casp4-inhibitor-screening

AI-Accelerated CASP4 Inhibitor Screening

An AI-based virtual screening pipeline that identified repositionable DrugBank compounds as candidate CASP4 (Caspase-4) inhibitors for Alzheimer's disease, combining ChemBERTa-based ML ranking, molecular docking, MD simulation, and MM/PBSA free-energy analysis.

Manuscript status: accepted; citation to be added upon publication. Code: github.com/mubashirhassangcul/Caspase4-inhibitor-screening Data: SidraBhatti/caspase4-inhibitor-screening-data

The full 5-stage pipeline: (1) data curation & preprocessing, (2) molecular structure retrieval & descriptor calculation (ChemBERTa + RDKit), (3) machine learning — Random Forest classification and regression, (4) molecular docking & binding-site analysis (AutoDock Vina, PrankWeb), (5) MD simulation & MM/PBSA analysis (GROMACS).

Role & Attribution

Sidra Bhatti led the AI-based prediction pipeline: ChemBERTa embedding generation, hybrid feature engineering, and development of the Random Forest classifier and regressor used to prioritize candidates. Molecular docking, MD simulations, and MM/PBSA analysis were performed by collaborators (see the GitHub repository for the full author list).

Pipeline

DrugBank Drug-Lib compounds
       ↓
Structure-based virtual screening (VSTH/Tianhe-2 vs. CASP4, PDB 6NRY) — ~1,739 initial hits
       ↓
ChEMBL (fetch experimental pIC50 labels; active if pIC50 ≥ 8.2)
       ↓
Feature extraction: ChemBERTa SMILES embeddings (768-dim) + RDKit physicochemical descriptors (6)
       ↓
Random Forest Classifier (active vs. inactive; 500 trees, 774-dim hybrid features)
       ↓
Random Forest Regressor (pIC50 prediction on actives)
       ↓
Top-10 candidate selection → ADMET/PK-PD filtering
       ↓
Binding-site ID (PrankWeb) + AutoDock Vina docking vs. CASP4
       ↓
Top-5 candidates + donepezil reference → 100-ns MD in triplicate (GROMACS, CHARMM36)
       ↓
MM/PBSA free-energy ranking → benchmarking vs. donepezil

Method

Feature engineering. Each screened compound's SMILES string is encoded with a pretrained ChemBERTa transformer (768-dim embedding), concatenated with 6 RDKit physicochemical descriptors, giving a 774-dim hybrid feature vector per compound.

Random Forest classifier. 500 trees, trained on the 774-dim hybrid features to distinguish active vs. inactive compounds (active defined as ChEMBL pIC50 ≥ 8.2).

Random Forest regressor. Trained on the experimental pIC50 values of active compounds only, used to rank candidates by predicted potency.

Results

AI-Ranked Shortlist

Top-10 compounds by RF classifier/regressor score, before ADMET/PK-PD filtering:

Final Candidates (after ADMET/PK-PD filtering, went to docking)

This set differs from the raw AI-ranked shortlist above — 7 compounds overlap, 3 were filtered out and replaced after ADMET/PK-PD screening:

Structures:

<table> <tr> <td align="center"><img src="structures/DB00439_Cerivastatin.png" width="160"><br>_{DB00439 Cerivastatin}</td> <td align="center"><img src="structures/DB00519_Trandolapril.png" width="160"><br>_{DB00519 Trandolapril}</td> <td align="center"><img src="structures/DB01068_Clonazepam.png" width="160"><br>_{DB01068 Clonazepam}</td> <td align="center"><img src="structures/DB01544_Flunitrazepam.png" width="160"><br>_{DB01544 Flunitrazepam}</td> <td align="center"><img src="structures/DB05316_Pimavanserin.png" width="160"><br>_{DB05316 Pimavanserin}</td> </tr> <tr> <td align="center"><img src="structures/DB06202_Lasofoxifene.png" width="160"><br>_{DB06202 Lasofoxifene}</td> <td align="center"><img src="structures/DB06203_Alogliptin.png" width="160"><br>_{DB06203 Alogliptin}</td> <td align="center"><img src="structures/DB08882_Linagliptin.png" width="160"><br>_{DB08882 Linagliptin}</td> <td align="center"><img src="structures/DB08897_Aclidinium.png" width="160"><br>_{DB08897 Aclidinium}</td> <td align="center"><img src="structures/DB09477_Enalaprilat.png" width="160"><br>_{DB09477 Enalaprilat}</td> </tr> </table>

MD / MM-PBSA Validation

The top 5 candidates (DB00519, DB01068, DB06202, DB08882, DB05316) plus the donepezil reference underwent 100-ns MD simulation in triplicate (GROMACS/CHARMM36):

Backbone RMSD — structural stability over the trajectory.

Per-residue RMSF — local flexibility.

Radius of gyration — overall compactness.

Solvent-accessible surface area.

Protein-ligand center-of-mass distance.

Total system energy.

Binding free energies: DB05316 (−21.4 kcal/mol) and DB00519 (−20.9 kcal/mol) exceeded even the reference compound donepezil, identifying them as the most promising CASP4 inhibitors.

Data availability

DrugBank raw data (drugbank.xml) and structure/SMILES columns are excluded per DrugBank's terms of use — see the companion Dataset repo for the processed screening/feature tables that are safe to share.

Citation

Manuscript accepted; BibTeX will be added here once publication details are final.