Out-of-Distribution

Rethinking Molecular OOD Generalization via Target-Aware Source Selection

This research addresses challenges in robust molecular property prediction under extreme out-of-distribution (OOD) scenarios crucial for AI-driven drug discovery. It proposes SCOPE-BENCH, a new benchmark for OOD performance evaluation, and POMA, a framework for multi-source adaptation to overcome limitations of existing methods.