Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX.

ABSTRACT: Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple mole

SUBMITTER: Kengkanna A 

PROVIDER: S-EPMC10997661 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature