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DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms.
Zhang, Siqin; Yang, Kuo; Liu, Zhenhong; Lai, Xinxing; Yang, Zhen; Zeng, Jianyang; Li, Shao.
Afiliação
  • Zhang S; Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.
  • Yang K; Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.
  • Liu Z; Institute for Brain Disorders, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
  • Lai X; Institute for Brain Disorders, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
  • Yang Z; School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
  • Zeng J; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
  • Li S; Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.
Brief Bioinform ; 24(1)2023 01 19.
Article em En | MEDLINE | ID: mdl-36527428
Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activating/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug-target (DT) mechanisms and data scarcity, modelling this problem based on deep learning methods to accurately predict DT activating/inhibiting mechanisms remains a considerable challenge. Here, by considering network pharmacology, we propose a multi-view deep learning model, DrugAI, which combines four modules, i.e. a graph neural network for drugs, a convolutional neural network for targets, a network embedding module for drugs and targets and a deep neural network for predicting activating/inhibiting mechanisms between drugs and targets. Computational experiments show that DrugAI performs better than state-of-the-art methods and has good robustness and generalization. To demonstrate the reliability of the predictive results of DrugAI, bioassay experiments are conducted to validate two drugs (notopterol and alpha-asarone) predicted to activate TRPV1. Moreover, external validation bears out 61 pairs of mechanism relationships between natural products and their targets predicted by DrugAI based on independent literatures and PubChem bioassays. DrugAI, for the first time, provides a powerful multi-view deep learning framework for robust prediction of DT activating/inhibiting mechanisms.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China