Systematic assessment of structure-promiscuity relationships between different types of kinase inhibitors

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

Applications of promiscuity of FDA-approved kinase inhibitors in drug repositioning and toxicity

Promiscuity of therapeutics has important implications in treatment and toxicity. So far, a comprehensive understanding of promiscuity related to kinase inhibitors is lacking and such an analysis may offer potential opportunities for drug repurposing. In the present study, profiling of inhibitor-specific kinases based on the available biochemical IC50s was performed, fold-change of IC50 values for additional targets were calculated by taking the primary target as the reference kinase, and finally the promiscuity degree (PD) for FDA-approved kinase inhibitors was calculated. Surprisingly, class II inhibitors showed more PD than that of the class I inhibitors. We further identified cancer types and sub-types in which additional kinase targets or off-targets of inhibitors were overexpressed for potential drug repurposing. In addition, the expression of these kinases in normal human tissues were also profiled to predict toxicity following drug repositioning. Taken together, the study offers opportunities for cancer treatment in a kinase-specific manner.