The Role of Artificial Intelligence and Machine Learning in Revolutionizing Drug Discovery
The Role of Artificial Intelligence and Machine Learning in Revolutionizing Drug Discovery |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-5 |
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| Year of Publication : 2024 | ||
| Author : Varsha D. Jadhav, Dhananjay R. Dolas, Amar Buchade, Sachin N. Deshmukh |
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| DOI : 10.14445/22315381/IJETT-V72I5P114 | ||
How to Cite?
Varsha D. Jadhav, Dhananjay R. Dolas, Amar Buchade, Sachin N. Deshmukh, "The Role of Artificial Intelligence and Machine Learning in Revolutionizing Drug Discovery," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 131-140, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P114
Abstract
Today Artificial Intelligence (AI) is transforming the practice of drug discovery over a few years and has revolutionized the field. There are several artificial intelligence and machine learning techniques used in drug discovery. This paper describes the use of AI and ML in the development of drugs so that the results are more accurate and efficient. An organized evaluation of the review is carried out with the help of keyword searches in the available databases related to context, methods, and full text. The brief survey describes AI and ML in drug discovery making it cost effective both in time and money spent. The prevalent application of AI and ML methods indicates a blooming future for the drug industry. This will help researchers and the drug industry to utilize AI and ML in drug discovery growth.
Keywords
Artificial Intelligence, Drug Discovery, Explainable Artificial Intelligence, Generative Artificial Intelligence, Machine Learning.
References
[1] Prashansa Agrawal, “Artificial Intelligence in Drug Discovery and Development,” Journal of Pharmacovigilance, vol. 6, no. 2, pp. 1-2, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] S. Agatonovic-Kustrin, and R. Beresford, “Basic Concepts of Artificial Neural Network (ANN) Modeling and its Application in Pharmaceutical Research,” Journal of Pharmaceutical and Biomedical Analysis, vol. 22, no. 5, pp. 717-727, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Alexandra T. Greenhill, and Bethany R. Edmunds, “A Primer of Artificial Intelligence in Medicine,” Techniques and Innovations in Gastrointestinal Endoscopy, vol. 22, no. 2, pp. 85-89, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Fabio Boniolo et al., “Artificial Intelligence in Early Drug Discovery Enabling Precision Medicine,” Expert Opinion on Drug Discovery, vol. 16, no. 9, pp. 991-1007, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sonal Priya et al., “Machine Learning Approaches and their Applications in Drug Discovery and Design,” Chemical Biology & Drug Design, vol. 100, no. 1, pp. 136-153, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Alexandre Blanco-González et al., “The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies,” Pharmaceuticals, vol. 16, no. 6, pp. 1-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Debleena Paul et al., “Artificial Intelligence in Drug Discovery and Development,” Drug Discovery Today, vol. 26, no. 1, pp. 80-93, 2021.
[CrossRef] [Publisher Link]
[8] Rafael V.C. Guido, Glaucius Oliva, and Adriano D. Andricopulo, “Modern Drug Discovery Technologies: Opportunities and Challenges in Lead Discovery,” Combinatorial Chemistry & High Throughput Screening, vol. 14, no. 10, pp. 830-839, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Wei Chen et al., “Artificial Intelligence for Drug Discovery: Resources, Methods, and Applications,” Molecular Therapy Nucleic Acids, vol. 31, pp. 691-702, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jianyuan Deng et al., “Artificial Intelligence in Drug Discovery: Applications and Techniques,” Briefings in Bioinformatics, vol. 23, no. 1, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Esther F. Schmid, Keith James, and Dennis A. Smith, “The Impact of Technological Advances on Drug Discovery Today,” Drug Information Journal: DIJ / Drug Information Association, vol. 35, pp. 41-45, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Lauv Patel et al., “Machine Learning Methods in Drug Discovery,” Molecules, vol. 25, no. 22, pp. 1-17, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Anthony C. Chang, “Big Data in Medicine: The Upcoming Artificial Intelligence,” Progress in Pediatric Cardiology, vol. 43, pp. 91-94, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Andreas H. Göller et al., “Bayer’s in Silico ADMET Platform: A Journey of Machine Learning over the Past Two Decades,” Drug Discovery Today, vol. 25, no. 9, pp. 1702-1709, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Israa Abu-elezz et al., “The Benefits and Threats of Blockchain Technology in Healthcare: A Scoping Review,” International Journal of Medical Informatics, vol. 142, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Maged N. Kamel Boulos, James T. Wilson, and Kevin A. Clauson, “Geospatial Blockchain: Promises, Challenges, and Scenarios in Health and Healthcare,” International Journal of Health Geographics, vol. 17, pp. 1-10, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Byeong Ju Park et al., “Pharmaceutical Applications of 3D Printing Technology: Current Understanding and Future Perspectives,” Journal of Pharmaceutical Investigation, vol. 49, pp. 575-585, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Richard S. Sutton, and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, 1998.
[Google Scholar] [Publisher Link]
[19] Huidong Tang et al., “EarlGAN: An Enhanced Actor Critic Reinforcement Learning Agent-Driven GAN for de novo Drug Design,” Pattern Recognition Letters, vol. 175, pp. 45-51, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Benjamin Q. Huynh, Hui Li, and Maryellen L. Giger, “Digital Mammographic Tumor Classification Using Transfer Learning From Deep Convolutional Neural Networks,” Journal of Medical Imaging, vol. 3, no. 3, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hironao Yamada et al., “Predicting Materials Properties with Little Data Using Shotgun Transfer Learning,” American Chemical Society Central Publication, vol. 5, no. 10, pp. 1717-1730, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Andrew Arnold, Ramesh Nallapati, and William W. Cohen, “A Comparative Study of Methods for Transductive Transfer Learning,” Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), Omaha, NE, USA, pp. 77-82, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Chaeyoung Moon, and Dongsup Kim, “Prediction of Drug–Target Interactions through Multi-Task Learning,” Scientific Reports, vol. 12, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Dongfang Li et al., “Drug Knowledge Discovery via Multi-Task Learning and Pre-Trained Models,” BMC Medical Informatics and Decision Making, vol. 21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Yu Zhang, and Qiang Yang, “A Survey on Multi-Task Learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5586-5609, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Rich Caruana, “Multitask Learning,” Machine Learning, vol. 28, pp. 41-75, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Suresh Dara et al., “Machine Learning in Drug Discovery: A Review,” Artificial Intelligence Review, vol. 55, no. 3, pp. 1947-1999, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Muhammad Ammad-Ud-Din et al., “Systematic Identification of Feature Combinations for Predicting Drug Response with Bayesian Multi-View Multi-Task Linear Regression,” Bioinformatics, vol. 33, no. 14, pp. i359-i368, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Xiyan He et al., “Multi-Task Learning with One-Class SVM,” Neurocomputing, vol. 133, pp. 416-426, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Ravi Manne, “Machine Learning Techniques in Drug Discovery and Development,” International Journal of Applied Research, vol. 7, no. 4, pp. 21-28, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Jie Yu, Xutong Li, and Mingyue Zheng, “Current Status of Active Learning for Drug Discovery,” Artificial Intelligence in the Life Sciences, vol. 1, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Chun-Teh Chen, and Grace X. Gu, “Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning,” Advanced Science, vol. 7, no. 5, pp. 1-10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Daniel Reker, “Practical Considerations for Active Machine Learning in Drug Discovery,” Drug Discovery Today: Technologies, vol. 32-33, pp. 73-79, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Manfred K. Warmuth et al., “Active Learning with Support Vector Machines in the Drug Discovery Process,” The Journal for Chemical Information and Computer Scientists, vol. 43, no. 2, pp. 667-673, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[35] QHwan Kim et al., “Bayesian Neural Network with Pretrained Protein Embedding Enhances Prediction Accuracy of Drug-Protein Interaction,” Bioinformatics, vol. 37, no. 20, pp. 3428-3435, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Faming Liang, Qizhai Li, and Lei Zhou, “Bayesian Neural Networks for Selection of Drug Sensitive Genes,” Journal of the American Statistical Association, vol. 113, no. 523, pp. 955-972, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Elizaveta Semenova et al., “A Bayesian Neural Network for Toxicity Prediction,” Computational Toxicology, vol. 16, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Kumar Shridhar, Felix Laumann, and Marcus Liwicki, “A Comprehensive Guide to Bayesian Convolutional Neural Network with Variational Inference,” arXiv, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[39] José Jiménez-Luna, Francesca Grisoni, and Gisbert Schneider, “Drug Discovery with Explainable Artificial Intelligence,” Nature Machine Intelligence, vol. 2, no. 10, pp. 573-584, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Ignacio Ponzoni, Juan Antonio Páez Prosper, and Nuria E. Campillo, “Explainable Artificial Intelligence: A Taxonomy and Guidelines for its Application to Drug Discovery,” WIREs Computational Molecular Science, vol. 13, no. 6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Bekir Aksoy, Mehmet Yücel, and Nergiz Aydin, “Using Explainable Artificial Intelligence in Drug Discovery: A Theoretical Research,” Explainable Machine Learning for Multimedia Based Healthcare Applications, pp. 181-190, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Vinicius M. Alves et al., “Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models,” Journal of Chemical Information and Modeling, vol. 58, no. 6, pp. 1214-1223, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Jurica Levatic et al., “Semi-Supervised Regression Trees with Application to QSAR Modelling,” Expert Systems with Applications, vol. 158, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Maria Korshunova et al., “Generative and Reinforcement Learning Approaches for the Automated De Novo Design of Bioactive Compounds,” Communications Chemistry, vol. 5, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Jiarui Ding, Anne Condon, and Sohrab P. Shah, “Interpretable Dimensionality Reduction of Single Cell Transcriptome Data with Deep Generative Models,” Nature Communications, vol. 9, no. 1, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Eugene Lin, Sudipto Mukherjee, and Sreeram Kannan, “A Deep Adversarial Variational Autoencoder Model for Dimensionality Reduction in Single-Cell RNA Sequencing Analysis,” BMC Bioinformatics, vol. 21, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Josep Arús-Pous et al., “SMILES-Based Deep Generative Scaffold Decorator for De-novo Drug Design,” Journal of Cheminformatics, vol. 12, pp. 1-18, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Andrew Steane, “Quantum Computing,” Reports on Progress in Physics, vol. 61, no. 2, pp. 117-173, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Rongxin Xia, and Sabre Kais, “Quantum Machine Learning for Electronic Structure Calculations,” Nature Communications, vol. 9, no. 1, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Peter Wittek, Quantum Machine Learning: What Quantum Computing Means to Data Mining, Elsevier Science, pp. 1-176, 2014.
[Google Scholar] [Publisher Link]
[51] Anas N. Al-Rabadi, Reversible Logic Synthesis: From Fundamentals to Quantum Computing, Springer Berlin Heidelberg, pp. 1-427, 2012.
[Google Scholar] [Publisher Link]
[52] Ji-An Li et al., “Quantum Reinforcement Learning During Human Decision-Making,” Nature Human Behaviour, vol. 4, no. 3, pp. 294-307, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[53] A. Sannia et al., “A Hybrid Classical-Quantum Approach to Speed-Up Q-Learning,” Scientific Reports, vol. 13, no. 1, 2023.
[Google Scholar] [Publisher Link]
[54] Y. Cao, J. Romero, and A. Aspuru-Guzik, “Potential of Quantum Computing for Drug Discovery,” IBM Journal of Research and Development, vol. 62, no. 6, pp. 6:1-6:20, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Michael Broughton et al., “Tensorflow Quantum: A Software Framework for Quantum Machine Learning,” arXiv, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Akshay Chadha, and Preeti Kaur, “Comparative Analysis of Recommendation System,” 2015 4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, Noida, India, pp. 313-318, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[57] Brent Smith, and Greg Linden, “Two Decades of Recommender Systems at Amazon.com,” IEEE Internet Computing, vol. 21, no. 3, pp. 12-18, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[58] Rung-Ching Chen et al., “A Recommendation System Based on Domain Ontology and SWRL for Anti-Diabetic Drugs Selection,” Expert Systems with Applications, vol. 39, no. 4, pp. 3995-4006, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[59] Van-Dai Ta, Chuan-Ming Liu, and Goodwill Wandile Nkabinde, “Big Data Stream Computing in Healthcare Real-Time Analytics,” 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, pp. 37-42, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[60] Ekaterina A. Sosnina et al., “Recommender Systems in Antiviral Drug Discovery,” ACS Omega, vol. 5, no. 25, pp. 15039-15051, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[61] Hafsa Habehh, and Suril Gohel, “Machine Learning in Healthcare,” Current Genomics, vol. 22, no. 4, pp. 291-300, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[62] Raj M. Ratwani, “Electronic Health Records and Improved Patient Care: Opportunities for Applied Psychology,” Current Directions in Psychological Science, vol. 26, no. 4, pp. 359-365, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Inmaculada Hernandez, and Yuting Zhang, “Using Predictive Analytics and Big Data to Optimize Pharmaceutical Outcomes,” American Journal of Health-System Pharmacy, vol. 74, no. 18, pp. 1494-1500, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Xin Yang et al., “Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery,” Chemical Reviews, vol. 119, no. 18, pp. 10520-10594, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[65] Heang-Ping Chan et al., “Deep Learning in Medical Image Analysis,” Advances in Experimental Medicine and Biology, vol. 1213, pp. 3-21, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Helen K Brittain, Richard Scott, and Ellen Thomas, “The Rise of the Genome and Personalised Medicine,” Clinical Medicine, vol. 17, no. 6, pp. 545-551, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[67] Raihan Rafique, S.M. Riazul Islam, and Julhash U. Kazi, “Machine Learning in the Prediction of Cancer Therapy,” Computational and Structural Biotechnology Journal, vol. 19, pp. 4003-4017, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Elliot B. Sloane, and Ricardo J. Silva, “Artificial Intelligence in Medical Devices and Clinical Decision Support Systems,” Clinical Engineering Handbook, pp. 556-658, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[69] Ali Talyshinskii et al., “Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care through Artificial Intelligence,” Current Urology Reports, vol. 25, pp. 9-18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[70] Hitesh Chopra et al., “Revolutionizing Clinical Trials: The Role of AI in Accelerating Medical Breakthroughs,” International Journal of Surgery, vol. 109, no. 12, pp. 4211-4220, 2023.
[CrossRef] [Google Scholar] [Publisher Link]