Exploration of chemical space with partial labeled noisy student self-training and self-supervised graph embedding.

ABSTRACT:

Background

Drug discovery is time-consuming and costly. Machine learning, especially deep learning, shows great potential in quantitative structure-activity relationship (QSAR) modeling to accelerate drug discovery process and reduce its cost. A big challenge in developing robust and generalizable deep learning models for QSAR is the lack of a large amount of data with high-quality and balanced labels. To address this challenge, we developed a self-training method, Partially LAbeled Noisy Student (PLANS), and a novel self-supervised graph embedding, Graph-Isomorphism-Network Fingerprint (GINFP), for chemical compounds representations with substructure information using unlabeled data. The representations can be used for predicting chemical properties such as binding affinity, toxic

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9063120 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature