Journal of Molecular and Pharmaceutical Sciences
Volume 4, Numéro 2, Pages 71-83
2025-12-25

Deep Neural Networks For Predicting The Anti-cancer Activity Of Beta-blockers



Authors : Boufenara Mohamed Nadjib . Bahi Meriem . Bechlem Houria .

Abstract

Drug repurposing has emerged as a powerful strategy to accelerate anticancer drug discovery while reducing costs and development timelines. Beta-adrenergic receptor antagonists (beta-blockers), widely prescribed for cardiovascular diseases, have demonstrated increasing evidence of anticancer properties through modulation of proliferation, angiogenesis, immune response, and metastatic signaling. In this study, we integrate deep learning–based virtual screening with molecular docking to evaluate FDA-approved beta-blockers for potential repositioning against cancer-related protein targets. A deep neural network (DNN) classification model was trained using curated bioactivity datasets to predict anticancer likelihood, followed by docking simulations to validate structural interactions. Among 108 beta-blockers and derivatives screened, the DNN model identified propranolol, carvedilol, indenolol, idropranolol, and a derivative of nifenalol as top anticancer candidates. Docking results further supported these predictions, showing high binding affinities and favorable molecular interaction profiles, particularly with the androgen receptor (PDB ID: 5T8E). Our findings demonstrate the utility of deep learning coupled with molecular docking for effective drug repurposing and highlight promising beta-blocker candidates for future preclinical evaluation.

Keywords

Drug Repositioning, Deep Learning, Deep Neural Network, Beta-Blockers, Virtual Screening, Molecular Docking.

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