MaskMol: knowledge-guided molecular image pre-training framework for activity cliffs with pixel masking.

ABSTRACT:

Background

Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them.

Results

Our research indicates that as molecular similarity increases, graph-based methods struggle to capture these nuances, whereas image-based approaches effectively retain the distinctions. Thus, we developed MaskMol, a knowledge-guided molecular image self-supervised learning framework. MaskMol accurately learns the representation of molecular images by considering multiple levels of molecular knowledge, such as atoms, bonds, and substructures. By utilizing pixel masking tasks, MaskMol extracts fine-grained information from molecular ima

SUBMITTER: Cheng Z 

PROVIDER: S-EPMC12462177 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature