PharmRF: A machine‐learning scoring function to identify the best protein‐ligand complexes for structure‐based pharmacophore screening with high enrichments

• Published in: Journal of computational chemistry 2022-05, Vol.43 (12), p.847-863
• Main Authors: Kumar, Sivakumar Prasanth, Dixit, Nandan Y., Patel, Chirag N., Rawal, Rakesh M., Pandya, Himanshu A.
• Format: Article
• Language: English
• Subjects: Affinity; Binding; Coordination compounds; Crystal structure; Datasets; docking; Enrichment; enrichment rate; Ligands; machine learning; Pharmacology; pharmacophore; Proteins; scoring function; Scoring models; Screening; Success
• ISSN: 0192-8651; 1096-987X
• DOI: 10.1002/jcc.26840

Structure‐based pharmacophore models are often developed by selecting a single protein‐ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore‐based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore‐based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein‐ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high‐affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure‐based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein‐ligand systems of the DUD‐E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF‐2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein‐ligand systems of LIT‐PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF. A machine‐learning scoring function to identify protein‐ligand complexes with desirable pharmacophoric elements with the potential to secure high active enrichments in database screening of small molecules.