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Masking and Demasking Agents

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...

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

Zhixiang Cheng^1,2, Hongxin Xiang^1,2, Pengsen Ma¹

¹College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.

|September 24, 2025

Summary

Activity cliffs pose challenges for machine learning models. Our novel MaskMol framework uses molecular images to accurately predict compound potency and identify potential drug candidates.

Keywords:

Activity cliff estimation Deep learning Drug discovery Explainable artificial intelligence Knowledge-guided pre-training

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Area of Science:

Computational chemistry
Drug discovery
Machine learning

Background:

Activity cliffs, pairs of similar molecules with differing potency, challenge current machine learning models.
High molecular similarity can cause model representation collapse, hindering accurate predictions.

Purpose of the Study:

To develop a novel self-supervised learning framework for molecular image representation.
To improve the accurate prediction of compound potency and activity cliff identification.
To enhance virtual screening and drug discovery processes.

Main Methods:

Developed MaskMol, a knowledge-guided molecular image self-supervised learning framework.
Employed pixel masking tasks to extract fine-grained information from molecular images.
Incorporated multi-level molecular knowledge (atoms, bonds, substructures) into the learning process.

Main Results:

MaskMol accurately learns molecular image representations, outperforming 25 state-of-the-art methods in activity cliff estimation and potency prediction across 20 targets.
Image-based approaches, like MaskMol, effectively capture distinctions missed by graph-based methods.
Identified candidate EP4 inhibitors for tumor treatment with high biological interpretability.

Conclusions:

MaskMol advances molecular image representation learning and virtual screening for drug discovery.
The study highlights the importance of addressing activity cliffs in structure-activity relationship (SAR) analysis.
Provides new insights into identifying subtle structural changes impacting molecular potency.