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使用深度神经网络和象征性AI进行化学合成计划
Marwin H S Segler1,2, Mike Preuss3, Mark P Waller4
1Institute of Organic Chemistry and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität, Münster, Germany.
Nature
|March 30, 2018
概括
这项研究引入了一种人工智能驱动的逆合成工具,可显著提高计划有机分子合成的速度和准确性. 这种新的方法产生了高质量的合成路径,与专家化学家相美.
科学领域:
- 有机化学
- 人工智能
- 计算化学
背景情况:
- 复合对于计划有机分子合成至关重要.
- 目前的计算机辅助逆合成工具速度缓慢,结果低于最佳.
研究的目的:
- 开发一个更快,更准确的计算机辅助复合系统.
- 为了利用人工智能和蒙特卡洛树的搜索,
主要方法:
- 结合深度神经网络 (扩展策略和过网络) 的蒙特卡罗树搜索.
- 在已发表的有机反应数据集上训练神经网络.
- 采用象征性人工智能进行回合成路径的发现.
主要成果:
- 与传统方法相比,人工智能系统解决了近两倍的分子问题.
- 这种新系统比现有的基于规则的计算机辅助逆合成速度快30倍.
- 双盲测试显示,人工智能生成的路线质量相当于文献中的路线.
结论:
- 人工智能驱动的复杂化比传统的计算方法有了显著的进步.
- 开发的系统为化学家提供了强大而有效的合成规划工具.
- 这种方法提高了复杂有机分子合成的发现和可行性.
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