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Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.
Lin, Xuan; Dai, Lichang; Zhou, Yafang; Yu, Zu-Guo; Zhang, Wen; Shi, Jian-Yu; Cao, Dong-Sheng; Zeng, Li; Chen, Haowen; Song, Bosheng; Yu, Philip S; Zeng, Xiangxiang.
Afiliação
  • Lin X; College of Computer Science, Xiangtan University, Xiangtan, China.
  • Dai L; College of Computer Science, Xiangtan University, Xiangtan, China.
  • Zhou Y; College of Computer Science, Xiangtan University, Xiangtan, China.
  • Yu ZG; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China.
  • Zhang W; College of Informatics, Huazhong Agricultural University, China.
  • Shi JY; Northwestern Polytechnical University, Xian, China.
  • Cao DS; Xiangya School of Pharmaceutical Sciences, Central South University, China.
  • Zeng L; AIDD department of Yuyao Biotech, Shanghai, China.
  • Chen H; College of Computer Science and Electronic Engineering, Hunan University, 410013 Changsha, P. R. China.
  • Song B; College of Information Science and Engineering, Hunan University, Changsha, China.
  • Yu PS; University of Illinois at Chicago and also holds the Wexler Chair in Information Technology.
  • Zeng X; College of Information Science and Engineering, Hunan University, Changsha, China.
Brief Bioinform ; 24(4)2023 07 20.
Article em En | MEDLINE | ID: mdl-37401373
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Redes Neurais de Computação Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Redes Neurais de Computação Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido