Organic synthesis methodology enables the synthesis of complex molecules and materials used in all fields of science and technology and represents a vast body of accumulated knowledge optimally suited for deep learning. While most organic reactions involve distinct functional groups and can readily be learned by deep learning models and chemists alike, regio- and stereoselective transformations are more challenging because their outcome also depends on functional group surroundings. Here, we challenge the Molecular Transformer model to predict reactions on carbohydrates where regio- and stereoselectivity are notoriously difficult to predict. We show that transfer learning of the general patent reaction model with a small set of carbohydrate reactions produces a specialized model returning predictions for carbohydrate reactions with remarkable accuracy. We validate these predictions experimentally with the synthesis of a lipid-linked oligosaccharide involving regioselective protections and stereoselective glycosylations. The transfer learning approach should be applicable to any reaction class of interest.

有机合成方法能够合成用于所有科学技术领域的复杂分子和材料，代表了最适合深度学习的大量积累的知识。虽然大多数有机反应涉及不同的官能团，并且可以很容易地被深度学习模型和化学家学习，但区域选择性和立体选择性转化更具挑战性，因为它们的结果也取决于官能团环境。在这里，我们挑战分子变压器模型来预测碳水化合物的反应，其中区域选择性和立体选择性是众所周知难以预测的。我们表明，对一小组碳水化合物反应的通用专利反应模型进行迁移学习会产生一个专门的模型，以极高的准确性返回对碳水化合物反应的预测。我们通过合成涉及区域选择性保护和立体选择性糖基化的脂质连接寡糖来实验验证这些预测。迁移学习方法应该适用于任何感兴趣的反应类别。