VsNsbench: evaluating AlphaFold3-embed induced-fit mechanism for enhanced virtual screening

While AlphaFold3 (AF3) extends AlphaFold2 (AF2) by predicting holo structures, it remains unclear whether its modeling process captures similar induced-fit mechanisms. In this study, we benchmarked the VS performance of ligand-induced AF3 holo structures on two datasets: a subset of DUD-E and VsNsBench designed to avoid sequence-level information leakage. On both datasets, AF3 holo structures demonstrated substantially improved enriching capability compared to AF3 apo , experimental apo , and AF2 structures. Compared to experimental holo structures, AF3 models demonstrated inferior performance on the DUD-E subset but performed slightly better on VsNsBench. Further analysis revealed that AF3’s induced modeling critically depends on the bound ligand’s affinity: high-affinity ligands produced conformations enabling excellent enrichment, while low-affinity or random ligands yielded poor performance. Moreover, direct VS using AF3 alone achieved satisfactory performance, but computational efficiency remains a major bottleneck for large-scale applications, even with single-round multiple sequence alignment (MSA) generation. In a DFG-motif kinase case study, AF3 successfully modeled inhibitor-specific conformations with a 75% success rate. These findings demonstrate that AF3 effectively incorporates induced-fit modeling, though improvement is needed, particularly for modeling multi-state conformational ensembles.