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Agraph convolution-based classification model for identifying anticancer metabolites from traditional vietnamese herbal medicine database

Published: 02 February 2018 Publication History

Abstract

Vietnam has been well known as a source of abundantly diverse herbal medicines for thousands of years, which serves a variety of purposes in drug development in attempts to address health issues, such as cancer. As claimed by a chemoinformatics-related principle that structurally similar chemical compounds will very likely have similar biological activity, this study employs molecular graph convolution, a machine learning architecture for extracting features from small molecules as undirected graphs, to predict anticancer ability of Vietnamese herbal medicines based on their metabolites' structures. In addition to molecular graph convolution, extended connectivity fingerprint (ECFP), a traditional featurizer for exploiting details of molecules, is also performed in order to make performance comparison. Finally, we successfully constructed a graph convolution-based neural network with high predictive accuracy on both training and validation set, suggesting that the model is reliable in detecting anticancer activity.

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Cited By

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  • (2024)Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer CompoundsJournal of Chemical Information and Modeling10.1021/acs.jcim.4c00643Online publication date: 28-Aug-2024
  • (2021)iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded EncodingJournal of Chemical Information and Modeling10.1021/acs.jcim.1c0062862:21(5059-5068)Online publication date: 21-Oct-2021

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ICMLSC '18: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing
February 2018
198 pages
ISBN:9781450363365
DOI:10.1145/3184066
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 02 February 2018

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Author Tags

  1. cancer
  2. extended connectivity fingerprint
  3. herbal medicines
  4. metabolites
  5. molecular graph convolution

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View all
  • (2024)Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer CompoundsJournal of Chemical Information and Modeling10.1021/acs.jcim.4c00643Online publication date: 28-Aug-2024
  • (2021)iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded EncodingJournal of Chemical Information and Modeling10.1021/acs.jcim.1c0062862:21(5059-5068)Online publication date: 21-Oct-2021

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