Rethinking Molecular OOD Generalization via Target-Aware Source Selection
Researchers propose SCOPE-BENCH, a new benchmark for evaluating molecular property prediction under extreme out-of-distribution conditions, and POMA, a framework that uses reinforcement learning to select optimal source scaffolds for domain adaptation. Results show that existing models' errors increase up to 8x on the new benchmark, while POMA reduces error by up to 11.2%.
[2605.13932] Rethinking Molecular OOD Generalization via Target-Aware Source Selection
[Submitted on 13 May 2026]
Title:Rethinking Molecular OOD Generalization via Target-Aware Source Selection
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Abstract:Robust prediction of molecular properties under extreme out-of-distribution (OOD) scenarios is a pivotal bottleneck in AI-driven drug discovery. Current scaffold-splitting protocols fail to obstruct microscopic semantic overlap, predisposing models to shortcut learning and overestimating their true extrapolation capability; meanwhile, conventional domain adaptation paradigms suffer under extreme structural shifts, as blindly aligning heterogeneous source libraries injects topological noise and triggers negative transfer. To address these two challenges, scaffold-cluster out-of-distribution performance evaluation benchmark (SCOPE-BENCH), a benchmark built on cluster-level partitioning in an explicit physicochemical descriptor space, is proposed alongside policy optimization for multi-source adaptation (POMA), a framework that formulates knowledge transfer as a retrieve-compose-adapt pipeline: labeled source scaffolds structurally close to the unlabeled target are first identified as proxy targets; a reinforcement learning policy then adaptively selects the optimal source subset from an exponentially large candidate pool; and dual-scale domain adaptation is finally performed at macroscopic topological and microscopic pharmacophore scales. Evaluations show that prediction errors of state-of-the-art 3D molecular models surge by up to 8.0x on SCOPE-BENCH with a mean of 5.9x, while POMA achieves up to an 11.2% reduction in mean absolute error with an average relative improvement of 6.2% across diverse backbone architectures. Code is available at this https URL.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2605.13932 [cs.LG]
(or arXiv:2605.13932v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.13932
arXiv-issued DOI via DataCite (pending registration)
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From: Zhuohao Lin [view email] [v1] Wed, 13 May 2026 16:09:46 UTC (4,787 KB)
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