molecular-graph

MotifRole-Diff proposes a role-aware corruption schedule for masked discrete diffusion on molecular graphs, allocating masking rates based on denoising difficulty and graph-level perturbation impact, demonstrating improved validity and reduced FCD on QM9 and MOSES benchmarks.

This paper proposes a probabilistic contrastive pretraining framework for molecular graph transformers to improve multi-task ADME property prediction in drug discovery, achieving significant gains on three benchmarks.