Artificial Intelligence and Machine Learning in Drug Design and Development

6Artificial Intelligence-Powered Molecular Docking: A Promising Tool for Rational Drug Design

Nabajit Kumar Borah, Yukti Tripathi, Aastha Tanwar, Deeksha Tiwari, Aditi Sinha, Shailja Sharma, Neetu Jabalia, Ruchi Jakhmola Mani, Seneha Santoshi and Hina Bansal*

Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India

Abstract

Molecular docking is a vital computational method for predicting how small molecules bind to target proteins, aiding drug discovery. It involves screening vast small molecule databases to identify potential drug candidates, relying on scoring functions to rank them. This interaction between proteins and ligands is the cornerstone of drug design. Integrating artificial intelligence (AI) algorithms has revolutionized this field, boosting efficiency and accuracy. This chapter explores various docking techniques, with a focus on AI-based methods. Neural networks, reinforcement learning, and evolutionary algorithms play pivotal roles, enhancing prediction accuracy and speed by utilizing deep learning models trained on extensive protein– ligand datasets. This integration has the potential to expedite drug discovery. Recognizing that AI is not a standalone solution, the chapter emphasizes the need for integration with other methodologies to achieve comprehensive drug discovery. It also addresses the challenges and limitations of AI in molecular docking, pointing toward future research directions. ...