ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
Eindhoven University of Technology, Department of Biomedical Engineering, Eindhoven, Netherlands.
Methods Mol Biol. 2022;2390:207-232. doi: 10.1007/978-1-0716-1787-8_9.
Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language models have been shown to perform well in various experimental scenarios. In this study, we provide a hands-on introduction to chemical language modeling. A technique based on recurrent neural networks is discussed in detail, together with a step-by-step guide to applying this AI method for focused compound library design. The program code is freely available at URL: github.com/ETHmodlab/de_novo_design_RNN .
人工智能(AI)为药物化学中的命中和先导发现提供了新的可能性。已经有几种 AI 被用于前瞻性的从头药物设计。在这些方法中,化学语言模型在各种实验场景中表现良好。在这项研究中,我们提供了化学语言建模的实践介绍。详细讨论了基于递归神经网络的技术,并提供了一个逐步指南,用于应用此 AI 方法进行有针对性的化合物库设计。程序代码可在 URL:github.com/ETHmodlab/de_novo_design_RNN 处免费获得。