Artificial Intelligence for Computer-Aided Drug Discovery

Abstract

The continuous implementation of Artificial Intelligence (AI) in multiple scientific domains and the rapid advancement in computer software and hardware, along with other parameters, have rapidly fuelled this development. The technology can contribute effectively in solving many challenges and constraints in the traditional development of the drug. Traditionally, large-scale chemical libraries are screened to find one promising medicine. In recent years, more reasonable structure-based drug design approaches have avoided the first screening phases while still requiring chemists to design, synthesize, and test a wide range of compounds to produce possible novel medications. The process of turning a promising chemical into a medicinal candidate can be expensive and time-consuming. Additionally, a new medication candidate may still fail in clinical trials even after demonstrating promise in laboratory research. In fact, less than 10% of medication candidates that undergo Phase I trials really reach the market. As a consequence, the unmatched data processing power of AI systems may expedite and enhance the drug development process in four different ways: by opening up links to novel biological systems, superior or distinctive chemistry, greater success rates, and faster and less expensive innovation trials. Since these technologies may be used to address a variety of discovery scenarios and biological targets, it is essential to comprehend and distinguish between use cases. As a result, we have emphasized how AI may be used in a variety of areas of the pharmaceutical sciences, including in-depth opportunities for drug research and development.

Key words

artificial intelligence - artificial neural networks - computer-aided drug design - deep learning - drug design and discovery - machine learning - quantitative structure-activity relationship

