Pharmacol. 2013 doi: 10.3389/fphar.2013.00038.
    1. Yao ZJ, Dong J, Che YJ, et al. TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models. J Comput Aided Mol Des. 2016 doi: 10.1007/s10822-016-9915-2.
    1. Meng C, Hu Y, Zhang Y, Guo F. PSBP-SVM: a machine learning-based computational identifier for predicting polystyrene binding peptides. Front Bioeng Biotechnol. 2020 doi: 10.3389/fbioe.2020.00245.
    1. Shen C, Luo J, Ouyang W, et al. IDDkin: network-based influence deep diffusion model for enhancing prediction of kinase inhibitors. Bioinformatics. 2020 doi: 10.1093/bioinformatics/btaa1058.
    1. Jewison T, Su Y, Disfany FM, et al. SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res. 2014 doi: 10.1093/nar/gkt1067.
    1. Dalabira E, Viennas E, Daki E, et al. DruGeVar: an online resource triangulating drugs with genes and genomic biomarkers for clinical pharmacogenomics. Public Health Genom. 2014 doi: 10.1159/000365895.
    1. Verma J, Luo H, Hu J, Zhang P (2017) DrugPathSeeker: Interactive UI for exploring drug-ADR relation via pathways. In: IEEE Pacific Visualization Symposium
    1. Jarada T, Rokne J, Alhajj R. SNF-NN: Computational Method To Predict Drug-Disease Interactions Using Similarity Network Fusion and Neural Networks. Res Sq. 2021 doi: 10.21203/-56433/v1.
    1. Cao X, Fan R, Zeng W. DeepDrug: a general graph-based deep learning framework for drug relation prediction. biorxiv. 2020 doi: 10.1101/2020.11.09.375626.
    1. Hartenfeller M, Schneider G. Enabling future drug discovery by de novo design. Wiley Interdiscip Rev Comput Mol Sci. 2011;1:742–759. doi: 10.1002/wcms.49.
    1. Schneider P, Schneider G. De Novo design at the edge of chaos. J Med Chem. 2016;59:4077–4086. doi: 10.1021/acs.jmedchem.5b01849.
    1. Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today. 2015;20(3):318–331. doi: 10.1016/j.drudis.2014.10.012.
    1. Vyas V, Jain A, Jain A, Gupta A. Virtual screening: a fast tool for drug design. Sci Pharm. 2008;76(3):333–360. doi: 10.3797/scipharm.0803-03.
    1. Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform. 2009;1:1–11. doi: 10.1186/1758-2946-1-8.
    1. Blaschke T, Olivecrona M, Engkvist O, et al. Application of generative autoencoder in De novo molecular design. Mol Inform. 2018;37:1–11. doi: 10.1002/minf.201700123.
    1. Jaakkola TS, Haussler D (1999) Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems
    1. Kadurin A, Aliper A, Kazennov A, et al. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017;8:10883–10890. doi: 10.18632/oncotarget.14073.
    1. Müller AT, Hiss JA, Schneider G. Recurrent neural network model for constructive peptide design. J Chem Inf Model. 2018;58:472–479. doi: 10.1021/acs.jcim.7b00414.
    1. Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform. 2017;9:1–14. doi: 10.1186/s13321-017-0235-x.
    1. Merk D, Friedrich L, Grisoni F, Schneider G. De novo design of bioactive small molecules by artificial intelligence. Mol Inform. 2018;37:3–6. doi: 10.1002/minf.201700153.
    1. Sarkar D (2018) A comprehensive hands-on guide to transfer learning with real-world applications in deep learning. Medium
    1. Li X, Fourches D. Inductive transfer learning for molecular activity prediction: next-gen QSAR models with MolPMoFiT. J Cheminform. 2020 doi: 10.1186/s13321-020-00430-x.
    1. Engkvist O, Norrby PO, Selmi N, et al. Computational prediction of chemical reactions: current status and outlook. Drug Discov Today. 2018;23:1203–1218. doi: 10.1016/j.drudis.2018.02.014.
    1. Hessler G, Baringhaus KH. Artificial intelligence in drug design. Molecules. 2018 doi: 10.3390/molecules23102520.
    1. Domenico A, Nicola G, Daniela T, et al. De novo drug design of targeted chemical libraries based on artificial intelligence and pair-based multiobjective optimization. J Chem Inf Model. 2020;60:4582–4593. doi: 10.1021/acs.jcim.0c00517.
