Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach involved analyzing key interactions for FYN inhibition using established multi-kinase inhibitors such as Staurosporine, Dasatinib, and Saracatinib. We utilized ECFP4 circular fingerprints and the t-SNE machine learning algorithm to compare molecular similarities between FDA-approved drugs and known clinical trial inhibitors.

Insightful t-SNE guided exploration spotlighting Palbociclib and Ribociclib analogues as novel WEE1 kinase inhibitory candidates.

In the era of targeted therapeutics, protein kinases like WEE1 have become pivotal drug targets, especially for cancer therapy. Utilizing a multi-faceted approach, our study adds fresh insights to this endeavour. We employed the t-SNE algorithm, combined with ECFP4 fingerprints, to analyse the molecular similarity between FDA-approved drugs and known clinical trial inhibitors.