Publikationsverlauf

Eingereicht: 17. März 2023

Angenommen: 11. April 2023

Artikel online veröffentlicht:
05. Juni 2023

• References

• 1 Bajorath J, Kearnes S, Walters WP. et al. Artificial intelligence in drug discovery: into the great wide open. J Med Chem 2020; 63: 8651-8652
• 2 Jiménez-Luna J, Grisoni F, Weskamp N. et al. Artificial intelligence in drug discovery: Recent advances and future perspectives. Expert Opin Drug Discov 2021; 16: 949-959
• 3 Vijayan RS, Kihlberg J, Cross JB. et al. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov Today 2022; 27: 967-84
• 4 Schneider P, Walters WP, Plowright AT. et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 2020; 19: 353-364
• 5 Chan HS, Shan H, Dahoun T. et al. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci 2019; 40: 592-604
• 6 Mitchell JB.. Artificial intelligence in pharmaceutical research and development. Future Med Chem 2018; 10: 1529-1531
• 7 Rohall SL, Auch L, Gable J. et al. An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations. J Med Chem 2020; 63: 8824-8834
• 8 Malandraki-Miller S, Riley PR.. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021; 26: 887-901
• 9 Vamathevan J, Clark D, Czodrowski P. et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019; 18: 463-477
• 10 Paul D, Sanap G, Shenoy S. et al. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26: 80-93
• 11 Ramesh AN, Kambhampati C, Monson JR. et al. Artificial intelligence in medicine. Ann R Coll Surg Engl 2004; 86: 334-338
• 12 Kalyane D, Sanap G, Paul D. et al. Chapter 3 - Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In: Tekade RKBT-TF of PPD and R, Hrsg. Advances in Pharmaceutical Product Development and Research. Academic Press; 2020: 73-107
• 13 Hammett LP.. The Effect of Structure upon the Reactions of Organic Compounds. Benzene Derivatives. J Am Chem Soc 1937; 59: 96-103
• 14 Hansch C, Fujita T.. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure. J Am Chem Soc 1964; 86: 1616-1626
• 15 Miller E, Hansch C.. Structure-Activity Analysis of Tetrahydrofolate Analogs Using Substituent Constants and Regression Analysis. J Pharm Sci 1967; 56: 92-97
• 16 Weinstein JN, Kohn KW, Grever MR. et al. Neural Computing in Cancer Drug Development: Predicting Mechanism of Action. Science (80- ) 1992; 258: 447-451
• 17 Mak K-K, Pichika MR.. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 2019; 24: 773-780
• 18 Bahl M, Barzilay R, Yedidia AB. et al. High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision. Radiology 2018; 286: 810-818
• 19 Walker AJ.. The potential application of artificial intelligence in transport. IEE Proc – Intell Transp Syst 2006; 153: 183-198
• 20 Seddon G, Lounnas V, McGuire R. et al. Drug design for ever, from hype to hope. J Comput Aided Mol Des 2012; 26: 137-150
• 21 Gunčar G, Kukar M, Notar M. et al. An application of machine learning to haematological diagnosis. Sci Rep 2018; 8: 411
• 22 Koohy H.. The rise and fall of machine learning methods in biomedical research. F1000Research 2017; 6: 2012
• 23 Ezrachi A, Stucke ME.. Artificial intelligence & collusion: When computers inhibit competition. U Ill L Rev 2017; 1775
• 24 Steels L, Brooks R.. The artificial life route to artificial intelligence: Building embodied, situated agents. Routledge; 2018
• 25 Khanna T.. Foundations of neural networks. Addison-Wesley Longman Publishing Co., Inc; 1990
• 26 Chen H, Engkvist O, Wang Y. et al. The rise of deep learning in drug discovery. Drug Discov Today 2018; 23: 1241-1250
• 27 Hänggi M, Moschytz GS.. Robust Template Design. In: Cellular Neural Networks. Springer; 2000: 23-46
• 28 Segler MHS, Preuss M, Waller MP.. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018; 555: 604-610
• 29 Jain AK, Mao J, Mohiuddin KM.. Artificial neural networks: A tutorial. Computer (Long Beach Calif) 1996; 29: 31-44
• 30 Grys BT, Lo DS, Sahin N. et al. Machine learning and computer vision approaches for phenotypic profiling. J Cell Biol 2017; 216: 65-71
• 31 Jabbar MA, Samreen S, Aluvalu R.. The future of health care: Machine learning. Int J Eng Technol 2018; 7: 23
• 32 Lamberti MJ, Wilkinson M, Donzanti BA. et al. A study on the application and use of artificial intelligence to support drug development. Clin Ther 2019; 41: 1414-1426
• 33 Huang Z, Juarez JM, Li X.. Data Mining for Biomedicine and Healthcare. J Healthc Eng 2017; 2017: 1-2
• 34 Mamoshina P, Vieira A, Putin E. et al. Applications of Deep Learning in Biomedicine. Mol Pharm 2016; 13: 1445-1454
• 35 Segler MH, Kogej T, Tyrchan C. et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent Sci 2018; 4: 120-131
• 36 Okafo G.. Adapting drug discovery to artificial intelligence. Drug Target Rev 2018; 50-52
• 37 Hessler G, Baringhaus K-H.. Artificial Intelligence in Drug Design. Molecules 2018; 23
• 38 Yang X, Wang Y, Byrne R. et al. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119: 10520-10594
• 39 Öztürk H, Özgür A, Ozkirimli E.. DeepDTA: deep drug–target binding affinity prediction. Bioinformatics 2018; 34: i821-i829
• 40 Rupp M, Körner R, Tetko IV.. Estimation of acid dissociation constants using graph kernels. Mol Inform 2010; 29: 731-740
• 41 Putin E, Asadulaev A, Ivanenkov Y. et al. Reinforced adversarial neural computer for de novo molecular design. J Chem Inf Model 2018; 58: 1194-1204
• 42 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; 53: 1563-1575
• 43 Kumar R, Sharma A, Siddiqui MH. et al. Prediction of human intestinal absorption of compounds using artificial intelligence techniques. Curr Drug Discov Technol 2017; 14: 244-254
• 44 Ryu JY, Kim HU, Lee SY.. Deep learning improves prediction of drug–drug and drug–food interactions. Proc Natl Acad Sci 2018; 115: E4304-E4311
• 45 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
• 46 Popova M, Isayev O, Tropsha A.. Deep reinforcement learning for de novo drug design. Sci Adv 2018; 4: eaap7885
• 47 Reddy AS, Zhang S.. Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol 2013; 6: 41-47
• 48 Jumper J, Evans R, Pritzel A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596: 583-589
• 49 Mayr A, Klambauer G, Unterthiner T. et al. DeepTox: toxicity prediction using deep learning. Front Environ Sci 2016; 3: 80
• 50 Worth CL, Preissner R, Blundell TL.. SDM – a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res 2011; 39: W215-W222
• 51 Yang Y, Heffernan R, Paliwal K. et al. Spider2: A package to predict secondary structure, accessible surface area, and main-chain torsional angles by deep neural networks. Predict protein Second Struct 2017; 55-63
• 52 Yuan Q, Yang Y.. Sequence-Based Predictions of Residues that Bind Proteins and Peptides. In: Machine Learning in Bioinformatics of Protein Sequences: Algorithms, Databases and Resources for Modern Protein Bioinformatics. World Scientific; 2023: 237-263
• 53 Kumar K, Kumar P, Deb D. et al. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. In: Healthcare. Multidisciplinary Digital Publishing Institute; 2023: 207
• 54 Ma W, Zhang S, Li Z. et al. Predicting Drug-Target Affinity by Learning Protein Knowledge From Biological Networks. IEEE J Biomed Heal Informatics. 2023
• 55 D’Souza S, Prema KV, Balaji S. et al. Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins. Interdiscip Sci Comput Life Sci 2023; 1-10
• 56 Krishnan A, Vinod D.. Graph Convolutional Capsule Regression (GCCR): A Model for Accelerated Filtering of Novel Potential Candidates for SARS-CoV-2 Based on Binding Affinity. Curr Comput Aided Drug Des. 2023
• 57 Lysenko A, Sharma A, Boroevich KA. et al. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci alliance 2018; 1(6): e201800098
• 58 Bhattamisra SK, Banerjee P, Gupta P. et al. Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data Cogn Comput 2023; 7: 10
• 59 Ahmadian M, Ahmadian S, Ahmadi M.. RDERL: Reliable deep ensemble reinforcement learning-based recommender system. Knowledge-Based Syst 2023; 110289
• 60 Amanatidis D, Vaitsi K, Dossis M.. Deep Neural Network Applications for Bioinformatics. In: 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). IEEE 2022; 1-9
• 61 Nag S, Baidya AT, Mandal A. et al. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12: 110
• 62 Rapicavoli RV, Alaimo S, Ferro A. et al. Computational Methods for Drug Repurposing. Adv Exp Med Biol 2022; 1361: 119-141
• 63 De Glas NA, Bastiaannet E, Engels CC. et al. Validity of the online PREDICT tool in older patients with breast cancer: a population-based study. Br J Cancer 2016; 114: 395-400
• 64 Kim S, Chen J, Cheng T. et al. PubChem 2023 update. Nucleic Acids Res 2023; 51: D1373-D1380
• 65 Xie Z, Wang H, Han S. et al. DeepVS: A deep learning approach for RF-based vital signs sensing. InProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2022
• 66 Matlock MK, Hughes TB, Swamidass SJ.. XenoSite server: a web-available site of metabolism prediction tool. Bioinformatics 2015; 31: 1136-1137
• 67 Šícho M, de Bruyn Kops C, Stork C. et al. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. J Chem Inf Model 2017; 57: 1832-1846
• 68 Rydberg P, Gloriam DE, Zaretzki J. et al. SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism. ACS Med Chem Lett 2010; 1: 96-100