Molecular Design Method Using a Reversible Tree Representation of Chemical Compounds and Deep Reinforcement Learning.
J Chem Inf Model
; 62(17): 4032-4048, 2022 09 12.
Article
en En
| MEDLINE
| ID: mdl-35960209
ABSTRACT
Automatic design of molecules with specific chemical and biochemical properties is an important process in material informatics and computational drug discovery. In this study, we designed a novel coarse-grained tree representation of molecules (Reversible Junction Tree; "RJT") for the aforementioned purposes, which is reversely convertible to the original molecule without external information. By leveraging this representation, we further formulated the molecular design and optimization problem as a tree-structure construction using deep reinforcement learning ("RJT-RL"). In this method, all of the intermediate and final states of reinforcement learning are convertible to valid molecules, which could efficiently guide the optimization process in simple benchmark tasks. We further examined the multiobjective optimization and fine-tuning of the reinforcement learning models using RJT-RL, demonstrating the applicability of our method to more realistic tasks in drug discovery.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Refuerzo en Psicología
/
Aprendizaje Profundo
Idioma:
En
Revista:
J Chem Inf Model
Asunto de la revista:
INFORMATICA MEDICA
/
QUIMICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Japón