Machine Learning Applications in Drug Discovery
DOI:
https://doi.org/10.17721/fujcV13I1P1-66Keywords:
drug discovery, drug development, machine learning, artificial intelligence in drug discovery, deep learning in drug developmentAbstract
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on its transformative impact across various stages of the drug development pipeline. We delve into key ML methodologies, including supervised and unsupervised learning, neural networks, and reinforcement learning, examining their underlying principles and specific contributions to drug discovery processes. By exploring case studies and recent advancements, this review illustrates how ML algorithms have been utilized to predict drug-target interactions, optimize drug design, and streamline clinical trial processes. Furthermore, we discuss the challenges and limitations of implementing ML techniques in this field and highlight emerging trends and future directions. This review aims to offer researchers a thorough understanding of ML's potential to revolutionize drug discovery and equip them with the insights needed to leverage these technologies effectively.
References
Ugurlu S. Computational Methods in Drug Discovery and Development. OAJ Materials and Devices 2024;8:1230-1-1230-43. https://doi.org/10.23647/ca.md20241230
Grewal P. A Critical Conceptual Analysis of Definitions of Artificial Intelligence as Applicable to Computer Engineering. IOSR Journal of Computer Engineering 2014;16(2):09-13. https://doi.org/10.9790/0661-16210913
Zhu H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annual Review of Pharmacology and Toxicology 2020;60(1):573-589. https://doi.org/10.1146/annurev-pharmtox-010919-023324
Patel L, Shukla T, Huang X, Ussery D, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020;25(22):5277. https://doi.org/10.3390/molecules25225277
Petrova E. Innovation in the Pharmaceutical Industry: The Process of Drug Discovery and Development. International Series in Quantitative Marketing 2013:19-81. https://doi.org/10.1007/978-1-4614-7801-0_2
Sinha S, Vohora D. Drug Discovery and Development. Pharmaceutical Medicine and Translational Clinical Research 2018:19-32. https://doi.org/10.1016/b978-0-12-802103-3.00002-x
Sarker I. Machine Learning: Algorithms, Real-World Applications and Research Directions. 2021. https://doi.org/10.20944/preprints202103.0216.v1
Helm J, Swiergosz A, Haeberle H, Karnuta J, Schaffer J, Krebs V, Spitzer A, Ramkumar P. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Current Reviews in Musculoskeletal Medicine 2020;13(1):69-76. https://doi.org/10.1007/s12178-020-09600-8
Nkemdilim M, Uzoamaka P, Daniel U, Chidi M. An Overview of Supervised Machine Learning Paradigms and their Classifiers. International Journal of Advanced Engineering, Management and Science 2024;10(3):24-32. https://doi.org/10.22161/ijaems.103.4
Dayan P, Sahani M, Deback G. Unsupervised learning. The MIT encyclopedia of the cognitive sciences 1999:857–859.
Dike H, Zhou Y, Deveerasetty K, Wu Q. Unsupervised Learning Based On Artificial Neural Network: A Review. 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) 2018:322-327. https://doi.org/10.1109/cbs.2018.8612259
Wang X, Zhao Y, Pourpanah F. Recent advances in deep learning. International Journal of Machine Learning and Cybernetics 2020;11(4):747-750. https://doi.org/10.1007/s13042-020-01096-5
Schmidt RM. Recurrent neural networks (rnns): A gentle introduction and overview. arXiv preprint 2019:1912.05911
Bisong E. Recurrent Neural Networks (RNNs). Building Machine Learning and Deep Learning Models on Google Cloud Platform 2019:443-473. https://doi.org/10.1007/978-1-4842-4470-8_36
Mou L, Ghamisi P, Zhu X. Deep Recurrent Neural Networks for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2017;55(7):3639-3655. https://doi.org/10.1109/tgrs.2016.2636241
M.Tarwani K, Edem S. Survey on Recurrent Neural Network in Natural Language Processing. International Journal of Engineering Trends and Technology 2017;48(6):301-304. https://doi.org/10.14445/22315381/ijett-v48p253
Walters W, Barzilay R. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction. Accounts of Chemical Research 2020;54(2):263-270. https://doi.org/10.1021/acs.accounts.0c00699
Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electronic Markets 2021;31(3):685-695. https://doi.org/10.1007/s12525-021-00475-2
Ugurlu S, McDonald D, Lei H, Jones A, Li S, Tong H, Butler M, He S. Cobdock: an accurate and practical machine learning-based consensus blind docking method. Journal of Cheminformatics 2024;16(1):5. https://doi.org/10.1186/s13321-023-00793-x
Ugurlu S. CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches. Journal of Computer-Aided Molecular Design 2025;39(1):48. https://doi.org/10.1007/s10822-025-00629-w
Krivák R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. Journal of Cheminformatics 2018;10(1):1-12. https://doi.org/10.1186/s13321-018-0285-8
Le Guilloux V, Schmidtke P, Tuffery P. Fpocket: An open source platform for ligand pocket detection. BMC Bioinformatics 2009;10(1):1-11. https://doi.org/10.1186/1471-2105-10-168
Mao J, Akhtar J, Zhang X, Sun L, Guan S, Li X, Chen G, Liu J, Jeon H, Kim M, No K, Wang G. Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience 2021;24(9):103052. https://doi.org/10.1016/j.isci.2021.103052
Stokes J, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia N, MacNair C, French S, Carfrae L, Bloom-Ackermann Z, Tran V, Chiappino-Pepe A, Badran A, Andrews I, Chory E, Church G, Brown E, Jaakkola T, Barzilay R, Collins J. A Deep Learning Approach to Antibiotic Discovery. Cell 2020;180(4):688-702. https://doi.org/10.1016/j.cell.2020.01.021
Zhavoronkov A, Ivanenkov Y, Aliper A, Veselov M, Aladinskiy V, Aladinskaya A, Terentiev V, Polykovskiy D, Kuznetsov M, Asadulaev A, Volkov Y, Zholus A, Shayakhmetov R, Zhebrak A, Minaeva L, Zagribelnyy B, Lee L, Soll R, Madge D, Xing L, Guo T, Aspuru-Guzik A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology 2019;37(9):1038-1040. https://doi.org/10.1038/s41587-019-0224-x
Carpenter K, Huang X. Machine Learning-based Virtual Screening and Its Applications to Alzheimer’s Drug Discovery: A Review. Current Pharmaceutical Design 2018;24(28):3347-3358. https://doi.org/10.2174/1381612824666180607124038
Escalante H. Automated Machine Learning—A Brief Review at the End of the Early Years. Natural Computing Series 2021:11-28. https://doi.org/10.1007/978-3-030-72069-8_2
Priya S, Tripathi G, Singh D, Jain P, Kumar A. Machine learning approaches and their applications in drug discovery and design. Chemical Biology & Drug Design 2022;100(1):136-153. https://doi.org/10.1111/cbdd.14057
Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. Journal of Chemical Theory and Computation 2023;19(21):7478-7495. https://doi.org/10.1021/acs.jctc.3c00814
Vázquez J, López M, Gibert E, Herrero E, Luque F. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020;25(20):4723. https://doi.org/10.3390/molecules25204723
Shiammala P, Duraimutharasan N, Vaseeharan B, Alothaim A, Al-Malki E, Snekaa B, Safi S, Singh S, Velmurugan D, Selvaraj C. Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors. Methods 2023;219:82-94. https://doi.org/10.1016/j.ymeth.2023.09.010
Segall M, Barber C. Addressing toxicity risk when designing and selecting compounds in early drug discovery. Drug Discovery Today 2014;19(5):688-693. https://doi.org/10.1016/j.drudis.2014.01.006
Rao N, Powar R. Post-Marketing Drug Withdrawals: A Review. Pharmaceutical Chemistry Journal 2023;57(7):1138-1146. https://doi.org/10.1007/s11094-023-02994-y
Kumar L. Pharmacovigilance/reporting adverse drug reactions: An approach to enhance health surveillance and extending market share by minimizing the chances of drug withdrawals. Int J Pharm Pharm Sci 2015;7(9):1–7.
Krewski D, Andersen M, Tyshenko M, Krishnan K, Hartung T, Boekelheide K, Wambaugh J, Jones D, Whelan M, Thomas R, Yauk C, Barton-Maclaren T, Cote I. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Archives of Toxicology 2019;94(1):1-58. https://doi.org/10.1007/s00204-019-02613-4
Shukla S, Huang R, Austin C, Xia M. The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform. Drug Discovery Today 2010;15(23-24):997-1007. https://doi.org/10.1016/j.drudis.2010.07.007
Zhang L, Zhang H, Ai H, Hu H, Li S, Zhao J, Liu H. Applications of Machine Learning Methods in Drug Toxicity Prediction. Current Topics in Medicinal Chemistry 2018;18(12):987-997. https://doi.org/10.2174/1568026618666180727152557
Hwang T, Carpenter D, Lauffenburger J, Wang B, Franklin J, Kesselheim A. Failure of Investigational Drugs in Late-Stage Clinical Development and Publication of Trial Results. JAMA Internal Medicine 2016;176(12):1826. https://doi.org/10.1001/jamainternmed.2016.6008
Cavasotto C, Scardino V. Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point. ACS Omega 2022;7(51):47536-47546. https://doi.org/10.1021/acsomega.2c05693
Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it?. Acta Pharmaceutica Sinica B 2022;12(7):3049-3062. https://doi.org/10.1016/j.apsb.2022.02.002
Andersen M, Krewski D. Toxicity Testing in the 21st Century: Bringing the Vision to Life. Toxicological Sciences 2008;107(2):324-330. https://doi.org/10.1093/toxsci/kfn255
Agrawal V, Khadikar P. QSAR prediction of toxicity of nitrobenzenes. Bioorganic & Medicinal Chemistry 2001;9(11):3035-3040. https://doi.org/10.1016/s0968-0896(01)00211-5
Setiya A, Jani V, Sonavane U, Joshi R. MolToxPred: small molecule toxicity prediction using machine learning approach. RSC Advances 2024;14(6):4201-4220. https://doi.org/10.1039/d3ra07322j
Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: Toxicity Prediction using Deep Learning. Frontiers in Environmental Science 2016;3:80. https://doi.org/10.3389/fenvs.2015.00080
Wu Y, Wang G. Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis. International Journal of Molecular Sciences 2018;19(8):2358. https://doi.org/10.3390/ijms19082358
Sharma B, Chenthamarakshan V, Dhurandhar A, Pereira S, Hendler J, Dordick J, Das P. Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Scientific Reports 2023;13(1):4908. https://doi.org/10.1038/s41598-023-31169-8
Blundell T, Sibanda B, Montalvão R, Brewerton S, Chelliah V, Worth C, Harmer N, Davies O, Burke D. Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery. Philosophical Transactions of the Royal Society B: Biological Sciences 2006;361(1467):413-423. https://doi.org/10.1098/rstb.2005.1800
Schenone M, Dančík V, Wagner B, Clemons P. Target identification and mechanism of action in chemical biology and drug discovery. Nature Chemical Biology 2013;9(4):232-240. https://doi.org/10.1038/nchembio.1199
Misra B, Langefeld C, Olivier M, Cox L. Integrated omics: tools, advances and future approaches. Journal of Molecular Endocrinology 2019;62(1):R21-R45. https://doi.org/10.1530/jme-18-0055
Lopes R, Prasad M. Beyond the promise: evaluating and mitigating off-target effects in CRISPR gene editing for safer therapeutics. Frontiers in Bioengineering and Biotechnology 2024;11:1339189. https://doi.org/10.3389/fbioe.2023.1339189
Wolber G. Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discovery Today 2008;13(1-2):23-29. https://doi.org/10.1016/j.drudis.2007.09.007
Nettles J, Jenkins J, Bender A, Deng Z, Davies J, Glick M. Bridging Chemical and Biological Space: “Target Fishing” Using 2D and 3D Molecular Descriptors. Journal of Medicinal Chemistry 2006;49(23):6802-6810. https://doi.org/10.1021/jm060902w
Lo Y, Senese S, Damoiseaux R, Torres J. 3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping. ACS Chemical Biology 2016;11(8):2244-2253. https://doi.org/10.1021/acschembio.6b00253
Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery. Expert Opinion on Drug Discovery 2016;11(7):707-715. https://doi.org/10.1080/17460441.2016.1190706
Bagherian M, Sabeti E, Wang K, Sartor M, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Briefings in Bioinformatics 2020;22(1):247-269. https://doi.org/10.1093/bib/bbz157
Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S. Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery 2019;18(6):463-477. https://doi.org/10.1038/s41573-019-0024-5
Kim J, Park S, Min D, Kim W. Comprehensive Survey of Recent Drug Discovery Using Deep Learning. International Journal of Molecular Sciences 2021;22(18):9983. https://doi.org/10.3390/ijms22189983
Surade S, Blundell T. Structural Biology and Drug Discovery of Difficult Targets: The Limits of Ligandability. Chemistry & Biology 2012;19(1):42-50. https://doi.org/10.1016/j.chembiol.2011.12.013
Shoshan M, Linder S. Target specificity and off-target effects as determinants of cancer drug efficacy. Expert Opinion on Drug Metabolism & Toxicology 2008;4(3):273-280. https://doi.org/10.1517/17425255.4.3.273
Rudmann D. On-target and Off-target-based Toxicologic Effects. Toxicologic Pathology 2012;41(2):310-314. https://doi.org/10.1177/0192623312464311
Bender A, Scheiber J, Glick M, Davies J, Azzaoui K, Hamon J, Urban L, Whitebread S, Jenkins J. Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure. ChemMedChem 2007;2(6):861-873. https://doi.org/10.1002/cmdc.200700026
de Jong L, Uges D, Franke J, Bischoff R. Receptor–ligand binding assays: Technologies and Applications. Journal of Chromatography B 2005;829(1-2):1-25. https://doi.org/10.1016/j.jchromb.2005.10.002
Tame J. Scoring functions: A view from the bench. Journal of Computer-Aided Molecular Design 1999;13(2):99-108. https://doi.org/10.1023/a:1008068903544
Li J, Fu A, Zhang L. An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking. Interdisciplinary Sciences: Computational Life Sciences 2019;11(2):320-328. https://doi.org/10.1007/s12539-019-00327-w
Chen Y. Beware of docking!. Trends in Pharmacological Sciences 2015;36(2):78-95. https://doi.org/10.1016/j.tips.2014.12.001
Ballester P, Mitchell J. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics 2010;26(9):1169-1175. https://doi.org/10.1093/bioinformatics/btq112
