Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in thepharmaceutical industry.

Abstract

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discoveryresearch, requires molecular representation. Previous reports have demonstratedthat machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates andcan be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses therecent updates in AI tools as cheminformatics application in medicinal chemistry for thedata-driven decision making of drug discoveryand challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.