Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that 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 and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

As the drug safety and efficacy is hampered in the presence of an impurity, the international regulatory agencies laid down stringent limits for the control of impurities in the active pharmaceutical ingredient and pharmaceutical formulations. The conventional approaches lack the characterization of impurities in trace levels, due to sensitivity issues, hyphenated techniques are preferred. Among the modern hyphenated techniques, liquid chromatography-mass spectrometry (LC-MS) has high sensitivity and can analyze large number of organic compounds in a short period of time. In the present study, the impurity profiling of various drug substances and products using LC-MS about past 6 years were retrospect for its importance, instrumentations, and applications.