Wang R, Fang X, Lu Y, Wang S. The PDBbind Database: Collection of Binding Affinities for Protein−Ligand Complexes with Known Three-Dimensional Structures. Journal of Medicinal Chemistry 2004;47(12):2977-2980. https://doi.org/10.1021/jm030580l
Li H, Leung K, Wong M, Ballester P. Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets. Molecular Informatics 2015;34(2-3):115-126. https://doi.org/10.1002/minf.201400132
Deng Z, Chuaqui C, Singh J. Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein−Ligand Binding Interactions. Journal of Medicinal Chemistry 2003;47(2):337-344. https://doi.org/10.1021/jm030331x
Colombo M, Peretto I. Chemistry strategies in early drug discovery: an overview of recent trends. Drug Discovery Today 2008;13(15-16):677-684. https://doi.org/10.1016/j.drudis.2008.03.007
Nicolaou K. Advancing the Drug Discovery and Development Process. Angewandte Chemie 2014;126(35):9280-9292. https://doi.org/10.1002/ange.201404761
Campos K, Coleman P, Alvarez J, Dreher S, Garbaccio R, Terrett N, Tillyer R, Truppo M, Parmee E. The importance of synthetic chemistry in the pharmaceutical industry. Science 2019;363(6424):eaat0805. https://doi.org/10.1126/science.aat0805
Schneider G. Automating drug discovery. Nature Reviews Drug Discovery 2017;17(2):97-113. https://doi.org/10.1038/nrd.2017.232
Zhao Y, Chen G, Liu J. Polymer data challenges in the ai era: Bridging gaps for next-generation energy materials. arXiv preprint 2025:2505.13494.
Saini V. Machine learning prediction of empirical polarity using SMILES encoding of organic solvents. Molecular Diversity 2022;27(5):2331-2343. https://doi.org/10.1007/s11030-022-10559-6
Zhou Z, Li X, Zare R. Optimizing Chemical Reactions with Deep Reinforcement Learning. ACS Central Science 2017;3(12):1337-1344. https://doi.org/10.1021/acscentsci.7b00492
Reker D, Hoyt E, Bernardes G, Rodrigues T. Adaptive Optimization of Chemical Reactions with Minimal Experimental Information. Cell Reports Physical Science 2020;1(11):100247. https://doi.org/10.1016/j.xcrp.2020.100247
Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP. Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems 2015:28.
Herna´ndez-Lobato JM, Requeima J, Pyzer-Knapp EO, Aspuru-Guzik A. Parallel and distributed thompson sampling for large-scale accelerated exploration of chemical space. In International conference on machine learning. PMLR 2017:1470–1479.
Gómez-Bombarelli R, Wei J, Duvenaud D, Hernández-Lobato J, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel T, Adams R, Aspuru-Guzik A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science 2018;4(2):268-276. https://doi.org/10.1021/acscentsci.7b00572
Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular graph convolutions: moving beyond fingerprints. Journal of Computer-Aided Molecular Design 2016;30(8):595-608. https://doi.org/10.1007/s10822-016-9938-8
Konc J. Binding site comparisons for target-centered drug discovery. Expert Opinion on Drug Discovery 2019;14(5):445-454. https://doi.org/10.1080/17460441.2019.1588883
Ugurlu S, McDonald D, He S. MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model. Journal of Cheminformatics 2024;16(1):116. https://doi.org/10.1186/s13321-024-00882-5
Kozlovskii I, Popov P. Computational methods for binding site prediction on macromolecules. Quarterly Reviews of Biophysics 2025;58:e12. https://doi.org/10.1017/s003358352500006x
Lexa K, Carlson H. Protein flexibility in docking and surface mapping. Quarterly Reviews of Biophysics 2012;45(3):301-343. https://doi.org/10.1017/s0033583512000066
Carlson H. Protein Flexibility is an Important Component of Structure-Based Drug Discovery. Current Pharmaceutical Design 2002;8(17):1571-1578. https://doi.org/10.2174/1381612023394232
Sotriffer C, Klebe G. Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design. Il Farmaco 2002;57(3):243-251. https://doi.org/10.1016/s0014-827x(02)01211-9
Etzion-Fuchs A, Todd D, Singh M. dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains. Nucleic Acids Research 2021;49(13):e78-e78. https://doi.org/10.1093/nar/gkab356
Santana C, Izidoro S, de Melo-Minardi R, Tyzack J, Ribeiro A, Pires D, Thornton J, de A. Silveira S. GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs. Nucleic Acids Research 2022;50(W1):W392-W397. https://doi.org/10.1093/nar/gkac323
Roche D, Tetchner S, McGuffin L. FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins. BMC Bioinformatics 2011;12(1):1-20. https://doi.org/10.1186/1471-2105-12-160
Roche D, Buenavista M, McGuffin L. The FunFOLD2 server for the prediction of protein–ligand interactions. Nucleic Acids Research 2013;41(W1):W303-W307. https://doi.org/10.1093/nar/gkt498
Yang J, Roy A, Zhang Y. Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 2013;29(20):2588-2595. https://doi.org/10.1093/bioinformatics/btt447
Wu Q, Peng Z, Zhang Y, Yang J. COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking. Nucleic Acids Research 2018;46(W1):W438-W442. https://doi.org/10.1093/nar/gky439
Parasrampuria D, Benet L, Sharma A. Why Drugs Fail in Late Stages of Development: Case Study Analyses from the Last Decade and Recommendations. The AAPS Journal 2018;20(3):46. https://doi.org/10.1208/s12248-018-0204-y
Sun A, Benet L. Late-Stage Failures of Monoclonal Antibody Drugs: A Retrospective Case Study Analysis. Pharmacology 2020;105(3-4):145-163. https://doi.org/10.1159/000505379
Tsaioun K, Bottlaender M, Mabondzo A. ADDME – Avoiding Drug Development Mistakes Early: central nervous system drug discovery perspective. BMC Neurology 2009;9(Suppl 1):S1. https://doi.org/10.1186/1471-2377-9-s1-s1
Wang M, Cao R, Zhang L, Yang X, Liu J, Xu M, Shi Z, Hu Z, Zhong W, Xiao G. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Research 2020;30(3):269-271. https://doi.org/10.1038/s41422-020-0282-0
Ashburn T, Thor K. Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery 2004;3(8):673-683. https://doi.org/10.1038/nrd1468
R N, Khan S, kumar A, T R M, Alojail M, Sangwan S, Saraee M. Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation. SLAS Technology 2025;31:100238. https://doi.org/10.1016/j.slast.2024.100238
Thakur A, Kulkarni S, Thakur G, Khan N. Transforming drug discovery: Leveraging deep learning and nlp for accelerated drug repurposing through text mining in biomedical literature. International Journal of Intelligent Systems and Applications in Engineering 2024;21:165–172.
