STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks.
Comput Biol Med
; 167: 107691, 2023 12.
Article
en En
| MEDLINE
| ID: mdl-37976819
With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training process may be unstable, such as gradient disappearance and model collapse, which may lead to the generation of molecules that do not conform to chemical rules or a single style. In this paper, a novel method called STAGAN was proposed to solve the difficulty of model training, by adding a new gradient penalty term in the discriminator and designing a parallel layer of batch normalization used in generator. As an illustration of method, STAGAN generated higher valid and unique molecules than previous models in training datasets from QM9 and ZINC-250K. This indicates that the proposed method can effectively solve the instability problem in the model training process, and can provide more instructive guidance for the further study of molecular graph generation.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Descubrimiento de Drogas
/
Aprendizaje Profundo
/
Modelos Químicos
Idioma:
En
Revista:
Comput Biol Med
Año:
2023
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos