杂环反合成的迁移学习。

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Ewa Wieczorek, Joshua W. Sin, Sara Tanovic, Matthew T. O. Holland, Liam Wilbraham, Victor Sebastián-Pérez, Anthony Bradley, Dominik Miketa, Paul E. Brennan and Fernanda Duarte*, 
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引用次数: 0

摘要

杂环化合物是药物化学中重要的支架,可以调节药物的结合方式和药代动力学性质。杂环的重要性已经通过大量包含杂环及其性质的数据集的发表得到了例证。然而,这些数据集缺乏针对已发表的杂环化合物的合成路线。因此,新的和不常见的杂环化合物不容易合成。虽然反合成预测模型通常可以用于辅助合成化学家,但由于数据可用性低,它们在杂环形成反应中的性能较差。在这项工作中,我们比较了四种不同迁移学习方法的使用,以克服低数据可用性问题,并提高环断连接的逆合成预测模型的性能。混合微调模型达到了36.5%的最高精度,而且,62.1%的预测是化学有效的和环断裂的。此外,我们通过重建两个已发表的类药物靶点的合成路线,证明了混合微调模型在药物发现中的适用性。最后,我们介绍了一种随着新的反应数据的出现而进一步微调模型的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transfer Learning for Heterocycle Retrosynthesis

Heterocycles are important scaffolds in medicinal chemistry that can be used to modulate the binding mode as well as the pharmacokinetic properties of drugs. The importance of heterocycles has been exemplified by the publication of numerous data sets containing heterocyclic rings and their properties. However, those data sets lack synthetic routes toward the published heterocycles. Consequently, novel and uncommon heterocycles are not easily synthetically accessible. While retrosynthetic prediction models could usually be used to assist synthetic chemists, their performance is poor for heterocycle formation reactions due to low data availability. In this work, we compare the use of four different transfer learning methods to overcome the low data availability problem and improve the performance of retrosynthesis prediction models for ring-breaking disconnections. The mixed fine-tuned model achieves top-1 accuracy of 36.5%, and, moreover, 62.1% of its predictions are chemically valid and ring-breaking. Furthermore, we demonstrate the applicability of the mixed fine-tuned model in drug discovery by recreating synthetic routes toward two drug-like targets published in 2023. Finally, we introduce a method for further fine-tuning the model as new reaction data becomes available.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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