Augmenting genetic algorithms with machine learning for inverse molecular design,Chemical Science

当前位置： X-MOL 学术 › Chem. Sci. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Augmenting genetic algorithms with machine learning for inverse molecular design
Chemical Science ( IF 7.6 ) Pub Date : 2024-09-11 , DOI: 10.1039/d4sc02934h
Hannes Kneiding ₁ , David Balcells ₁

Affiliation

Evolutionary and machine learning methods have been successfully applied to the generation of molecules and materials exhibiting desired properties. The combination of these two paradigms in inverse design tasks can yield powerful methods that explore massive chemical spaces more efficiently, improving the quality of the generated compounds. However, such synergistic approaches are still an incipient area of research and appear underexplored in the literature. This perspective covers different ways of incorporating machine learning approaches into evolutionary learning frameworks, with the overall goal of increasing the optimization efficiency of genetic algorithms. In particular, machine learning surrogate models for faster fitness function evaluation, discriminator models to control population diversity on-the-fly, machine learning based crossover operations, and evolution in latent space are discussed. The further potential of these synergistic approaches in generative tasks is also assessed, outlining promising directions for future developments.

中文翻译：

通过机器学习增强遗传算法以进行逆向分子设计

进化和机器学习方法已成功应用于生成具有所需特性的分子和材料。这两种范式在逆向设计任务中的结合可以产生强大的方法，可以更有效地探索大量化学空间，从而提高生成化合物的质量。然而，这种协同方法仍然是一个新兴的研究领域，并且在文献中似乎尚未得到充分探索。这一观点涵盖了将机器学习方法纳入进化学习框架的不同方式，总体目标是提高遗传算法的优化效率。特别是，讨论了用于更快适应度函数评估的机器学习代理模型、用于动态控制群体多样性的鉴别器模型、基于机器学习的交叉操作以及潜在空间中的进化。还评估了这些协同方法在生成任务中的进一步潜力，概述了未来发展的有希望的方向。

更新日期：2024-09-11

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南