Cai C, Guo P, Zhou Y, Zhou J, Wang Q, Zhang F, Fang J, Cheng F. Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity. Journal of Chemical Information and Modeling 2019;59(3):1073-1084. https://doi.org/10.1021/acs.jcim.8b00769
Madhukar N, Khade P, Huang L, Gayvert K, Galletti G, Stogniew M, Allen J, Giannakakou P, Elemento O. A Bayesian machine learning approach for drug target identification using diverse data types. Nature Communications 2019;10(1):5221. https://doi.org/10.1038/s41467-019-12928-6
Régnier S. What is the value of ‘me-too’ drugs?. Health Care Management Science 2013;16(4):300-313. https://doi.org/10.1007/s10729-013-9225-3
Aronson J, Green A. Me‐too pharmaceutical products: History, definitions, examples, and relevance to drug shortages and essential medicines lists. British Journal of Clinical Pharmacology 2020;86(11):2114-2122. https://doi.org/10.1111/bcp.14327
Napolitano F, Zhao Y, Moreira V, Tagliaferri R, Kere J, D’Amato M, Greco D. Drug repositioning: a machine-learning approach through data integration. Journal of Cheminformatics 2013;5(1):30. https://doi.org/10.1186/1758-2946-5-30
Koromina M, Pandi M, Patrinos G. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. OMICS: A Journal of Integrative Biology 2019;23(11):539-548. https://doi.org/10.1089/omi.2019.0151
Peteani G, Huynh M, Gerebtzoff G, Rodríguez-Pérez R. Application of machine learning models for property prediction to targeted protein degraders. Nature Communications 2024;15(1):5764. https://doi.org/10.1038/s41467-024-49979-3
Khuat TT, Bassett R, Otte E, Grevis-James A, Gabrys B. Applications of machine learning in biopharmaceutical process development and manufacturing: Current trends, challenges, and opportunities. arXiv preprint 2023:2310.09991.
Gupta N, Pandya P, Verma S. Computational Predictions for Multi-Target Drug Design. Methods in Pharmacology and Toxicology 2018:27-50. https://doi.org/10.1007/7653_2018_26
Feldmann C, Yonchev D, Bajorath J. Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning. Biomolecules 2020;10(12):1605. https://doi.org/10.3390/biom10121605
Kleandrova V, DS Cordeiro M, Speck-Planche A. Current In Silico Methods for Multi-Target Drug Discovery in Early Anticancer Research: The Rise of the Perturbation-Theory Machine Learning Approach. Future Medicinal Chemistry 2023;15(18):1647-1650. https://doi.org/10.4155/fmc-2023-0241
Morphy R, Kay C, Rankovic Z. From magic bullets to designed multiple ligands. Drug Discovery Today 2004;9(15):641-651. https://doi.org/10.1016/s1359-6446(04)03163-0
Rothman R, Baumann M, Savage J, Rauser L, McBride A, Hufeisen S, Roth B. Evidence for Possible Involvement of 5-HT 2B Receptors in the Cardiac Valvulopathy Associated With Fenfluramine and Other Serotonergic Medications. Circulation 2000;102(23):2836-2841. https://doi.org/10.1161/01.cir.102.23.2836
Löscher W. Single-Target Versus Multi-Target Drugs Versus Combinations of Drugs With Multiple Targets: Preclinical and Clinical Evidence for the Treatment or Prevention of Epilepsy. Frontiers in Pharmacology 2021;12:730257. https://doi.org/10.3389/fphar.2021.730257
Makhoba X, Viegas Jr. C, Mosa R, Viegas F, Pooe O. Potential Impact of the Multi-Target Drug Approach in the Treatment of Some Complex Diseases. Drug Design, Development and Therapy 2020;Volume 14:3235-3249. https://doi.org/10.2147/dddt.s257494
Morphy R, Rankovic Z. Multi-target Drugs. The Practice of Medicinal Chemistry 2008:549-571. https://doi.org/10.1016/b978-0-12-374194-3.00027-5
Lavecchia A, Cerchia C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discovery Today 2016;21(2):288-298. https://doi.org/10.1016/j.drudis.2015.12.007
Anighoro A, Bajorath J, Rastelli G. Polypharmacology: Challenges and Opportunities in Drug Discovery. Journal of Medicinal Chemistry 2014;57(19):7874-7887. https://doi.org/10.1021/jm5006463
Real M, Barnhill M, Higley C, Rosenberg J, Lewis J. Drug-Induced Liver Injury: Highlights of the Recent Literature. Drug Safety 2018;42(3):365-387. https://doi.org/10.1007/s40264-018-0743-2
Hopkins A. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology 2008;4(11):682-690. https://doi.org/10.1038/nchembio.118
Lim H, He D, Qiu Y, Krawczuk P, Sun X, Xie L. Rational discovery of dual-indication multi-target PDE/Kinase inhibitor for precision anti-cancer therapy using structural systems pharmacology. PLOS Computational Biology 2019;15(6):e1006619. https://doi.org/10.1371/journal.pcbi.1006619
Tan D, Thomas G, Garrett M, Banerji U, de Bono J, Kaye S, Workman P. Biomarker-Driven Early Clinical Trials in Oncology. The Cancer Journal 2009;15(5):406-420. https://doi.org/10.1097/ppo.0b013e3181bd0445
Frank R, Hargreaves R. Clinical biomarkers in drug discovery and development. Nature Reviews Drug Discovery 2003;2(7):566-580. https://doi.org/10.1038/nrd1130
Frangogiannis N. Biomarkers: hopes and challenges in the path from discovery to clinical practice. Translational Research 2012;159(4):197-204. https://doi.org/10.1016/j.trsl.2012.01.023
Aydin B, Arga K, Karadag A. Omics-Driven Biomarkers of Psoriasis: Recent Insights, Current Challenges, and Future Prospects. Clinical, Cosmetic and Investigational Dermatology 2020;Volume 13:611-625. https://doi.org/10.2147/ccid.s227896
Kori M, Gov E, Arga K, Sinha R. Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer. Biomarker Insights 2024;19:11772719241287400. https://doi.org/10.1177/11772719241287400
Nakayasu ES, Gritsenko M, Piehowski PD, Gao Y, Orton DJ, Schepmoes AA, Fillmore TL, Frohnert BI, Rewers M, Krischer JP. Tutorial: best practices and considerations for mass- spectrometry-based protein biomarker discovery and validation. Nature Protocols 2021;16(8):3737–3760.
