SCORCH2: a generalised heterogeneous consensus model for high-enrichment interaction-based virtual screening
Author(s)
Chen, Lin
Blay, Vincent
Ballester, Pedro
Houston, Douglas R
Type
Journal Article
Abstract
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 occur, 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 simultaneously enhance the performance and interpretability of virtual screening by leveraging interaction features. Comparing 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. Furthermore, SCORCH2 obviates the need for meticulous docking pose selection, streamlining the screening process. These advances highlight the potential of SCORCH2 as a valuable tool in accelerating drug discovery campaigns.
Date Issued
2025-11-13
Date Acceptance
2025-08-08
Citation
Advanced Science, 2025, 12 (42)
ISSN
2198-3844
Publisher
Wiley
Journal / Book Title
Advanced Science
Volume
12
Issue
42
Copyright Statement
© 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Publication Status
Published
Article Number
e08318
Date Publish Online
2025-08-20