将合成可及性纳入药物设计:利用艾伯维 15 年平行库数据集预测铃木交联反应产率。

IF 15.6 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Priyanka Raghavan, Alexander J. Rago, Pritha Verma, Majdi M. Hassan, Gashaw M. Goshu, Amanda W. Dombrowski, Abhishek Pandey, Connor W. Coley* and Ying Wang*, 
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引用次数: 0

摘要

尽管在药物设计中越来越多地使用计算工具来补充药物化学家的专业知识和直觉,但预测药物化学工作中的合成产率仍是一项尚未解决的挑战。现有的设计工作流程可以从反应产率预测中获益匪浅,因为这可以减少宝贵的材料浪费,并提供更多的相关化合物以推进设计、制造、测试、分析(DMTA)循环。在这项工作中,我们详细介绍了对艾伯维药物化学库数据集的评估,以建立预测铃木偶联反应产率的机器学习模型。密度泛函理论(DFT)推导出的特征与摩根指纹的结合被认为比单击编码基线建模效果更好,结果令人鼓舞。总体而言,我们观察到在 15 年的回顾性库数据集中,对未见过的反应物结构具有适度的通用性。此外,我们还将该模型的预测结果与药物化学家专家的预测结果进行了比较,发现该模型通常能更准确地预测反应成功率和反应产量。最后,我们展示了这种方法的应用,建议用结构上和电子学上相似的构筑模块来替代合成前或合成后分别预测或观察到的不成功的构筑模块。产率预测模型用于选择预测产率较高的相似单体,从而提高相关类药物分子的合成效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie’s 15-Year Parallel Library Data Set

Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie’s 15-Year Parallel Library Data Set

Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie’s 15-Year Parallel Library Data Set

Despite the increased use of computational tools to supplement medicinal chemists’ expertise and intuition in drug design, predicting synthetic yields in medicinal chemistry endeavors remains an unsolved challenge. Existing design workflows could profoundly benefit from reaction yield prediction, as precious material waste could be reduced, and a greater number of relevant compounds could be delivered to advance the design, make, test, analyze (DMTA) cycle. In this work, we detail the evaluation of AbbVie’s medicinal chemistry library data set to build machine learning models for the prediction of Suzuki coupling reaction yields. The combination of density functional theory (DFT)-derived features and Morgan fingerprints was identified to perform better than one-hot encoded baseline modeling, furnishing encouraging results. Overall, we observe modest generalization to unseen reactant structures within the 15-year retrospective library data set. Additionally, we compare predictions made by the model to those made by expert medicinal chemists, finding that the model can often predict both reaction success and reaction yields with greater accuracy. Finally, we demonstrate the application of this approach to suggest structurally and electronically similar building blocks to replace those predicted or observed to be unsuccessful prior to or after synthesis, respectively. The yield prediction model was used to select similar monomers predicted to have higher yields, resulting in greater synthesis efficiency of relevant drug-like molecules.

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来源期刊
CiteScore
24.40
自引率
6.00%
发文量
2398
审稿时长
1.6 months
期刊介绍: The flagship journal of the American Chemical Society, known as the Journal of the American Chemical Society (JACS), has been a prestigious publication since its establishment in 1879. It holds a preeminent position in the field of chemistry and related interdisciplinary sciences. JACS is committed to disseminating cutting-edge research papers, covering a wide range of topics, and encompasses approximately 19,000 pages of Articles, Communications, and Perspectives annually. With a weekly publication frequency, JACS plays a vital role in advancing the field of chemistry by providing essential research.
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