ReqVS: Reliable Virtual Screening via Interaction Entropy

Sequence-based virtual screening has emerged as a scalable approach for drug discovery by enabling compound prioritization directly from protein sequences without requiring 3D structures. However, its practical application remains limited by unreliable predictions on new targets and unexplored chemical space, largely due to the uncertainty introduced when inferring structure-dependent protein–ligand interactions from sequence information alone. Here, interaction uncertainty is quantified using an entropy-based estimator, while an ensemble mechanism integrates complementary information from heterogeneous sequence-based interaction predictors. Based on this, ReqVS is developed as a reliability-aware sequence-based virtual screening framework that prioritizes compounds maximizing predicted interaction scores while minimizing the uncertainty to produce robust compound rankings. Benchmark evaluations further show that ReqVS achieves performance comparable to structure-based methods while improving ranking robustness across targets. When applied to the challenging GPCR target HCAR1, ReqVS identified a high-affinity antagonist, representing the first experimentally validated HCAR1 antagonist discovered using sequence-based virtual screening. These results highlight reliability-aware sequence-based screening as a practical paradigm for structure-independent drug discovery.