MolFCL: predicting molecular properties through chemistry-guided contrastive and prompt learning.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Feb 4, 2025
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.