Hosein Fooladi

Machine Learning Researcher

Oral presentation at the EUROPIN Summer School in Drug Design, summarizing our work on understanding and improving the generalization of machine learning models in chemical space — covering how out-of-distribution data should be defined for molecular property prediction, the limits of in-distribution-based model selection, and practical recommendations for bioactivity and ADMET tasks.

ALineMol overview: a training set with in-distribution and out-of-distribution test molecules; bar charts showing the performance drop from ID to OOD for two models; a scatter of OOD against ID performance; and the experimental setup of datasets, splitters and models.
ALineMol in one picture: molecules are split into in-distribution (ID) and out-of-distribution (OOD) test sets, models are compared on their ID-to-OOD performance drop, and we ask how well ID performance predicts OOD performance. Evaluated on eight datasets (CYP isoforms, hERG, HIV, AMES), ten splitters (scaffold-, property- and cluster-based, Lo-Hi, DataSAIL) and classical ML, GNN and pretrained-GNN models.

Authors: Hosein Fooladi and Johannes Kirchmair.

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