DrugBank mining with machine learning reveals novel candidates for BCL-2 inhibition.

Apoptosis, also known as programmed cell death, is a fundamental biological process essential for development and cellular homeostasis. An imbalance in the levels of pro- and antiapoptotic proteins of the BCL-2 family can inhibit apoptosis and contribute to tumor formation. Although small-molecule inhibitors targeting BCL-2 proteins have been developed, their clinical efficacy remains limited, highlighting the need for new approaches to discover effective inhibitors. In this study, we used a machine learning-based approach to identify potential BCL-2 inhibitors. The activity data for BCL-2 ligands were curated from the ChEMBL database and used to train and evaluate multiple classification models. Of the seven algorithms tested, the LightGBM model performed the best and was used to predict novel BCL-2 inhibitors in the DrugBank database. This strategy identified two candidate compounds, Opelconazole and Zongertinib, from the curated DrugBank All dataset, which covered all categories. These results demonstrate the potential of machine learning-based drug repositioning for the discovery of effective BCL-2 inhibitors, which could contribute to the development of targeted antiapoptotic therapeutics.