SCScore: Synthetic Complexity Learned from a Reaction Corpus,Journal of Chemical Information and Modeling

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SCScore: Synthetic Complexity Learned from a Reaction Corpus
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2018-01-26 00:00:00 , DOI: 10.1021/acs.jcim.7b00622
Connor W. Coley ₁ , Luke Rogers ₁ , William H. Green ₁ , Klavs F. Jensen ₁

Affiliation

Several definitions of molecular complexity exist to facilitate prioritization of lead compounds, to identify diversity-inducing and complexifying reactions, and to guide retrosynthetic searches. In this work, we focus on synthetic complexity and reformalize its definition to correlate with the expected number of reaction steps required to produce a target molecule, with implicit knowledge about what compounds are reasonable starting materials. We train a neural network model on 12 million reactions from the Reaxys database to impose a pairwise inequality constraint enforcing the premise of this definition: that on average, the products of published chemical reactions should be more synthetically complex than their corresponding reactants. The learned metric (SCScore) exhibits highly desirable nonlinear behavior, particularly in recognizing increases in synthetic complexity throughout a number of linear synthetic routes.

中文翻译：

SCScore：从反应语料库中学到的合成复杂性

存在一些分子复杂性的定义，以促进先导化合物的优先排序，识别引起多样性和复杂化的反应以及指导逆合成搜索。在这项工作中，我们将重点放在合成的复杂性上，并重新定义其定义，以与产生目标分子所需的预期反应步骤数相关联，同时对哪些化合物是合理的起始原料有隐含的认识。我们根据Reaxys数据库中的1200万个反应训练了一个神经网络模型，以施加成对的不等式约束，从而加强了此定义的前提：平均而言，已发布的化学反应的产物在合成上应比其相应的反应物更复杂。学习的指标（SCScore）表现出非常理想的非线性行为，

更新日期：2018-01-26

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