ArticleProtein science : a publication of the Protein Society2024
FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.Current medicinal chemistry · 2025Review
- Introduction to Respiratory Syncytial Virus.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening.The journal of physical chemistry. B · 2024Article
- FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.Protein science : a publication of the Protein Society · 2024Article
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Abstract
Protein function annotation and drug discovery often involve finding small molecule binders. In the early stages of drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing. While our recent ligand homology modeling (LHM)-machine learning VLS method FRAGSITE outperformed approaches that combined traditional docking to generate protein-ligand poses and deep learning scoring functions to rank ligands, a more robust approach that could identify a more diverse set of binding ligands is needed. Here, we describe FRAGSITE2 that shows significant improvement on protein targets lacking known small molecule binders and no confident LHM identified template ligands when benchmarked on two commonly used VLS datasets: For both the DUD-E set and DEKOIS2.0 set and ligands having a Tanimoto coefficient (TC) < 0.7 to the template ligands, the 1% enrichment factor (EF
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