[2406.01825] Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching

Computer Science > Machine Learning arXiv:2406.01825 (cs) [Submitted on 3 Jun 2024 (v1), last revised 24 Mar 2026 (this version, v5)] Title:Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching Authors:Yunni Qu (1), Bhargav Vaduri (1), Karthikeya Jatoth (1), James Wellnitz (2), Dzung Dinh (1), Seth Veenbaas (2), Jonathan Chapman (2), Alexander Tropsha (2), Junier Oliva (1) ((1) Department of Computer Science, University of North Carolina at Chapel Hill, (2) Eshelman School of Pharmacy, University of North Carolina at Chapel Hill) View a PDF of the paper titled Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching, by Yunni Qu (1) and 11 other authors View PDF HTML (experimental) Abstract:Machine learning (ML) models are increasingly deployed for virtual screening in drug discovery, where the goal is to identify novel, chemically diverse scaffolds while minimizing experimental costs. This creates a fundamental challenge: the most valuable discoveries lie in out-of-distribution (OOD) regions beyond the training data, yet ML models often degrade under distribution shift. Standard novelty-rejection strategies ensure reliability within the training domain but limit discovery by rejecting precisely the novel scaffolds most worth finding. Moreover, experimental budgets permit testing only a small fraction of nominated candidates, demanding models that produce reliable confidence estimates. We introduce EXPLOR (Extrapolatory Pseudo-Label Matchin...