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J Chem Inf Model ; 64(14): 5646-5656, 2024 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-38976879

RESUMO

Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.


Assuntos
Aprendizado Profundo , Descoberta de Drogas , Proteínas , Descoberta de Drogas/métodos , Proteínas/química , Proteínas/metabolismo , Ligantes , Preparações Farmacêuticas/química , Preparações Farmacêuticas/metabolismo , Domínios Proteicos
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