Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models,ACS Central Science

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Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
ACS Central Science ( IF 12.7 ) Pub Date : 2017-09-05 00:00:00 , DOI: 10.1021/acscentsci.7b00303
Bowen Liu ₁ , Bharath Ramsundar ₂ , Prasad Kawthekar ₂ , Jade Shi ₁ , Joseph Gomes ₁ , Quang Luu Nguyen ₁ , Stephen Ho ₁ , Jack Sloane ₁ , Paul Wender _{1,

3} , Vijay Pande _{1,

2,

4}

Affiliation

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.

中文翻译：

使用神经序列模型的逆合成反应预测

我们描述了一个完全数据驱动的模型，该模型学习执行逆合成反应预测任务，该任务被视为序列到序列映射问题。端到端训练模型具有由两个循环神经网络组成的编码器-解码器体系结构，该体系结构以前在解决其他序列对序列预测任务（例如机器翻译）方面显示出巨大的成功。该模型在来自美国专利文献的50,000个实验反应实例上进行了训练，这些实例涵盖了药物化学家常用的10种广泛的反应类型。我们发现我们的模型与基于规则的专家系统基准模型具有可比的性能，并且还克服了与基于规则的专家系统以及包含基于规则的专家系统组件的任何机器学习方法相关的某些限制。

更新日期：2017-10-25

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