Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

MoTSE: an interpretable task similarity estimator for small molecular property prediction tasks

Han Li, Xinyi Zhao, Shuya Li, Fangping Wan, Dan Zhao, Jianyang Zeng
doi: https://doi.org/10.1101/2021.01.13.426608
Han Li
1Institute for Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xinyi Zhao
1Institute for Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shuya Li
1Institute for Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Fangping Wan
1Institute for Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dan Zhao
1Institute for Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zhaodan2018@mail.tsinghua.edu.cn zengjy321@tsinghua.edu.cn
Jianyang Zeng
1Institute for Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
2MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zhaodan2018@mail.tsinghua.edu.cn zengjy321@tsinghua.edu.cn
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Understanding the molecular properties (e.g., physical, chemical or physiological characteristics and biological activities) of small molecules plays essential roles in biomedical researches. The accumulating amount of datasets has enabled the development of data-driven computational methods, especially the machine learning based methods, to address the molecular property prediction tasks. Due to the high cost of obtaining experimental labels, the datasets of individual tasks generally contain limited amount of data, which inspired the application of transfer learning to boost the performance of the molecular property prediction tasks. Our analyses revealed that simultaneously considering similar tasks, rather than randomly chosen ones, can significantly improve the performance of transfer learning in this field. To provide accurate estimation of task similarity, we proposed an effective and interpretable computational tool, named Molecular Tasks Similarity Estimator (MoTSE). By extracting task-related local and global knowledge from pretrained graph neural networks (GNNs), MoTSE projects individual tasks into a latent space and measures the distance between the embedded vectors to derive the task similarity estimation and thus enhance the molecular prediction results. We have validated that the task similarity estimated by MoTSE can serve as a useful guidance to design a more accurate transfer learning strategy for molecular property prediction. Experimental results showed that such a strategy greatly outperformed baseline methods including training from scratch and multitask learning. Moreover, MoTSE can provide interpretability for the estimated task similarity, through visualizing the important loci in the molecules attributed by the attribution method employed in MoTSE. In summary, MoTSE can provide an accurate method for estimating the molecular property task similarity for effective transfer learning, with good interpretability for the learned chemical or biological insights underlying the intrinsic principles of the task similarity.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ∗ This work was supported in part by the National Natural Science Foundation of China [61872216, 81630103], the Turing AI Institute of Nanjing and the Zhongguancun Haihua Institute for Frontier Information Technology.

  • https://github.com/lihan97/MoTSE

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted January 16, 2021.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
MoTSE: an interpretable task similarity estimator for small molecular property prediction tasks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
MoTSE: an interpretable task similarity estimator for small molecular property prediction tasks
Han Li, Xinyi Zhao, Shuya Li, Fangping Wan, Dan Zhao, Jianyang Zeng
bioRxiv 2021.01.13.426608; doi: https://doi.org/10.1101/2021.01.13.426608
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
MoTSE: an interpretable task similarity estimator for small molecular property prediction tasks
Han Li, Xinyi Zhao, Shuya Li, Fangping Wan, Dan Zhao, Jianyang Zeng
bioRxiv 2021.01.13.426608; doi: https://doi.org/10.1101/2021.01.13.426608

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4382)
  • Biochemistry (9594)
  • Bioengineering (7091)
  • Bioinformatics (24861)
  • Biophysics (12615)
  • Cancer Biology (9956)
  • Cell Biology (14354)
  • Clinical Trials (138)
  • Developmental Biology (7948)
  • Ecology (12105)
  • Epidemiology (2067)
  • Evolutionary Biology (15988)
  • Genetics (10925)
  • Genomics (14739)
  • Immunology (9869)
  • Microbiology (23670)
  • Molecular Biology (9484)
  • Neuroscience (50866)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2683)
  • Physiology (4014)
  • Plant Biology (8657)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2394)
  • Systems Biology (6435)
  • Zoology (1346)