skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices

Deep Learning Based Drug Metabolites Prediction

Wang, Disha ; Liu, Wenjun ; Shen, Zihao ; Jiang, Lei ; Wang, Jie ; Li, Shiliang ; Li, Honglin

Frontiers in pharmacology, 2020-01, Vol.10, p.1586-1586 [Periódico revisado por pares]

Switzerland: Frontiers Research Foundation

Texto completo disponível

Citações Citado por
  • Título:
    Deep Learning Based Drug Metabolites Prediction
  • Autor: Wang, Disha ; Liu, Wenjun ; Shen, Zihao ; Jiang, Lei ; Wang, Jie ; Li, Shiliang ; Li, Honglin
  • Assuntos: deep learning ; drug metabolism ; Metabolites ; metabolites prediction ; Pharmacology ; Physiological aspects ; reaction rules ; SMARTS
  • É parte de: Frontiers in pharmacology, 2020-01, Vol.10, p.1586-1586
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Reviewed by: Cao Dongsheng, Central South University, China; Mingyue Zheng, Chinese Academy of Sciences, China
    Edited by: Alex Zhavoronkov, Biogerontology Research Foundation, United Kingdom
    This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology
  • Descrição: Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.
  • Editor: Switzerland: Frontiers Research Foundation
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.