SCORCH2: a generalised heterogeneous consensus model for high-enrichment interaction-based virtual screening
The discovery of effective therapeutics remains a complex, costly, and time-consuming endeavor, characterized by high failure rates and significant resource investments. A central bottleneck in early-stage drug discovery is identifying suitable hit compounds with moderate affinity for known biological targets. Although advancements have been made, current in silico virtual screening methods are subject to limitations, including model overfitting, data bias, and constrained interpretability in their predictive processes. In this study, we present SCORCH2, a machine learning-based framework designed to enhance both the performance and interpretability of virtual screening by leveraging interaction features. Compared with its predecessor SCORCH, SCORCH2 exhibits superior predictive accuracy and generalizability across a wide range of biological targets. Importantly, SCORCH2 demonstrates robust hit identification capabilities on previously unseen targets, indicating strong transferability. These results highlight the potential of SCORCH2 as a valuable tool in accelerating drug discovery, offering reliable predictive capabilities while improving the interpretability of virtual screening models.