Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features.
J Chem Inf Model
; 64(14): 5646-5656, 2024 Jul 22.
Article
em En
| MEDLINE
| ID: mdl-38976879
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Descoberta de Drogas
/
Aprendizado Profundo
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