Identification of Potential Nontoxic Human BTK Inhibitors through an Integrated Deep Learning and Structure-Based Drug Repositioning Strategy.

Bruton's tyrosine kinase (BTK) has been a key player in the pathogenesis of multiple autoimmune diseases as its overexpression drives the hyperactivation of the B-cell signaling pathway. While the BTK inhibitors, including ibrutinib, have shown significant inhibitory potential, their low potency and higher toxicity emphasize the need for safer and more effective alternatives. This study develops an in silico pipeline involving deep learning, structure-based drug repositioning, and toxicity analysis to identify potential BTK inhibitors. A curated dataset of BTK-targeting bioactive compounds was rigorously filtered, and the resulting high-quality compounds were used to train and test an artificial neural network (ANN) model. The trained model was then applied to assess the bioactivity of an FDA-approved drug library. The putative compounds were further screened using molecular docking, providing three compounds, including gozetotide, micafungin, and candicidin, as the top hits. Molecular simulations further validated the atomic level stability of these compounds through various post-trajectory analyses, including RMSD, RMSF, RoG, hydrogen bonding, PCA, FEL, and DCCM, suggesting their stable binding profiles within the BTK active site. Finally, the GNN-based toxicity analysis revealed that the suggested compounds did not exhibit any significant toxicity concern, supporting their safety as potential therapeutic agents. These findings contribute to the advancement of safer and more effective treatments for autoimmune diseases and require further clinical trials of gozetotide, micafungin, and candicidin as BTK-targeted therapies.