Machine Learning in Drug Discovery: A Review

作者:Suresh Dara, Swetha Dhamercherla, Surender Singh Jadav, CH Madhu Babu, Mohamed Jawed Ahsan

摘要

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

论文关键词:Artificial intelligence, Drug discovery, Machine learning, Target validation, Prognostic biomarkers, Digital pathology

论文评审过程:

论文官网地址:https://doi.org/10.1007/s10462-021-10058-4