Loading
Histone Deacetylase Inhibitors Research / Protein Degradation and Inhibitors · Journal article
Biology · September 8, 2026
Raises a question worth testing. It does not answer one.
This study reports an AI-driven virtual screening workflow that prioritizes FDA-approved drugs as potential HDAC3 inhibitor candidates by identifying molecular descriptor patterns. The workflow recovered known HDAC inhibitors and predicted previously unreported candidates such as tyrosine kinase inhibitors, but remains at the hypothesis-generation stage with no experimental or clinical validation.
Computational machine learning screening study. FDA-approved drug compounds; no human, animal, or cell-based subjects.. Intervention: Machine learning–based virtual screening for HDAC3 inhibitory activity.
Machine learning model screened 1615 FDA-approved compounds and yielded 120 candidates with predicted HDAC3 inhibitory activity Workflow recovered known HDAC inhibitors: romidepsin, vorinostat, and panobinostat Tyrosine kinase inhibitors imatinib and osimertinib were identified as novel candidates, suggesting structural overlap between kinase and HDAC3 pharmacophores
Tyrosine kinase inhibitors imatinib and osimertinib were identified as novel candidates, suggesting structural overlap between kinase and HDAC3 pharmacophores
This computational tool may accelerate identification of drug candidates for further laboratory and preclinical testing, but clinicians and researchers should regard all predicted inhibitors as hypothetical until validated experimentally or in cell-based assays.
This is an exploratory computational study using machine learning to predict HDAC3 inhibition by FDA-approved drugs, without experimental validation or clinical data, raising mechanistic questions rather than answering therapeutic ones.
As stated by the source record.
Quoted from the source exactly as published.
This computational tool may accelerate identification of drug candidates for further laboratory and preclinical testing, but clinicians and researchers should regard all predicted inhibitors as hypothetical until validated experimentally or in cell-based assays.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.