Year: 2023
Type: article
Abstract: MOTIVATION: Contrastive learning has been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery. However, existing methods that generate molecular views by noise-adding operations for contrastive learning may face the semantic inconsistency problem, which leads to false positive pairs and consequently poor prediction performance. RESULTS: To address this problem, in this article, we first propose a semantic-invariant view generation method by properly breaking molecular graphs into fragment pairs. Then, we develop a Fragment-based Semantic-Invariant Contrastive Learning (FraSICL) model based on this view generation method for molecular property prediction. The FraSICL model consists of two branches to generate representations of views for contrastive learning, meanwhile a multi-view fusion and an auxiliary similarity loss are introduced to make better use of the information contained in different fragment-pair views. Extensive experiments on various benchmark datasets show that with the least number of pre-training samples, FraSICL can achieve state-of-the-art performance, compared with major existing counterpart models. AVAILABILITY AND IMPLEMENTATION: The code is publicly available at https://github.com/ZiqiaoZhang/FraSICL.
Source: Bioinformatics
Authors: Ziqiao Zhang, Ailin Xie, Jihong Guan, Shuigeng Zhou
Institutions: Fudan University, Tongji University
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,618
Awards: 单细胞RNA测序数据聚类方法研究, G2062780035
OpenAlex ID: W4385329973