Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. 2019;20:1878–912.

Article  CAS  PubMed  Google Scholar 

Iwata H. Application of in Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis. Chem Pharm Bull (Tokyo). 2023;71:398–405.

Article  CAS  PubMed  Google Scholar 

Nag S, Baidya A, Mandal A, Mathew AT, Das B, Devi B, Kumar R. Deep learning tools for advancing drug discovery and development. 3 Biotech. 2022;12:110.

Article  PubMed  PubMed Central  Google Scholar 

Naga D, Parrott N, Ecker GF, Olivares-Morales A. Evaluation of the success of high-throughput physiologically based pharmacokinetic (HT-PBPK) modeling predictions to inform early drug discovery. Mol Pharm. 2022;19:2203–16.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jayaraj PB, Jain S. Ligand based virtual screening using SVM on GPU. Comput Biol Chem. 2019;83:107143.

Article  CAS  PubMed  Google Scholar 

Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med. 2021;137:104851.

Article  PubMed  Google Scholar 

Dezső Z, Ceccarelli M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinforma. 2020;21:1–12.

Article  Google Scholar 

Pliakos K, Vens C. Drug-target interaction prediction with tree-ensemble learning and output space reconstruction. BMC Bioinforma. 2020;21:11.

Article  Google Scholar 

Page MJ, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ [Internet], British Medical Journal Publishing Group, 2021.

“WoS Query”, Accessed: Jan. 06, 2024. [Online]. Available: https://www.webofscience.com/wos/woscc/summary/60417e9f-5b12-4a50-b463-97d9caf10abc-c2f38ce2/relevance/1

Niazi SK, Mariam Z. Recent advances in machine-learning-based chemoinformatics: a comprehensive review. Int J Mol Sci. 2023;24:11488.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Podlewska S, Kurczab R. Mutual Support of Ligand-and Structure-Based Approaches—To What Extent We Can Optimize the Power of Predictive Model? Case Study of Opioid Receptors. Molecules. 2021;26:1607.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Onawole AT, Hussein IA, Saad MA, Ahmed MEM, Nimir H. Computational screening of potential inhibitors of desulfobacter postgatei for pyrite scale prevention in oil and gas wells. ACS Omega. 2021;6:10607–17.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang Z, Belecciu T, Eaves J, Reimers M, Bachmann MH, Woldring D. Phytochemical drug discovery for COVID-19 using high-resolution computational docking and machine learning assisted binder prediction. J Biomol Struct Dyn. 2023;41:6643–6663.

Article  CAS  PubMed  Google Scholar 

Wu Y, Song Z, Little JC, Zhong M, Li H, Xu Y. An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes. Environ Int. 2021;156:106748.

Article  CAS  PubMed  Google Scholar 

Jiang D, Lei T, Wang Z, Shen C, Cao D, Hou T. ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning. J Cheminform. 2020;12:1–26.

Article  Google Scholar 

Huang X, Tang F, Hua Y, Li X. In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods. Chem Biol Drug Des. 2021;98:248–57.

Article  CAS  PubMed  Google Scholar 

Mamoshina P, Bueno-Orovio A, Rodriguez B. Dual transcriptomic and molecular machine learning predicts all major clinical forms of drug cardiotoxicity. Front Pharm. 2020;11:639.

Article  Google Scholar 

Meng J, Zhang L, Wang L, Li S, Xie D, Zhang Y, Liu H. TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction. Toxicology. 2021;464:153018.

Article  CAS  PubMed  Google Scholar 

Kadioglu O, Klauck SM, Fleischer E, Shan L, Efferth T. Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation. Arch Toxicol. 2021;95:2485–95.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nath A, Sahu GK. Exploiting ensemble learning to improve prediction of phospholipidosis inducing potential. J Theor Biol. 2019;479:37–47.

Article  CAS  PubMed  Google Scholar 

“EMA in London.” Accessed: Jan. 07, 2024. [Online]. Available: https://www.ema.europa.eu/en/about-us/history-ema/ema-london-1995-2019

“Basel.” Accessed: Jan. 07, 2024. [Online]. Available: https://baselarea.swiss/blog-post/biotech-companies-wing-their-way-to-the-basel-area/

Zdrazil, B. et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods, Nucleic Acids Res. p. gkad1004, 2023.

“ChEMBL.” Accessed: Jan. 04, 2024. [Online]. Available: https://www.ebi.ac.uk/chembl/

Zhao J, Yang J, Tian S, Zhang W. A survey of web resources and tools for the study of TCM network pharmacology. Quant Biol. 2019;7:17–29.

Article  CAS  Google Scholar 

Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine learning methods in drug discovery. Molecules. 2020;25:5277.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, et al. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J. 2021;19:4538–58.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics. 2023;15:1916.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Udegbe FC, Ebulue OR, Ebulue CC, Ekesiobi CS. Machine Learning in Drug Discovery: A critical review of applications and challenges. Computer Sci IT Res J. 2024;5:892–902.

Article  Google Scholar 

Dara S, Dhamercherla S, Jadav SS, Babu CHM, Ahsan MJ. Machine learning in drug discovery: a review. Artif Intell Rev. 2022;55:1947–99.

Article  PubMed  Google Scholar 

Zhong Z, Barkova A, Mottin, D. Knowledge-augmented graph machine learning for drug discovery: A survey from precision to interpretability, arXiv preprint arXiv:2302.08261, 2023. https://doi.org/10.48550/arXiv.2302.08261

Boswell Z, et al. In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses. Infect Drug Resist. 2023;2321–38.

Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou MM, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev. 2021;41:1427–73.

Article  PubMed  Google Scholar 

Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches. Biotechnol Bioprocess Eng. 2020;25:895–930.

Article  CAS  PubMed  Google Scholar 

Seifert MHJ, Wolf K, Vitt D. Virtual high-throughput in silico screening. Biosilico. 2003;1:143–9.

Article  CAS  Google Scholar 

Birkholtz L-M, et al. In silico Discovery of Chemotherapeutic Agents, in Infectious Disease Informatics, Springer, 2009, pp. 279–304.

Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, et al. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med. 2022;5:171.

Article  PubMed  PubMed Central  Google Scholar 

Lipkova J, Chen RJ, Chen B, Lu MY, Barbieri M, Shao D, et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022;40:1095–110.

Article