HelixVS: Deep Learning Enhanced Structure-based Virtual Screening Platform for Hit Discovery

Abstract: Drug discovery through virtual screening (VS) has become a popular strategy for identifying hits against protein targets. VS has the potential to reduce the cost and time needed for manual selection and wet-laboratory experiments. Improving the cost-effectiveness of virtual screening is a significant challenge, aiming to explore larger compound libraries while maintaining lower screening costs. Here, we present HelixVS, a structure-based VS platform enhanced by deep learning models. HelixVS integrates a precise deep learning-based pose-scoring model and a pose-screening module into a multi-stage VS process, enabling more effective screening of active compounds. Compared to classic molecular docking tools like Vina, HelixVS demonstrated significantly improved screening performance across nearly a hundred targets, achieving an average 2.6-fold higher enrichment factor (EF) and more than 10 times faster screening speed. We applied HelixVS in four drug development pipelines, targeting both traditional competitive drug-binding pockets and novel protein-protein interaction interfaces. Wet-lab validations across these pipelines consistently identified active compounds, with over 10% of the molecules tested in wet labs demonstrating activity at uM or even nM levels. This demonstrates the ability of HelixVS to identify high-affinity ligands for various targets and pockets. Furthermore, we provide a publicly available and free version of HelixVS with limited computing power to assist drug development scientists in accelerating their drug discovery processes.