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AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.
Zhang, Sizhe; Tian, Xuecong; Chen, Chen; Su, Ying; Huang, Wanhua; Lv, Xiaoyi; Chen, Cheng; Li, Hongyi.
Afiliação
  • Zhang S; College of Software, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Tian X; College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Chen C; College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Su Y; College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Huang W; College of Information Science and Engineering, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Lv X; College of Software, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Chen C; College of Software, Xinjiang University, Urumqi, 830046 Xinjiang, China.
  • Li H; Xinjiang University, Urumqi, 830046 Xinjiang, China.
J Chem Inf Model ; 64(10): 4373-4384, 2024 May 27.
Article em En | MEDLINE | ID: mdl-38743013
ABSTRACT
Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Simulação de Acoplamento Molecular Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Simulação de Acoplamento Molecular Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China
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