Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed which rely on a model of chemical reactivity. In this study, we model single-step retrosynthesis in a template-based approach using modern Hopfield networks (MHNs). We adapt MHNs to associate different modalities, reaction templates and molecules, which allows the model to leverage structural information about reaction templates. This approach significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed several times faster than that of baseline methods, we improve predictive performance for top-k exact match accuracy for $\mathrm{k}\geq5$ in the retrosynthesis benchmark USPTO-50k.
翻译:寻找有兴趣的分子的合成路径是发现新药物和材料的关键步骤。为了找到这些路径,采用了计算机辅助合成规划方法,这些方法依赖于化学反应模式。在本研究中,我们用现代Hopfield网络(MHNs)以模板为基础,模拟单步反合成方法。我们调整MHNs,将不同模式、反应模板和分子联系起来,使模型能够利用有关反应模板的结构信息。这一方法极大地改进了模板相关性预测的性能,特别是对于培训实例少或零的模板。用比基准方法快几倍的速度推断,我们改进了在复式合成基准USPTO-50k中最接近精确的精确度($\mathrm{k ⁇ qgeq5美元)的预测性能。