MolFCL: predicting molecular properties through chemistry-guided contrastive and prompt learning.

ABSTRACT:

Motivation

Accurately identifying and predicting molecular properties is a crucial task in molecular machine learning, and the key lies in how to extract effective molecular representations. Contrastive learning opens new avenues for representation learning, and a large amount of unlabeled data enables the model to generalize to the huge chemical space. However, existing contrastive learning-based models face two challenges: (i) existing methods destroy the original molecular environment and ignore chemical prior information, and (ii) there is a lack of a prior knowledge to guide the prediction of molecular properties.

Results

In this work, we propose a molecular property prediction framework called MolFCL, which consists of fragment-based contrastive learning and functional

SUBMITTER: Tang X 

PROVIDER: S-EPMC11878793 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature