Applications of AI in Computer-Aided Drug Discovery

Applications of AI in Computer-Aided Drug Discovery

Reet Kaur Kohli, Seneha Santoshi, Sunishtha S. Yadav, Vandana Chauhan
DOI: 10.4018/978-1-6684-5255-4.ch005
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Abstract

Drug discovery is the process in which healthcare is approached through identification of potential new therapeutic agents. CADD provides solutions at every stage of drug discovery including the leading challenges of cost and time. CADD has provided an effective solution to this challenge. AI has enabled multiple aspects of drug discovery, including the analysis of high content screening data and the design and synthesis of new molecules. The use of transparent methodologies like AI is crucial, particularly in drug repositioning/repurposing in rare diseases. An abundant variety of methods, in particular the concepts of deep learning, have been used for protein modelling and ligand-based drug discovery along with artificial neural networks for QSAR modelling. Structure-based ligand identification via AI modelling is also explored. AI presents the scientific community and the biopharma industry and its established processes for discovering and developing new medicines with new challenges.
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Introduction

Computer Aided Drug Discovery and design is an arena of Computational Biology that is being explored more and more with major improvements being made in the past decade. The amalgamations of AI and ML with existing CADD technologies has led to the numerous successful outcomes of CADD. Before delving into the concepts of CADD and its applications with AI, it is important to gain an understanding of what AI and ML are as follows.

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