ChemBERTa-Guided Virtual Screening Identifies Repurposable CASP4 Inhibitors for Alzheimer's Disease
- Ligand-based virtual screening pipeline mined DrugBank for CASP4 inhibitor candidates, combining docking-score prioritization with SMILES-derived ChemBERTa embeddings and physicochemical descriptors.
- A Random Forest classifier trained on the hybrid ChemBERTa/physicochemical feature set separated active from inactive compounds with high accuracy and enriched actives among top-ranked hits.
- A companion Random Forest regressor, trained on experimental pIC50 values, ranked candidates by predicted potency.
- Cheminformatics filtering, PK-PD evaluation, and molecular docking narrowed the field to five DrugBank-listed candidates.
- Molecular dynamics simulations and MM/PBSA free-energy analysis confirmed stable binding poses for the top candidates.
- Positions the workflow as a drug-repositioning route toward CASP4-targeted Alzheimer's disease therapy, a caspase linked to neuroinflammation and pyroptosis.
What actually sells this to me is the DrugBank sourcing, not the screening funnel itself: the five candidates that survive docking, ChemBERTa/RF classification, and MM/PBSA already come with known ADMET and need no de novo synthesis to reach an animal model — that's a tractability shortcut a genuine hit-finding campaign rarely gets. CASP4's pocket is the usual caspase catalytic Cys-His dyad, so the real question the MD trajectories should answer is whether these scaffolds actually engage that dyad or just sit in a peripheral exosite; binding energy alone won't tell me that. Selectivity is the open flag — caspase-1/3/4 share fold homology, and off-target caspase hits in a neuroinflammation context could cut the wrong way. Methodologically, ChemBERTa embeddings layered onto classical descriptors for RF triage is a sane upgrade over pure docking-score ranking, though I'd want held-out chemotype validation before trusting the pIC50 regressor outside its training distribution.