Clark A, Lillard J. A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology. Genes 2024;15(8):1036. https://doi.org/10.3390/genes15081036
Nagana Gowda G, Raftery D. Biomarker Discovery and Translation in Metabolomics. Current Metabolomics 2013;1(3):227-240. https://doi.org/10.2174/2213235x113019990005
Li B, Shin H, Gulbekyan G, Pustovalova O, Nikolsky Y, Hope A, Bessarabova M, Schu M, Kolpakova-Hart E, Merberg D, Dorner A, Trepicchio W. Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib. PLOS ONE 2015;10(6):e0130700. https://doi.org/10.1371/journal.pone.0130700
Kraus V. Biomarkers as drug development tools: discovery, validation, qualification and use. Nature Reviews Rheumatology 2018;14(6):354-362. https://doi.org/10.1038/s41584-018-0005-9
Weatherall J, Khan F, Patel M, Dearden R, Shameer K, Dennis G, Feldberg G, White T, Khosla S. Clinical trials, real-world evidence, and digital medicine. The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry 2021:191-215. https://doi.org/10.1016/b978-0-12-820045-2.00011-8
Hartman E, Scott A, Karlsson C, Mohanty T, Vaara S, Linder A, Malmström L, Malmström J. Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis. Nature Communications 2023;14(1):5359. https://doi.org/10.1038/s41467-023-41146-4
DeGroat W, Mendhe D, Bhusari A, Abdelhalim H, Zeeshan S, Ahmed Z. IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics 2023;39(12):btad755. https://doi.org/10.1093/bioinformatics/btad755
Fuchs T, Buhmann J. Computational pathology: Challenges and promises for tissue analysis. Computerized Medical Imaging and Graphics 2011;35(7-8):515-530. https://doi.org/10.1016/j.compmedimag.2011.02.006
Lyzogub M. Overview of clinical validation processes for artificial intelli- gence applications in pathology. PhD thesis, Vilniaus universitetas 2024.
Lee G, Veltri R, Zhu G, Ali S, Epstein J, Madabhushi A. Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings. European Urology Focus 2017;3(4-5):457-466. https://doi.org/10.1016/j.euf.2016.05.009
Lu C, Lewis J, Dupont W, Plummer W, Janowczyk A, Madabhushi A. An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival. Modern Pathology 2017;30(12):1655-1665. https://doi.org/10.1038/modpathol.2017.98
Lu C, Romo-Bucheli D, Wang X, Janowczyk A, Ganesan S, Gilmore H, Rimm D, Madabhushi A. Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Laboratory Investigation 2018;98(11):1438-1448. https://doi.org/10.1038/s41374-018-0095-7
Schmidt T, Bergner A, Schwede T. Modelling three-dimensional protein structures for applications in drug design. Drug Discovery Today 2014;19(7):890-897. https://doi.org/10.1016/j.drudis.2013.10.027
Schauperl M, Denny R. AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges. Journal of Chemical Information and Modeling 2022;62(13):3142-3156. https://doi.org/10.1021/acs.jcim.2c00026
Kuhlman B, Bradley P. Advances in protein structure prediction and design. Nature Reviews Molecular Cell Biology 2019;20(11):681-697. https://doi.org/10.1038/s41580-019-0163-x
Axen S, Huang X, Cáceres E, Gendelev L, Roth B, Keiser M. A Simple Representation of Three-Dimensional Molecular Structure. Journal of Medicinal Chemistry 2017;60(17):7393-7409. https://doi.org/10.1021/acs.jmedchem.7b00696
David A, Islam S, Tankhilevich E, Sternberg M. The AlphaFold Database of Protein Structures: A Biologist’s Guide. Journal of Molecular Biology 2022;434(2):167336. https://doi.org/10.1016/j.jmb.2021.167336
Morcos F, Pagnani A, Lunt B, Bertolino A, Marks D, Sander C, Zecchina R, Onuchic J, Hwa T, Weigt M. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proceedings of the National Academy of Sciences 2011;108(49):E1293–E1301. https://doi.org/10.1073/pnas.1111471108
Marks D, Colwell L, Sheridan R, Hopf T, Pagnani A, Zecchina R, Sander C. Protein 3D Structure Computed from Evolutionary Sequence Variation. PLoS ONE 2011;6(12):e28766. https://doi.org/10.1371/journal.pone.0028766
Nourmohammad A, Pun M, Visani G. Machine-Learning Model Reveals Protein-Folding Physics. Physics 2022;15:183. https://doi.org/10.1103/physics.15.183
Meibohm B, Derendorf H. Basic concepts of pharmacokinetic/pharmacodynamic (pk/pd) modelling. International journal of clinical pharmacology and therapeutics 1997;35(10):401–413.
