De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models.
J Chem Inf Model
; 62(20): 4863-4872, 2022 Oct 24.
Article
em En
| MEDLINE
| ID: mdl-36219571
ABSTRACT
Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to fine-tune graph-based deep generative models for de novo molecular design tasks. We show how our computational framework can successfully guide a pretrained generative model toward the generation of molecules with a specific property profile, even when such molecules are not present in the training set and unlikely to be generated by the pretrained model. We explored the following tasks generating molecules of decreasing/increasing size, increasing drug-likeness, and increasing bioactivity. Using the proposed approach, we achieve a model which generates diverse compounds with predicted DRD2 activity for 95% of sampled molecules, outperforming previously reported methods on this metric.
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Base de dados:
MEDLINE
Assunto principal:
Desenho de Fármacos
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article