BiosimDock

We outperform other models on accuracy of binding affinity and binding pose prediction Docking and virtual screening tools are meaningful only if they are able to make fast, accurate, and useful predictions. A good model can filter true binders from a broad pool of potential molecules, including false positives that may be similar in chemical properties. In the hit identification stage of drug discovery it can give drug hunting teams a chemically-diverse set of potential hit molecules to evaluate with experimental assays or further computational analysis, helping them narrow the path to a lead candidate. In contrast, a bad model returns many false positives, costing a team money and effort – and potentially leading drug hunters off-course for months or years. To benchmark, we tested the BiosimDock model on the PDBbind core dataset and the DEKOIS 2.0 dataset.1, 2, 3, 4 The PDBbind core dataset contains 285 experimental structures of protein-ligand bound complexes across different protein classes. It remains a standard due to widespread use in benchmarking, facilitating comparison between models. It also enables accuracy prediction based on binding poses. DEKOIS 2.0 (Demanding Evaluation Kits for Objective In silico Screening) is an extensively-curated dataset of 81 targets across protein classes, including proteases, kinases, transferases, oxido-reductases, nuclear receptors, and hydrolases. Each target has an accompanying library of true binders and decoys, which have similar physical and chemical properties but do not interact with the target protein. This enables rigorous benchmarking of models for enrichment of true binders over false positives.4, 5, 6