Molecular substructure tree generative model for de novo drug design.
Brief Bioinform
; 23(2)2022 03 10.
Article
en En
| MEDLINE
| ID: mdl-35039853
ABSTRACT
Deep learning shortens the cycle of the drug discovery for its success in extracting features of molecules and proteins. Generating new molecules with deep learning methods could enlarge the molecule space and obtain molecules with specific properties. However, it is also a challenging task considering that the connections between atoms are constrained by chemical rules. Aiming at generating and optimizing new valid molecules, this article proposed Molecular Substructure Tree Generative Model, in which the molecule is generated by adding substructure gradually. The proposed model is based on the Variational Auto-Encoder architecture, which uses the encoder to map molecules to the latent vector space, and then builds an autoregressive generative model as a decoder to generate new molecules from Gaussian distribution. At the same time, for the molecular optimization task, a molecular optimization model based on CycleGAN was constructed. Experiments showed that the model could generate valid and novel molecules, and the optimized model effectively improves the molecular properties.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Diseño de Fármacos
/
Modelos Moleculares
Idioma:
En
Revista:
Brief Bioinform
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
China