Exploration of chemical space with partial labeled noisy student self-training and self-supervised graph embedding.
In: BMC Bioinformatics, Jg. 23 (2022-05-02), Heft 1, S. 1-20
Online
academicJournal
Zugriff:
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, toxicity, and others. PLANS-GINFP allows us to exploit millions of unlabeled chemical compounds as well as labeled and partially labeled pharmacological data to improve the generalizability of neural network models. Results: We evaluated the performance of PLANS-GINFP for predicting Cytochrome P450 (CYP450) binding activity in a CYP450 dataset and chemical toxicity in the Tox21 dataset. The extensive benchmark studies demonstrated that PLANS-GINFP could significantly improve the performance in both cases by a large margin. Both PLANS-based self-training and GINFP-based self-supervised learning contribute to the performance improvement. Conclusion: To better exploit chemical structures as an input for machine learning algorithms, we proposed a self-supervised graph neural network-based embedding method that can encode substructure information. Furthermore, we developed a model agnostic self-training method, PLANS, that can be applied to any deep learning architectures to improve prediction accuracies. PLANS provided a way to better utilize partially labeled and unlabeled data. Comprehensive benchmark studies demonstrated their potentials in predicting drug metabolism and toxicity profiles using sparse, noisy, and imbalanced data. PLANS-GINFP could serve as a general solution to improve the predictive modeling for QSAR modeling. [ABSTRACT FROM AUTHOR]
Copyright of BMC Bioinformatics is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Exploration of chemical space with partial labeled noisy student self-training and self-supervised graph embedding.
|
---|---|
Autor/in / Beteiligte Person: | Liu, Yang ; Lim, Hansaim ; Xie, Lei |
Link: | |
Zeitschrift: | BMC Bioinformatics, Jg. 23 (2022-05-02), Heft 1, S. 1-20 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 1471-2105 (print) |
DOI: | 10.1186/s12859-022-04681-3 |
Schlagwort: |
|
Sonstiges: |
|