A Computational Software for Training Robust Drug-Target Affinity Prediction Models: pydebiaseddta.
J Comput Biol
; 30(11): 1240-1245, 2023 11.
Article
em En
| MEDLINE
| ID: mdl-37988394
ABSTRACT
Robust generalization of drug-target affinity (DTA) prediction models is a notoriously difficult problem in computational drug discovery. In this article, we present pydebiaseddta a computational software for improving the generalizability of DTA prediction models to novel ligands and/or proteins. pydebiaseddta serves as the practical implementation of the DebiasedDTA training framework, which advocates modifying the training distribution to mitigate the effect of spurious correlations in the training data set that leads to substantially degraded performance for novel ligands and proteins. Written in Python programming language, pydebiaseddta combines a user-friendly streamlined interface with a feature-rich and highly modifiable architecture. With this article we introduce our software, showcase its main functionalities, and describe practical ways for new users to engage with it.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Linguagens de Programação
/
Software
Idioma:
En
Revista:
J Comput Biol
Assunto da revista:
BIOLOGIA MOLECULAR
/
INFORMATICA MEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Turquia