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这就是为什么S S S S S S

Yanfei Guan1, Taegyo Lee1, Ke Wang2

  • 1Chemical Research and Development, Groton Laboratories, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States.

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此摘要是机器生成的。

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科学领域:

  • 计算化学是一种计算化学.
  • 预测化学反应的化学反应
  • 机器学习在化学中的应用

背景情况:

  • 预测区域选择性对于高效的药物合成至关重要.
  • 像DFT/TST和ML这样的当前方法有局限性 (耗时或依赖数据).

研究的目的:

  • 开发一种更快,更准确的方法来预测反应区域选择性.
  • 整合ML和DFT/TST以优化计算资源.

主要方法:

  • 一个混合工作流,结合了ML和DFT计算.
  • 对于低置信度的ML预测,DFT计算被选择性触发.
  • 该模型在SNR反应数据集 (Pfizer和USPTO) 上进行了训练和测试.

主要成果:

  • 在Pfizer数据集上达到96.3%的准确性,在USPTO数据集上达到94.7%.
  • 与传统的TST方法相比,显著减少了计算时间.

结论:

  • 混合ML-DFT方法为区域选择性预测提供了快速而准确的解决方案.
  • 这种方法可以通过尽量减少非生产性的合成努力来简化药物发现.