Gotta be SAFE: a new framework for molecular design

Noutahi, Emmanuel; Gabellini, Cristian; Craig, Michael; Lim, Jonathan S. C.; Tossou, Prudencio

doi:10.1039/d4dd00019f

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…ACEGEN currently provides an environment for language model experimentation including SMILES, DeepSMILES, SELFIES, AtomInSmiles and SAFE grammars, complemented by a user-friendly vocabulary class. Section A in the Supporting Information illustrates how the vocabulary and environment can be easily created and utilized for data generation in ACEGEN.…”

Section: Methodsmentioning

confidence: 99%

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery

Bou,

Thomas,

Dittert

et al. 2024

J. Chem. Inf. Model.

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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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Section: Methodsmentioning

confidence: 99%

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery

Bou,

Thomas,

Dittert

et al. 2024

J. Chem. Inf. Model.

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Review of External Field Effects on Electrocatalysis: Machine Learning Guided Design

Wang,

Zhou,

Luo

et al. 2024

Adv Funct Materials

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External field‐enhanced electrocatalysis is a novel and promising approach for boosting the efficiency of electrocatalytic reactions, potentially achieving significant enhancement without altering the composition and structure of electrocatalysts. In addition, the scaling relations of electrocatalysis typically lead to similar variations of initial‐state and transition‐state (TS) energy, which minimally impacts the reaction energy barrier. A sophisticated design of the external field effects shall break these scaling relations. This review provides a comprehensive overview of current research on the effect of mechanical, electric, and magnetic fields on electrocatalysis. It meticulously details the mechanisms underlying activity enhancement based on external field regulations, spanning from the synthesis of electrocatalytic materials to their behavior during the reaction process and modulation of the electrolyte environment. Additionally, the applications of emerging machine learning (ML) technologies in electrocatalysis design, including machine learning interatomic potentials (MLIPs) to simulate large‐scale and dynamic chemical reaction processes, data‐driven design and optimization of electrocatalysis performance, are briefly reviewed. In addition, the significant potential of ML technologies in conjunction with external field regulation, envisioning them as effective tools for optimizing or reverse designing electrocatalysis, considering both thermodynamic and kinetic factors as well as the dynamic effect of electrocatalyst surfaces under extreme external fields, is highlighted.

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