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

Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery

View ORCID ProfileYunchao “Lance” Liu, Yu Wang, Oanh Vu, View ORCID ProfileRocco Moretti, Bobby Bodenheimer, View ORCID ProfileJens Meiler, View ORCID ProfileTyler Derr
doi: https://doi.org/10.1101/2022.08.24.505155
Yunchao “Lance” Liu
1Vanderbilt University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Yunchao “Lance” Liu
Yu Wang
1Vanderbilt University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Oanh Vu
1Vanderbilt University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rocco Moretti
1Vanderbilt University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rocco Moretti
Bobby Bodenheimer
1Vanderbilt University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jens Meiler
1Vanderbilt University
2Leipzig University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jens Meiler
  • For correspondence: jens.meiler@vanderbilt.edu
Tyler Derr
1Vanderbilt University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tyler Derr
  • For correspondence: jens.meiler@vanderbilt.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph Neural Network (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chiralityawareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom’s similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other GNNs in CADD. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our codes are publicly available at https://github.com/meilerlab/MolKGNN.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • {yunchao.liu{at}vanderbilt.edu, yu.wang.1{at}vanderbilt.edu, oanh.t.vu.2{at}vanderbilt.edu, rocco.moretti{at}vanderbilt.edu, bobby.bodenheimer{at}vanderbilt.edu, jens.meiler{at}vanderbilt.edu, tyler.derr{at}vanderbilt.edu}

  • https://figshare.com/articles/dataset/Well-curated_QSAR_datasets_for_diverse_protein_targets/20539893

  • ↵2 This is explicitly mentioned in its manual: https://mnam.com/wp-content/uploads/2021/10/corina_classic_manual.pdf

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 August 26, 2022.
Download PDF
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.
Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
(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
Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
Yunchao “Lance” Liu, Yu Wang, Oanh Vu, Rocco Moretti, Bobby Bodenheimer, Jens Meiler, Tyler Derr
bioRxiv 2022.08.24.505155; doi: https://doi.org/10.1101/2022.08.24.505155
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
Yunchao “Lance” Liu, Yu Wang, Oanh Vu, Rocco Moretti, Bobby Bodenheimer, Jens Meiler, Tyler Derr
bioRxiv 2022.08.24.505155; doi: https://doi.org/10.1101/2022.08.24.505155

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 (4684)
  • Biochemistry (10361)
  • Bioengineering (7677)
  • Bioinformatics (26337)
  • Biophysics (13530)
  • Cancer Biology (10687)
  • Cell Biology (15444)
  • Clinical Trials (138)
  • Developmental Biology (8498)
  • Ecology (12822)
  • Epidemiology (2067)
  • Evolutionary Biology (16865)
  • Genetics (11400)
  • Genomics (15480)
  • Immunology (10617)
  • Microbiology (25221)
  • Molecular Biology (10224)
  • Neuroscience (54478)
  • Paleontology (402)
  • Pathology (1668)
  • Pharmacology and Toxicology (2897)
  • Physiology (4344)
  • Plant Biology (9249)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2558)
  • Systems Biology (6781)
  • Zoology (1466)