Zou H, Banerjee P, Leung S, Yan X. Application of Pharmacokinetic-Pharmacodynamic Modeling in Drug Delivery: Development and Challenges. Frontiers in Pharmacology 2020;11:997. https://doi.org/10.3389/fphar.2020.00997
Lin J, Lu A. Role of Pharmacokinetics and Metabolism in Drug Discovery and Development. Pharmacological Reviews 1997;49(4):403-449. https://doi.org/10.1016/s0031-6997(24)01340-1
Sharma P, Patel N, Prasad B, Varma M. Pharmacokinetics: Theory and Application in Drug Discovery and Development. Drug Discovery and Development 2021:297-355. https://doi.org/10.1007/978-981-15-5534-3_11
Sood R, A. A. Pharmacokinetic and Pharmacodynamic Modeling (PK/PD) in Pharmaceutical Research: Current Research and Advances. Software and Programming Tools in Pharmaceutical Research 2024:153-169. https://doi.org/10.2174/9789815223019124010009
Ghani S, Khan N, Sable H, Yao F, Shafiq M. Computational techniques for enhancing PK/PD modeling and simulation and ADMET prediction. Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology 2025:153-174. https://doi.org/10.1016/b978-0-443-33024-7.00001-1
Liu X, Liu C, Huang R, Zhu H, Liu Q, Mitra S, Wang Y. Long short-term memory recurrent neural network for pharmacokinetic-pharmacodynamic modeling. Int. Journal of Clinical Pharmacology and Therapeutics 2021;59(02):138-146. https://doi.org/10.5414/cp203800
Liu C, Xu Y, Liu Q, Zhu H, Wang Y. Application of machine learning based methods in exposure–response analysis. Journal of Pharmacokinetics and Pharmacodynamics 2022;49(4):401-410. https://doi.org/10.1007/s10928-022-09802-2
Tang A. Machine Learning for Pharmacokinetic/Pharmacodynamic Modeling. Journal of Pharmaceutical Sciences 2023;112(5):1460-1475. https://doi.org/10.1016/j.xphs.2023.01.010
Keutzer L, You H, Farnoud A, Nyberg J, Wicha S, Maher-Edwards G, Vlasakakis G, Moghaddam G, Svensson E, Menden M, Simonsson U. Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin. Pharmaceutics 2022;14(8):1530. https://doi.org/10.3390/pharmaceutics14081530
Shardlow C, Generaux G, MacLauchlin C, Pons N, Skordos K, Bloomer J. Utilizing Drug-Drug Interaction Prediction Tools during Drug Development: Enhanced Decision Making Based on Clinical Risk. Drug Metabolism and Disposition 2011;39(11):2076-2084. https://doi.org/10.1124/dmd.111.039214
Wang N, Zhu B, Li X, Liu S, Shi J, Cao D. Comprehensive Review of Drug–Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities. Journal of Chemical Information and Modeling 2023;64(1):96-109. https://doi.org/10.1021/acs.jcim.3c01304
Han K, Cao P, Wang Y, Xie F, Ma J, Yu M, Wang J, Xu Y, Zhang Y, Wan J. A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning. Frontiers in Pharmacology 2022;12:814858. https://doi.org/10.3389/fphar.2021.814858
Ogidi O, Alfred-Ugbenbo D, Poripo B. AI-Driven Drug Discovery, Design, and Development in Immunological Disorders. Advances in Computational Intelligence and Robotics 2025:143-180. https://doi.org/10.4018/979-8-3693-9725-1.ch005
Patil R. Research and development in the pharmaceutical industry. Research and Development 2024;1(1).
Marques L, Costa B, Pereira M, Silva A, Santos J, Saldanha L, Silva I, Magalhães P, Schmidt S, Vale N. Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare. Pharmaceutics 2024;16(3):332. https://doi.org/10.3390/pharmaceutics16030332
Yin Q, Fan R, Cao X, Liu Q, Jiang R, Zeng W. DeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction. Quantitative Biology 2023;11(3):260-274. https://doi.org/10.15302/j-qb-022-0320
Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R, Friedman C. Drug—drug interaction through molecular structure similarity analysis. Journal of the American Medical Informatics Association 2012;19(6):1066-1074. https://doi.org/10.1136/amiajnl-2012-000935
Huang J, Niu C, Green C, Yang L, Mei H, Han J. Systematic Prediction of Pharmacodynamic Drug-Drug Interactions through Protein-Protein-Interaction Network. PLoS Computational Biology 2013;9(3):e1002998. https://doi.org/10.1371/journal.pcbi.1002998
Cami A, Manzi S, Arnold A, Reis B. Pharmacointeraction Network Models Predict Unknown Drug-Drug Interactions. PLoS ONE 2013;8(4):e61468. https://doi.org/10.1371/journal.pone.0061468
Lawson C, Hodgson J, Wilson R, Richards S. Prevalence, thresholds and the performance of presence–absence models. Methods in Ecology and Evolution 2013;5(1):54-64. https://doi.org/10.1111/2041-210x.12123
Cheng F, Zhao Z. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. Journal of the American Medical Informatics Association 2014;21(e2):e278-e286. https://doi.org/10.1136/amiajnl-2013-002512
Liu S, Huang Z, Qiu Y, Chen Y, Zhang W. Structural Network Embedding using Multi-modal Deep Auto-encoders for Predicting Drug-drug Interactions. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019:445-450. https://doi.org/10.1109/bibm47256.2019.8983337
Jang H, Song J, Kim J, Lee H, Kim I, Moon B, Oh J. Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. npj Digital Medicine 2022;5(1):88. https://doi.org/10.1038/s41746-022-00639-0
Grimes D, Schulz K. An overview of clinical research: the lay of the land. The Lancet 2002;359(9300):57-61. https://doi.org/10.1016/s0140-6736(02)07283-5
Nair B. Clinical Trial Designs. Indian Dermatology Online Journal 2019;10(2):193. https://doi.org/10.4103/idoj.idoj_475_18
Li A, Bergan R. Clinical trial design: Past, present, and future in the context of big data and precision medicine. Cancer 2020;126(22):4838-4846. https://doi.org/10.1002/cncr.33205
Bieske L, Zinner M, Dahlhausen F, Truebel H. Critical path activities in clinical trial setup and conduct: How to avoid bottlenecks and accelerate clinical trials. Drug Discovery Today 2023;28(10):103733. https://doi.org/10.1016/j.drudis.2023.103733
Council for International Organizations of Medical Sciences et al. Clinical research in resource-limited settings. Council for International Organizations of Medical Sciences 2021.
Feijoo F, Palopoli M, Bernstein J, Siddiqui S, Albright T. Key indicators of phase transition for clinical trials through machine learning. Drug Discovery Today 2020;25(2):414-421. https://doi.org/10.1016/j.drudis.2019.12.014
Wu K, Wu E, DAndrea M, Chitale N, Lim M, Dabrowski M, Kantor K, Rangi H, Liu R, Garmhausen M, Pal N, Harbron C, Rizzo S, Copping R, Zou J. Machine Learning Prediction of Clinical Trial Operational Efficiency. The AAPS Journal 2022;24(3):57. https://doi.org/10.1208/s12248-022-00703-3
Harrer S, Shah P, Antony B, Hu J. Artificial Intelligence for Clinical Trial Design. Trends in Pharmacological Sciences 2019;40(8):577-591. https://doi.org/10.1016/j.tips.2019.05.005
Kimko HC, Duffull SB. Simulation for designing clinical trials. Marcel Dekker Incorporated 2002.https://doi.org/10.1201/9780203910276
Follett L, Geletta S, Laugerman M. Quantifying risk associated with clinical trial termination: A text mining approach. Information Processing & Management 2019;56(3):516-525. https://doi.org/10.1016/j.ipm.2018.11.009
Kavalci E, Hartshorn A. Improving clinical trial design using interpretable machine learning based prediction of early trial termination. Scientific Reports 2023;13(1):121. https://doi.org/10.1038/s41598-023-27416-7
Elkin M, Zhu X. Predictive modeling of clinical trial terminations using feature engineering and embedding learning. Scientific Reports 2021;11(1):3446. https://doi.org/10.1038/s41598-021-82840-x
Gresham G. ClinicalTrials.gov. Principles and Practice of Clinical Trials 2020:1-18. https://doi.org/10.1007/978-3-319-52677-5_266-1
Chalmers E, Hill D, Zhao V, Lou E. Prescriptive analytics applied to brace treatment for AIS: a pilot demonstration. Scoliosis 2015;10(S2):1-4. https://doi.org/10.1186/1748-7161-10-s2-s13
Ezike T, Okpala U, Onoja U, Nwike C, Ezeako E, Okpara O, Okoroafor C, Eze S, Kalu O, Odoh E, Nwadike U, Ogbodo J, Umeh B, Ossai E, Nwanguma B. Advances in drug delivery systems, challenges and future directions. Heliyon 2023;9(6):e17488. https://doi.org/10.1016/j.heliyon.2023.e17488
Homayun B, Lin X, Choi H. Challenges and Recent Progress in Oral Drug Delivery Systems for Biopharmaceuticals. Pharmaceutics 2019;11(3):129. https://doi.org/10.3390/pharmaceutics11030129
Ugurlu S. Investigation of metallacages for cisplatin encapsulation using Density Functional Theory (DFT). OAJ Materials and Devices 2024;8 https://doi.org/10.26434/chemrxiv-2024-mp5z0
Mitchell M, Billingsley M, Haley R, Wechsler M, Peppas N, Langer R. Engineering precision nanoparticles for drug delivery. Nature Reviews Drug Discovery 2020;20(2):101-124. https://doi.org/10.1038/s41573-020-0090-8
Wen H, Jung H, Li X. Drug Delivery Approaches in Addressing Clinical Pharmacology-Related Issues: Opportunities and Challenges. The AAPS Journal 2015;17(6):1327-1340. https://doi.org/10.1208/s12248-015-9814-9
Bhandare A, Nannor KM. Bioavailability in drug design and development: A comprehensive review. World Journal of Pharmaceutical Research 2024;13(17):145–168.
Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal 2021;8(2):e188-e194. https://doi.org/10.7861/fhj.2021-0095
Vidhya K, Sultana A, M N, Rangareddy H. Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside. Cureus 2023;15(10). https://doi.org/10.7759/cureus.47486
Vora L, Gholap A, Jetha K, Thakur R, Solanki H, Chavda V. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 2023;15(7):1916. https://doi.org/10.3390/pharmaceutics15071916
Deng J, Dong W, Socher R, Li L, Kai Li , Li Fei-Fei . ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition 2009:248-255. https://doi.org/10.1109/cvpr.2009.5206848
Bannigan P, Aldeghi M, Bao Z, Häse F, Aspuru-Guzik A, Allen C. Machine learning directed drug formulation development. Advanced Drug Delivery Reviews 2021;175:113806. https://doi.org/10.1016/j.addr.2021.05.016
Mhatre S, Shukla S, Chavda V, Gandikota L, Patravale V. AI and ML for Development of Cell and Gene Therapy for Personalized Treatment. Bioinformatics Tools for Pharmaceutical Drug Product Development 2023:371-400. https://doi.org/10.1002/9781119865728.ch16
Dong Y, Yang T, Xing Y, Du J, Meng Q. Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes. Processes 2023;11(7):2096. https://doi.org/10.3390/pr11072096
Bhattamisra S, Banerjee P, Gupta P, Mayuren J, Patra S, Candasamy M. Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data and Cognitive Computing 2023;7(1):10. https://doi.org/10.3390/bdcc7010010
Minich D, Bland J. Personalized Lifestyle Medicine: Relevance for Nutrition and Lifestyle Recommendations. The Scientific World Journal 2013;2013(1):129841. https://doi.org/10.1155/2013/129841
Larry Jameson J, Longo D. Precision Medicine—Personalized, Problematic, and Promising. Obstetrical & Gynecological Survey 2015;70(10):612-614. https://doi.org/10.1097/01.ogx.0000472121.21647.38
Gray I, Kross A, Renfrew M, Wood P. Precision Medicine in Lifestyle Medicine: The Way of the Future?. American Journal of Lifestyle Medicine 2019;14(2):169-186. https://doi.org/10.1177/1559827619834527
KRAVITZ R, DUAN N, BRASLOW J. Evidence‐Based Medicine, Heterogeneity of Treatment Effects, and the Trouble with Averages. The Milbank Quarterly 2004;82(4):661-687. https://doi.org/10.1111/j.0887-378x.2004.00327.x
Hartman E, Grieve R, Ramsahai R, Sekhon J. From Sample Average Treatment Effect to Population Average Treatment Effect on the Treated: Combining Experimental with Observational Studies to Estimate Population Treatment Effects. Journal of the Royal Statistical Society Series A: Statistics in Society 2015;178(3):757-778. https://doi.org/10.1111/rssa.12094
Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020:baaa010.
Rahman MH, Islam T, Hossen ME, Chowdhury ME, Hayat R. Machine learning in healthcare: From diagnostics to personalized medicine and predictive analytics. Integrative Biomedical Research 2024;8(12):1–8.
Libbrecht M, Noble W. Machine learning applications in genetics and genomics. Nature Reviews Genetics 2015;16(6):321-332. https://doi.org/10.1038/nrg3920
Zhang Y, Li G, Bian W, Bai Y, He S, Liu Y, Liu H, Liu J. Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis. Annals of Translational Medicine 2022;10(24):1394-1394. https://doi.org/10.21037/atm-22-5986
Abraham J, Heimberger A, Marshall J, Heath E, Drabick J, Helmstetter A, Xiu J, Magee D, Stafford P, Nabhan C, Antani S, Johnston C, Oberley M, Korn W, Spetzler D. Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type. Translational Oncology 2021;14(3):101016. https://doi.org/10.1016/j.tranon.2021.101016
Afrifa‐Yamoah E, Adua E, Peprah‐Yamoah E, Anto E, Opoku‐Yamoah V, Acheampong E, Macartney M, Hashmi R. Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges. Chronic Diseases and Translational Medicine 2024;11(1):1-21. https://doi.org/10.1002/cdt3.137
Kaur Thethi S. Chapter 8 Machine learning models for cost-effective healthcare delivery systems: A global perspective. Digital Transformation in Healthcare 5.0 2024;:199-244. https://doi.org/10.1515/9783111327853-008
Topol E. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 2019;25(1):44-56. https://doi.org/10.1038/s41591-018-0300-7
Chakilam C. Next-generation healthcare: Merging ai, ml, and big data for accelerated disease diagnosis and personalized treatment. American Online Journal of Science and Engineering (AOJSE) 2023;1(1).