    1. Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater. 2019;18:435–441. doi: 10.1038/s41563-019-0338-z.
    1. Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2018;18(1):41–58. doi: 10.1038/nrd.2018.168.
    1. Kubick N, Pajares M, Enache I, et al. Repurposing Zileuton as a depression drug using an AI and in vitro approach. Molecules. 2020 doi: 10.3390/molecules25092155.
    1. Yuan Y, Pei J, Lai L. LigBuilder V3: a multi-target de novo drug design approach. Front Chem. 2020;8:1–18. doi: 10.3389/fchem.2020.00142.
    1. Wei L, Wen W, Rao L, et al. Cov_FB3D: a de novo covalent drug design protocol integrating the Ba-SAMP strategy and machine-learning-based synthetic tractability evaluation. J Chem Inf Model. 2020;60:4388–4402. doi: 10.1021/acs.jcim.9b01197.
    1. Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nat Mach Intell. 2020;2:573–584. doi: 10.1038/s42256-020-00236-4.
    1. Cavasotto CN, Di Filippo JI. Artificial intelligence in the early stages of drug discovery. Arch Biochem Biophys. 2021;698:108730. doi: 10.1016/j.abb.2020.108730.
    1. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20:273–286. doi: 10.1093/biostatistics/kxx069.
    1. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ. 2016;47:20–33. doi: 10.1016/j.jhealeco.2016.01.012.
    1. Ruddigkeit L, Van Deursen R, Blum LC, Reymond JL. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model. 2012;52:2864–2875. doi: 10.1021/ci300415d.
    1. Mohs RC, Greig NH. Drug discovery and development: role of basic biological research. Alzheimer’s Dement Transl Res Clin Interv. 2017;3(4):651–657. doi: 10.1016/2Fj.trci.2017.10.005.
    1. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–477. doi: 10.1038/s41573-019-0024-5.
    1. Niel O, Bastard P. Artificial intelligence in nephrology: core concepts, clinical applications, and perspectives. Am J Kidney Dis. 2019 doi: 10.1053/j.ajkd.2019.05.020.
    1. Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019 doi: 10.7717/peerj.7702.
    1. Rubin EH, Gilliland DG. Drug development and clinical trials-the path to an approved cancer drug. Nat Rev Clin Oncol. 2012;9:215–222. doi: 10.1038/nrclinonc.2012.22.
    1. Rautio J, Kumpulainen H, Heimbach T, et al. Prodrugs: design and clinical applications. Nat Rev Drug Discov. 2008;7(3):255–270. doi: 10.1038/nrd2468.
    1. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40:577–591. doi: 10.1016/j.tips.2019.05.005.
    1. Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp Clin Trials Commun. 2018;11:156–164. doi: 10.1016/2Fj.conctc.2018.08.001.
    1. Toh TS, Dondelinger F, Wang D. Looking beyond the hype: applied AI and machine learning in translational medicine. EBioMedicine. 2019;47:607–615. doi: 10.1016/j.ebiom.2019.08.027.
    1. Qi Y. Predicting phase 3 clinical trial results by modeling phase 2 clinical trial subject level data using deep learning. Proc Mach Learn Res. 2019;106:1–14.
    1. Viceconti M, Henney A, Morley-Fletcher E. In silico clinical trials: how computer simulation will transform the biomedical industry. Int J Clin Trials. 2016 doi: 10.18203/2349-3259.ijct20161408.
    1. Magalingam KB, Radhakrishnan A, Ping NS, Haleagrahara N. Current concepts of neurodegenerative mechanisms in Alzheimer’s disease. Biomed Res Int. 2018 doi: 10.1155/2018/3740461.
    1. Hussain R, Zubair H, Pursell S, Shahab M. Neurodegenerative diseases: regenerative mechanisms and novel therapeutic approaches. Brain Sci. 2018 doi: 10.3390/brainsci8090177.
    1. Levenson RW, Sturm VE, Haase CM. Emotional and behavioral symptoms in neurodegenerative disease: a model for studying the neural bases of psychopathology. Annu Rev Clin Psychol. 2014;10:581–606. doi: 10.1146/annurev-clinpsy-032813-153653.
    1. Gitler AD, Dhillon P, Shorter J. Neurodegenerative disease: Models, mechanisms, and a new hope. DMM Dis Model Mech. 2017;10:499–502. doi: 10.1242/dmm.030205.
