An AI-Assisted Workflow for Rapid Prioritization of FDA-Approved Drugs as HDAC3 Inhibitor Candidates for Drug Repurposing.

Epigenetic regulation through histone acetylation plays a critical role in gene expression and cancer progression. Because of its pivotal role in chromatin remodeling, Histone deacetylase 3 (HDAC3) has become a promising therapeutic target. In this study, an artificial intelligence (AI)-driven strategy was utilized to prioritize potential HDAC3 inhibitors among FDA-approved compounds to accelerate drug repurposing for cancer therapy. Existing HDAC3 inhibitors were identified in the BindingDB and were used to develop a machine learning (ML) model trained on the most potent inhibitors to identify molecular descriptor patterns associated with HDAC3 inhibition. The ML workflow then screened 1615 FDA-approved compounds, yielding 120 candidates with predicted HDAC3 inhibitory activity. Among these, known HDAC inhibitors, including romidepsin, vorinostat, and panobinostat, were selected, suggesting that the workflow can recover known HDAC inhibitors during virtual screening. Interestingly, tyrosine kinase inhibitors such as imatinib and osimertinib were also identified, indicating potential structural overlap between kinase- and HDAC3-binding pharmacophores. The analysis of the predicted docking scores also supported the prioritization results since the top 10 compounds had more negative predicted docking scores than the bottom 10 (p = 0.0074). This shows that the suggested workflow is useful for prioritizing FDA-approved compounds as potential HDAC3 inhibitors for further study.