Marcu LG, Marcu DC. Pharmacogenomics and big data in medical oncology: developments and challenges. Therapeutic advances in medical oncology 2024;16:17588359241287658.
Wang H, Huang J, Fang X, Liu M, Fan X, Li Y. Advances in next-generation sequencing (NGS) applications in drug discovery and development. Expert Opinion on Drug Discovery 2025;20(4):537-550. https://doi.org/10.1080/17460441.2025.2481262
Barros M, Paci M, Tervonen A, Passini E, Koivumäki J, Hyttinen J, Lenk K. From Multiscale Biophysics to Digital Twins of Tissues and Organs: Future Opportunities for in-silico Pharmacology. IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2024;10(4):576-594. https://doi.org/10.1109/tmbmc.2024.3442083
Corti A, Colombo M, Migliavacca F, Rodriguez Matas J, Casarin S, Chiastra C. Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models. Frontiers in Bioengineering and Biotechnology 2021;9:744560. https://doi.org/10.3389/fbioe.2021.744560
Gradeci D, Bove A, Charras G, Lowe A, Banerjee S. Single-cell approaches to cell competition: High-throughput imaging, machine learning and simulations. Seminars in Cancer Biology 2020;63:60-68. https://doi.org/10.1016/j.semcancer.2019.05.007
Sivakumar N, Mura C, Peirce S. Innovations in integrating machine learning and agent-based modeling of biomedical systems. Frontiers in Systems Biology 2022;2:959665. https://doi.org/10.3389/fsysb.2022.959665
Cogno N, Axenie C, Bauer R, Vavourakis V. Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation. Cancer Biology & Therapy 2024;25(1):2344600. https://doi.org/10.1080/15384047.2024.2344600
Deist T, Patti A, Wang Z, Krane D, Sorenson T, Craft D. Simulation-assisted machine learning. Bioinformatics 2019;35(20):4072-4080. https://doi.org/10.1093/bioinformatics/btz199
Ali M, Benfante V, Basirinia G, Alongi P, Sperandeo A, Quattrocchi A, Giannone A, Cabibi D, Yezzi A, Di Raimondo D, Tuttolomondo A, Comelli A. Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues. Journal of Imaging 2025;11(2):59. https://doi.org/10.3390/jimaging11020059
Choi H, Wang C, Pan X, Jang J, Cao M, Brazzo J, Bae Y, Lee K. Emerging machine learning approaches to phenotyping cellular motility and morphodynamics. Physical Biology 2021;18(4):041001. https://doi.org/10.1088/1478-3975/abffbe
Lee R, Wu Y, Goh D, Tan V, Ng C, Lim J, Lau M, Yeong J. Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment. Advanced Healthcare Materials 2023;12(14):2202457. https://doi.org/10.1002/adhm.202202457
Wang S, Rong R, Yang DM, Fujimoto J, Yan S, Cai L, Yang L, Luo D, Behrens C, Parra ER. Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer research 2020;80(10):2056–2066.
He W, Kong S, Lin R, Xie Y, Zheng S, Yin Z, Huang X, Su L, Zhang X. Machine Learning Assists in the Design and Application of Microneedles. Biomimetics 2024;9(8):469. https://doi.org/10.3390/biomimetics9080469
Datta S, Islam M, Rahman Sobuz M, Ahmed S, Kar M. Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review. Heliyon 2024;10(5):e26888. https://doi.org/10.1016/j.heliyon.2024.e26888
Clancy C, An G, Cannon W, Liu Y, May E, Ortoleva P, Popel A, Sluka J, Su J, Vicini P, Zhou X, Eckmann D. Multiscale Modeling in the Clinic: Drug Design and Development. Annals of Biomedical Engineering 2016;44(9):2591-2610. https://doi.org/10.1007/s10439-016-1563-0
Gondal M, Chaudhary S. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Frontiers in Oncology 2021;11:712505. https://doi.org/10.3389/fonc.2021.712505
Kim S, Thiessen P, Bolton E, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker B, Wang J, Yu B, Zhang J, Bryant S. PubChem Substance and Compound databases. Nucleic Acids Research 2015;44(D1):D1202-D1213. https://doi.org/10.1093/nar/gkv951
Gaulton A, Hersey A, Nowotka M, Bento A, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis L, Cibrián-Uhalte E, Davies M, Dedman N, Karlsson A, Magariños M, Overington J, Papadatos G, Smit I, Leach A. The ChEMBL database in 2017. Nucleic Acids Research 2016;45(D1):D945-D954. https://doi.org/10.1093/nar/gkw1074
Chen X, Liu M, Gilson M. BindingDB: A Web-Accessible Molecular Recognition Database. Combinatorial Chemistry & High Throughput Screening 2001;4(8):719-725. https://doi.org/10.2174/1386207013330670
Berman H, Battistuz T, Bhat T, Bluhm W, Bourne P, Burkhardt K, Feng Z, Gilliland G, Iype L, Jain S, Fagan P, Marvin J, Padilla D, Ravichandran V, Schneider B, Thanki N, Weissig H, Westbrook J, Zardecki C. The Protein Data Bank. Acta Crystallographica Section D Biological Crystallography 2002;58(6):899-907. https://doi.org/10.1107/s0907444902003451
Downloads
Published
Issue
Section
License
Copyright (c) 2025 French-Ukrainian Journal of Chemistry
This work is licensed under a Creative Commons Attribution 4.0 International License.
The French‑Ukrainian Journal of Chemistry holds copyright and publishes all articles under a Creative Commons Attribution 4.0 International licence (CC BY 4.0).
This license permits unrestricted use, sharing, adaptation, distribution, and reproduction in any medium or format, provided that the original author(s) and source are credited, a link to the license is included, and any changes made are indicated.
Authors grant the French‑Ukrainian Journal of Chemistry the exclusive right of first publication and may enter into separate, non‑exclusive distribution agreements for the published version (e.g., institutional repository, book chapter). Authors are also encouraged to post pre‑prints and post‑prints online to increase visibility and citation.
