Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling.

ABSTRACT: We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. We compare this pretraining strategy with two others: one based on molecular quantum properties (specifically the HOMO-LUMO gap) and one using a self-supervised atom masking technique. After fine-tuning on Therapeutic Data Commons ADMET datasets, we evaluate the performance improvement in the different models observing that models pretrained with atomic quantum mechanical properties produce in general better results. We then analyze the latent representations and observe that the supervised strategies preserve the pretraining information after fine-tuning a

SUBMITTER: Fallani A 

PROVIDER: S-EPMC11869672 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature