Scrutinization on Docking Against Individually Generated Target Pockets for Each Ligand

The longstanding challenge of limited exploration in protein receptor conformational space continues to constrain the precision of molecular docking. Ensemble docking, which employs methods such as molecular dynamics simulations to generate multiple receptor conformations for docking, has improved accuracy but remains limited by incomplete sampling and an inability to fully account for ligand-induced fit. To address these limitations, we introduce the concept of individual docking, a novel approach that involves docking against receptor conformations generated individually for each ligand in the docking library. This approach has only very recently become feasible due to advances in protein structure prediction, in particular end-to-end protein-ligand complex prediction technologies. In this study, we performed individual docking on 27 targets from the DUD-E dataset, using a two-step protocol that integrates NeuralPLexer or AlphaFold3 for receptor conformation sampling, followed by physics-based docking. Our results reveal that individual docking with AlphaFold3 predictions yields more than a twofold improvement in enrichment factors compared to standard docking. Furthermore, individual docking recovers distinct sets of active ligands, thereby expanding the diversity of virtual screening hits. Detailed analyses of pocket and ligand conformations suggest several potential incompatibilities between deep learning-based and physics-based virtual screening tools.