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

Authors

Zhixiang Cheng, Hongxin Xiang, Pengsen Ma, Li Zeng, Xin Jin, Xixi Yang, Jianxin Lin, Xinxin Feng, Yang Deng, Changhui Deng, Bosheng Song, Xiangxiang Zeng

Published in

BMC biology. Volume 23. Issue 1. Pages 279. Sep 24, 2025. Epub Sep 24, 2025.

Abstract

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.
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 images, overcoming the limitations of existing deep learning models in identifying subtle structural changes. Experimental results demonstrate MaskMol's high accuracy and transferability in activity cliff estimation and compound potency prediction across 20 different macromolecular targets, outperforming 25 state-of-the-art deep learning and machine learning approaches. Visualization analyses reveal MaskMol's high biological interpretability in identifying activity cliff-relevant molecular substructures. Notably, through MaskMol, we identified candidate EP4 inhibitors that could be used to treat tumors.
This study raises awareness about activity cliffs and introduces a novel method for molecular image representation learning and virtual screening, advancing drug discovery and providing new insights into structure-activity relationships (SAR).

PMID:
40993695
Bibliographic data and abstract were imported from PubMed on 25 Sep 2025.

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