Machine Learning Approaches to Rational Drug Design
Pharmaceutical industries are multibillionaire setups with a diligent team of scientists, researchers, technical manpower, and investors. A major concern of such industries is to always curtail the time and cost factor associated with them. Bioinformatics involving machine learning (ML) methods have come to the forefront to address this problem. The predictive and statistical efficacy of ML methodologies has even proven to propose better leads than a wet lab pipeline. This chapter aims to give a brief overview of underlying principles of mainly GAs and ANNs as popular ML algorithms and deeper insight into their robust applications in the field of modern day drug design. It also attempts to share the future prospects of such ML techniques and their limitations with possible solutions hereafter.
By Salman Akhtar, M. Kalim A. Khan, Khwaja Osama