HelixVS: Deep Learning-Enhanced Structure-Based Platform for Screening and Design

Abstract

Drug discovery through virtual screening (VS) has become a popular strategy for identifying hits against protein targets. Alongside VS, molecular design further expands accessible chemical space. Together, these approaches have the potential to reduce the cost and time needed for manual selection and wet-laboratory experiments, thereby accelerating drug discovery pipelines. 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.In addition, the HelixVS platform has been extended with HelixVS-Syn, which enables design of novel compounds from reference scaffolds. These designed molecules are seamlessly integrated into the HelixVS screening workflow, allowing researchers to explore both existing chemical libraries and novel chemical space with high affinity, synthetic accessibility, and structural novelty.