Learning molecular representation is a critical step in molecular machine
learning that significantly influences modeling success, particularly in
data-scarce situations. The concept of broadly pre-training neural networks has
advanced fields such as computer vision, natural language processing, and
protein engineering. However, similar approaches for small organic molecules
have not achieved comparable success. In this work, we introduce a novel
pre-training strategy, substrate scope contrastive learning, which learns
atomic representations tailored to chemical reactivity. This method considers
the grouping of substrates and their yields in published substrate scope tables
as a measure of their similarity or dissimilarity in terms of chemical
reactivity. We focus on 20,798 aryl halides in the CAS Content Collection
spanning thousands of publications to learn a representation of aryl halide
reactivity. We validate our pre-training approach through both intuitive
visualizations and comparisons to traditional reactivity descriptors and
physical organic chemistry principles. The versatility of these embeddings is
further evidenced in their application to yield prediction, regioselectivity
prediction, and the diverse selection of new substrates. This work not only
presents a chemistry-tailored neural network pre-training strategy to learn
reactivity-aligned atomic representations, but also marks a first-of-its-kind
approach to benefit from the human bias in substrate scope design.

学习分子表示是分子机器学习中一个关键步骤，它对于建模的成功具有重大影响，特别是在数据稀缺情况下。本研究引入了一种新颖的预训练策略，基底范围对比学习，通过学习与化学反应性相匹配的原子表示。该方法将已发布的基底范围表中的基底组织和收率视为其在化学反应性方面的相似性或差异性的度量。研究验证了预训练方法，并应用于产量预测、区域选择性预测和新基底的选择。该研究不仅提出了一种针对化学的神经网络预训练策略，学习与反应性相匹配的原子表示，而且还首次应用了基底范围设计中的人为偏见。