Polaris Challenge: Data-Driven Priors to Improve Docking for Pose Prediction

Accurate prediction of ligand binding poses is a central challenge in structure-based drug design. In this work, we developed a workflow for the ASAP-Polaris-OpenADMET antiviral competition, leveraging crystallographic training structures to predict time-split test ligand poses. Our approach relied on the open-source Vina-GPU software for ensemble docking, augmented with fragment-derived priors for pose generation and scoring. When fragment information was unavailable, we fell back to MM/GBSA for pose scoring. Overall, our method performed competitively and achieved over 50% success in predicting ligand poses within 2 Å RMSD for both SARS-CoV-2 and MERS-CoV protease targets. Both fragment-informed rescoring of Vina-generated poses and fragment-constrained pose generation improved accuracy, underscoring the utility of crystallographic fragments as priors. Importantly, this information transferred successfully from SARS-CoV-2 to MERS-CoV, highlighting the value of conserved binding pockets in guiding pose prediction across related viral proteases. Our results demonstrate that docking-based workflows, when augmented with transferable fragment knowledge and physics-based refinements, can achieve competitive accuracy for pose prediction within protein families while remaining efficient and fully open-source.