    1. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today. 2019;24(3):773–780. doi: 10.1016/j.drudis.2018.11.014.
    1. Peng J, Guan J, Shang X. Predicting Parkinson’s disease genes based on node2vec and autoencoder. Front Genet. 2019;10:1–6. doi: 10.3389/fgene.2019.00226.
    1. Thomas SN, Funk KE, Wan Y, et al. Dual modification of Alzheimer’s disease PHF-tau protein by lysine methylation and ubiquitylation: a mass spectrometry approach. Acta Neuropathol. 2012 doi: 10.1007/s00401-011-0893-0.
    1. Yousefian-Jazi A, Sung MK, Lee T, et al. Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network. Sci Rep. 2020;10:1–12. doi: 10.1038/s41598-020-69790-6.
    1. Gupta R, Ambasta RK, Kumar P. Identification of novel class I and class IIb histone deacetylase inhibitor for Alzheimer’s disease therapeutics. Life Sci. 2020 doi: 10.1016/j.lfs.2020.117912.
    1. Jamal S, Grover A, Grover S. Machine learning from molecular dynamics trajectories to predict caspase-8 Inhibitors against Alzheimer’s disease. Front Pharmacol. 2019;10:1–13. doi: 10.3389/fphar.2019.00780.
    1. Chen HY, Chen JQ, Li JY, et al. Deep learning and random forest approach for finding the optimal traditional Chinese medicine formula for treatment of Alzheimer’s Disease. J Chem Inf Model. 2019;59:1605–1623. doi: 10.1021/acs.jcim.9b00041.
    1. Ponzoni I, Sebastián-Pérez V, Martínez MJ, et al. QSAR Classification models for predicting the activity of inhibitors of beta-secretase (BACE1) associated with Alzheimer’s disease. Sci Rep. 2019;9:1–13. doi: 10.1038/s41598-019-45522-3.
    1. Kaiser TM, Dentmon ZW, Dalloul CE, et al. Accelerated discovery of novel Ponatinib Analogs with improved properties for the treatment of Parkinson’s disease. ACS Med Chem Lett. 2020;11:491–496. doi: 10.1021/acsmedchemlett.9b00612.
    1. Shao YM, Ma X, Paira P, et al. Discovery of indolylpiperazinylpyrimidines with dual-target profiles at adenosine A2A and dopamine D2 receptors for Parkinson’s disease treatment. PLoS ONE. 2018;13:1–27. doi: 10.1371/journal.pone.0188212.
    1. Chen ZD, Zhao L, Chen HY, et al. A novel artificial intelligence protocol to investigate potential leads for Parkinson’s disease. RSC Adv. 2020;10:22939–22958. doi: 10.1039/d0ra04028b.
    1. Deng L, Zhong W, Zhao L, et al. Artificial intelligence-based application to explore inhibitors of neurodegenerative diseases. Front Neurorobot. 2020 doi: 10.3389/fnbot.2020.617327.
    1. Oh M, Ahn J, Yoon Y. A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions. PLoS ONE. 2014;9:1–12. doi: 10.1371/journal.pone.0111668.
    1. Zhu Y, Jung W, Wang F, Che C. Drug repurposing against Parkinson’s disease by text mining the scientific literature. Libr Hi Tech. 2020;38:741–750. doi: 10.1108/LHT-08-2019-0170.
    1. Vatansever S, Schlessinger A, Wacker D, et al (2020) Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: state-of-the-arts and future directions. Med Res Rev Online ahead of print
    1. Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell. 2020 doi: 10.1016/j.cell.2020.01.021.
    1. Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of deep learning in biomedicine. Mol Pharm. 2016;13(5):1445–1454. doi: 10.1021/acs.molpharmaceut.5b00982.
    1. Preuer K, Klambauer G, Rippmann F, et al (2019) Interpretable deep learning in drug discovery. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    1. Ramsundar B, Liu B, Wu Z, et al. Is multitask deep learning practical for pharma? J Chem Inf Model. 2017 doi: 10.1021/acs.jcim.7b00146.
    1. Grace K, Salvatier J, Dafoe A, et al (2017) When Will AI Exceed Human Performance? evidence from AI experts. J Artif Intell Res 62:1–48
    1. Altae-Tran H, Ramsundar B, Pappu AS, Pande V. Low data drug discovery with one-shot learning. ACS Cent Sci. 2017;3:283–293. doi: 10.1021/acscentsci.6b00367.

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever