ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.
J Chem Inf Model
; 64(15): 5900-5911, 2024 Aug 12.
Article
em En
| MEDLINE
| ID: mdl-39092857
ABSTRACT
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at https//github.com/acellera/acegen-open and available for use under the MIT license.
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Base de dados:
MEDLINE
Assunto principal:
Descoberta de Drogas
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article