MACHINE LEARNING APPROACHES IN IN-SILICO DRUG DESIGN AND DEVELOPMENT: A COMPREHENSIVE REVIEW
S. M. Mahagama1 and N. T. Jayatilake2*
1Institute of Technology University of Moratuwa, Sri Lanka, 2Horizon Campus, Sri Lanka
Session: Technical Session D
Abstract
Machine Learning (ML) is premised on the idea that machines can learn from data, recognize patterns, and make optimum decisions. Machine learning approaches include various algorithms for interpreting and gaining knowledge from data. Recently such ML-based techniques have been applied in drug development, bioinformatics, cheminformatics, and other areas of medicine as well. Drug design involves creating small molecules that are favorable in shape and charge to the biomolecular target to which they bind. Since experimental and lab procedures are limited in throughput, accuracy, and cost, they are unsuitable for broad deployment. Therefore, the development of in-silico target-drug designing methods has gained increasing attention globally due to the advantages of speed and low cost. In silico techniques in pharmaceutical designing are a type of computerized simulation that employs computer-aided technologies which initialize with an understanding of precise biochemical reactions within the body forming combinations of them to meet a therapy profile. Computerized methods provide the benefit of producing novel candidates for drugs faster and at lower prices. Virtual screening and de novo design, in silico ADME/T prediction, and improved methods for assessing protein-ligand interaction and structured-based drug design are the major functions of computational drug development. In-silico drug design refers to the use of computational methods and simulations to design and optimize drug candidates. This process involves steps such as Target Identification and Validation, Structure-Based Drug Design, Ligand-Based Drug Design, Virtual Screening, Molecular Dynamics Simulations, ADMET Prediction and Optimization. The adoption of ML algorithms in the search of medicines is applicable throughout this entire process. In this review article, the machine learning applications employed in In-silico drug design and discovery are explored in detail.
Keywords: computer-assisted drug design, drug discovery, in-silico drug design, machine learning
DOI: 10.64752/TJEC5360