Retrosynthetic analysis is a canonical technique for planning the synthesis route of organic molecules in drug discovery and development. In this technique, the screening of synthetic tree branches requires accurate forward reaction prediction, but existing software is far from completing this step independently. Previous studies attempted to apply a neural network to forward reaction prediction, but the accuracy was not satisfying. Through using the Edit Vector-based description and extended-connectivity fingerprints to transform the reaction into a vector, this study focuses on the update of the neural network to improve the template-based forward reaction prediction. Hard-threshold activation and the target propagation algorithm are implemented by introducing mixed convex-combinatorial optimization. Comparative tests were conducted to explore the optimal hyperparameter set. Using 15,000 experimental reaction data extracted from granted United States patents, the proposed hard-threshold neural network was systematically trained and tested. The results demonstrated that a higher prediction accuracy was obtained than that for the traditional neural network with backpropagation algorithm. Some successfully predicted reaction examples are also briefly illustrated.

中文翻译：

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基于硬阈值神经网络的有机合成结果预测

逆合成分析是用于计划药物发现和开发中有机分子合成路线的一种规范技术。在这种技术中，合成树枝的筛选需要准确的正向反应预测，但是现有软件远不能独立完成此步骤。先前的研究试图将神经网络应用于反应预测，但准确性并不令人满意。通过使用基于编辑矢量的描述和扩展连接指纹将反应转换为矢量，本研究着重于神经网络的更新以改进基于模板的正向反应预测。通过引入混合凸组合优化来实现硬阈值激活和目标传播算法。进行了比较测试以探索最佳超参数集。使用从授权的美国专利中提取的15,000个实验反应数据，对拟议的硬阈值神经网络进行了系统的培训和测试。结果表明，与采用反向传播算法的传统神经网络相比，该方法具有更高的预测精度。还简要说明了一些成功预测的反应实例。