2026-10-10T20:05:45Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33769835 2026-09-15T02:04:46Z category_1 category_4 category_15 category_19 category_21 category_873 category_734 category_931 portal_63 item_type_3 month_year_09_2026

AI Accelerated Chemical Screening Integrates ChemBERTa to Identify Repositionable CASP4 Inhibitors via MD Simulations and MM/PBSA Analysis Mubashir Hassan (614591) Sidra Ghayour Bhatti (24910598) Muhammad Yasir (3555896) Wanjoo Chun (6752006) Andrzej Kloczkowski (612861) Biophysics Biochemistry Neuroscience Pharmacology Biotechnology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified roc – auc reference compound donepezil key physicochemical descriptors derived chemberta embeddings promising casp4 inhibitors potential casp4 inhibitors targeted alzheimer ’ 50 </ sub molecular dynamics simulations active compounds among integrated cheminformatics modeling cheminformatics modeling alzheimer ’ active compounds md simulations integrated ligand molecular docking inactive compounds ∼ 95 ∼ 3 − 20 workflow provides study employed score prioritization robust strategy ranked hits ranked candidates predicted potency pbsa free pbsa analysis leveraging docking fold enrichment experimental pic exceeded even energy analysis drugbank database 9 kcal This study employed an integrated ligand-based virtual screening pipeline to identify potential CASP4 inhibitors from the DrugBank database, leveraging docking-score prioritization, SMILES-derived ChemBERTa embeddings, and key physicochemical descriptors. Building on this foundation, the workflow incorporated virtual screening, cheminformatics modeling, PK–PD evaluation, molecular docking, molecular dynamics simulations, and MM/PBSA free-energy analysis to systematically prioritize repurposed DrugBank compounds for CASP4-targeted Alzheimer’s disease therapy. A Random Forest classifier trained on the hybrid ChemBERTa physicochemical feature set distinguished active from inactive compounds with ∼95% accuracy (ROC–AUC = 0.73) and achieved a ∼3.5-fold enrichment of active compounds among the top-ranked hits, while a companion Random Forest regressor, trained on the experimental pIC₅₀ values of active compounds, ranked candidates by predicted potency. In addition, integrated cheminformatics modeling, PK–PD analysis, and molecular docking further narrowed the selection to the top five candidate compounds: DB00519, DB01068, DB06202, DB08882, and DB05316. Finally, MD simulations and MM/PBSA calculations established clear thermodynamic support for DB05316 and DB00519, whose binding free energies (−21.4 and −20.9 kcal/mol, respectively) exceeded even the reference compound donepezil, highlighting them as the most promising CASP4 inhibitors. Overall, this workflow provides an efficient and robust strategy for prioritizing repositioned DrugBank compounds as potential CASP4 inhibitors against Alzheimer’s disease. 2026-09-14T00:00:00Z Dataset Dataset 10.1021/acs.jcim.6c02284.s002 https://figshare.com/articles/dataset/AI_Accelerated_Chemical_Screening_Integrates_ChemBERTa_to_Identify_Repositionable_CASP4_Inhibitors_via_MD_Simulations_and_MM_PBSA_Analysis/33769835 CC BY-NC 4.0