# Learning to Select Source Domains: Proxy-Rewarded Policy Optimization for Molecular OOD Generalization

arXiv: https://arxiv.org/abs/2605.13932 · HTML: https://mlsanity.com/paper/2605.13932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 18 of 18)

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- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Equivariant message passing for the prediction of tensorial properties and molecular spectra](https://mlsanity.com/paper/2102.03150/) (2021)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020)
- [LogME: Practical Assessment of Pre-trained Models for Transfer Learning](https://mlsanity.com/paper/2102.11005/) (2021)
- [GOOD: A Graph Out-of-Distribution Benchmark](https://mlsanity.com/paper/2206.08452/) (2022)
- [Wasserstein Weisfeiler-Lehman Graph Kernels](https://mlsanity.com/paper/1906.01277/) (2019)
- [Out-Of-Distribution Generalization on Graphs: A Survey](https://mlsanity.com/paper/2202.07987/) (2022)
- [E(n) Equivariant Topological Neural Networks](https://mlsanity.com/paper/2405.15429/) (2024)

## Abstract (arXiv)

Molecular property prediction under severe out-of-distribution (OOD) shifts remains challenging because conventional scaffold splits can retain local structural similarity between training and test molecules, while adaptation from heterogeneous source domains may cause negative transfer. We introduce SCOPE-Bench, a scaffold-cluster benchmark constructed by partitioning Bemis-Murcko scaffolds in an explicit physicochemical descriptor space, and POMA, a retrieve-compose-adapt framework for source-domain selection when target property labels are unavailable. POMA uses unlabeled target structures to retrieve labeled source scaffolds as proxy targets. A policy is trained from reductions in proxy-task mean absolute error after adaptation, and the selected source subset is then used for target adaptation with covariance alignment at the whole-molecule and BRICS-derived substructure levels. We evaluate HOMO, LUMO, and HOMO-LUMO gap prediction on QM9 using three 3D molecular backbones and 15 target-scaffold tasks. Relative to conventional scaffold splitting, prediction errors on SCOPE-Bench increase by up to 8.0-fold, with a mean increase of 5.9-fold. Relative to the supervised baseline under the strict OOD split, POMA reduces mean absolute error by up to 11.2%, with an average relative reduction of 6.2% over the nine backbone-property combinations. These results support adaptive source selection as a useful strategy for molecular prediction under strong structural distribution shifts.