Planning chemical syntheses with deep neural networks and symbolic AI
Planning chemical syntheses with deep neural networks and symbolic AI
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DOI：
10.1038/nature25978
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发表时间：
2018-03-29
期刊：
影响因子：
64.8
通讯作者：
Waller, Mark P.
中科院分区：
文献类型：
--
作者：
Segler, Marwin H. S.;Preuss, Mike;Waller, Mark P.
为了计划小有机分子的合成，化学家们使用反合成，这是一种解决问题的技术，其中目标分子递归地转化为越来越简单的前体。计算机辅助反合成将是一个有价值的工具，但目前它是缓慢的，提供的结果质量不令人满意。在这里，我们使用蒙特卡洛树搜索和符号人工智能（AI）来发现反合成路线。我们将蒙特卡罗树搜索与指导搜索的扩展策略网络和预先选择最有希望的反合成步骤的过滤网络相结合。这些深度神经网络基本上是在有机化学上发表过的所有反应上训练的。我们的系统解决的分子数量几乎是传统计算机辅助搜索方法的两倍，速度是传统计算机辅助搜索方法的30倍，传统计算机辅助搜索方法基于提取的规则和手工设计的启发式。在双盲AB测试中，化学家平均认为计算机生成的路线与文献报道的路线相同。
To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.