Machine Learning for Drug Discovery and Manufacturing

Abstract

Drug discovery and manufacturing are adequately time-consuming, complicated, and costly processes that depend on several parameters. Machine learning (ML) is becoming increasingly popular in drug discovery and manufacturing by yielding promising outcomes. ML techniques offer a collection of tools for improving the drug discovery and decision-making processes, with the utilization of large amounts of high-quality pharmaceutical data in a variety of applications such as de novo drug designs, hit discoveries, and QSAR analysis, to obtain reliable results. ML can be used at any step of the drug discovery and manufacturing process. Identification of predictive biomarkers, validation of drug targets, and examination of digital pathology information in clinical trials are just a few examples. The context and methodology of the ML applications are varied, with some generating precise forecasts and insights. As ML techniques are increasingly used, so do their limitations become more apparent. Such constraints include the necessity for big data, data scarcity, and the inability to evaluate and repeat the ML results. It's also becoming clear that the ML procedures aren't completely self-contained, thus necessitating the retraining of pharmacological data, even after the deployment of the ML results. There is still a huge demand to generate systematic and comprehensive high-dimensional pharmacological data in all sectors. Some factors for increasing the ML results include prognostic biomarkers, target validation, and digital pathology. The ML challenges must address the major cause of insufficiency in interpretability outcomes, which restrict their applications in drug discovery and manufacturing. To solve several challenges in validating ML algorithms and improving decision-making, clinical trials require absolute and methodological data. The use of ML can enhance data-dependent assessment making and accelerate drug discovery and manufacturing processes while lowering failure rates. This book chapter summarizes the recent literature on ML tools/methods used in drug discovery and manufacturing, which are used at each phase of drug development to speed up research and reduce risk and cost in clinical trials. The advanced innovative ML techniques to overcome some of these obstacles and their potential application in drug discovery are described with examples derived from drug discovery and related fields. The ML techniques discussed herein are expected to increase the ML roles in drug discovery and manufacturing processes to a new level with the aid of advanced computer intelligence.