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Multi-task learning models for predicting active compounds.
J Biomed Inform. 2020 Jun 29; 108:103484.JB

Abstract

The computational drug discovery methods can find potential drug-target interactions more efficiently and have been widely studied over past few decades. Such methods explore the relationship between the structural properties of compounds and their biological activity with the assumption that similar compounds tend to share similar biological targets and vice versa. However, traditional Quantitative Structure - Activity Relationship (QSAR) methods often do not have desired accuracy due to insufficient data of compound activity. In this paper, we focus on building Multi-Task Learning (MTL)-based QSAR models by considering multiple similar biological targets together and make shared information transfer across from one task to another, thereby improving not only the learning efficiency, but also the prediction accuracy. This paper selects 6 assay groups with similar biological targets from PubChem and builds their QSAR models with MTL simultaneously. According to the experiment results, our MTL-based QSAR models have better performance over traditional prominent machine learning algorithms and the improvements are even more obvious when other baseline models have low accuracy. The superiority of our models is also proved by Student's t-test with level of significance 5%. Moreover, this paper also explores three different assumptions on the underlying pattern in the dataset and finds that the joint feature MTL models further improve the performance of the QSAR models and are more suitable for building QSAR models for multiple similar biological targets.

Authors+Show Affiliations

School of Information Science and Engineering, Lanzhou University, 730000 Lanzhou, China. Electronic address: zhaozhl@lzu.edu.cn.School of Information Science and Engineering, Lanzhou University, 730000 Lanzhou, China.School of Information Science and Engineering, Lanzhou University, 730000 Lanzhou, China.School of Information Science and Engineering, Lanzhou University, 730000 Lanzhou, China.School of Information Science and Engineering, Lanzhou University, 730000 Lanzhou, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32615159

Citation

Zhao, Zhili, et al. "Multi-task Learning Models for Predicting Active Compounds." Journal of Biomedical Informatics, vol. 108, 2020, p. 103484.
Zhao Z, Qin J, Gou Z, et al. Multi-task learning models for predicting active compounds. J Biomed Inform. 2020;108:103484.
Zhao, Z., Qin, J., Gou, Z., Zhang, Y., & Yang, Y. (2020). Multi-task learning models for predicting active compounds. Journal of Biomedical Informatics, 108, 103484. https://doi.org/10.1016/j.jbi.2020.103484
Zhao Z, et al. Multi-task Learning Models for Predicting Active Compounds. J Biomed Inform. 2020 Jun 29;108:103484. PubMed PMID: 32615159.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Multi-task learning models for predicting active compounds. AU - Zhao,Zhili, AU - Qin,Jian, AU - Gou,Zhuoyue, AU - Zhang,Yanan, AU - Yang,Yi, Y1 - 2020/06/29/ PY - 2019/11/20/received PY - 2020/05/29/revised PY - 2020/06/09/accepted PY - 2020/7/3/pubmed PY - 2020/7/3/medline PY - 2020/7/3/entrez KW - Drug discovery KW - Machine learning KW - Multi-task learning KW - QSAR KW - Transfer learning SP - 103484 EP - 103484 JF - Journal of biomedical informatics JO - J Biomed Inform VL - 108 N2 - The computational drug discovery methods can find potential drug-target interactions more efficiently and have been widely studied over past few decades. Such methods explore the relationship between the structural properties of compounds and their biological activity with the assumption that similar compounds tend to share similar biological targets and vice versa. However, traditional Quantitative Structure - Activity Relationship (QSAR) methods often do not have desired accuracy due to insufficient data of compound activity. In this paper, we focus on building Multi-Task Learning (MTL)-based QSAR models by considering multiple similar biological targets together and make shared information transfer across from one task to another, thereby improving not only the learning efficiency, but also the prediction accuracy. This paper selects 6 assay groups with similar biological targets from PubChem and builds their QSAR models with MTL simultaneously. According to the experiment results, our MTL-based QSAR models have better performance over traditional prominent machine learning algorithms and the improvements are even more obvious when other baseline models have low accuracy. The superiority of our models is also proved by Student's t-test with level of significance 5%. Moreover, this paper also explores three different assumptions on the underlying pattern in the dataset and finds that the joint feature MTL models further improve the performance of the QSAR models and are more suitable for building QSAR models for multiple similar biological targets. SN - 1532-0480 UR - https://www.unboundmedicine.com/medline/citation/32615159/Multi-Task_Learning_Models_for_Predicting_Active_Compounds L2 - https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(20)30112-X DB - PRIME DP - Unbound Medicine ER -
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