Publishing neural networks in drug discovery might compromise training data privacy.

ABSTRACT: This study investigates the risks of exposing confidential chemical structures when machine learning models trained on these structures are made publicly available. We use membership inference attacks, a common method to assess privacy that is largely unexplored in the context of drug discovery, to examine neural networks for molecular property prediction in a black-box setting. Our results reveal significant privacy risks across all evaluated datasets and neural network architectures. Combining multiple attacks increases these risks. Molecules from minority classes, often the most valuable in drug discovery, are particularly vulnerable. We also found that representing molecules as graphs and using message-passing neural networks may mitigate these risks. We provide a framework to assess p

SUBMITTER: Kruger FP 

PROVIDER: S-EPMC11948693 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature