  An essential step in the discovery of new drugs and materials is the
synthesis of a molecule that exists so far only as an idea to test its
biological and physical properties. While computer-aided design of virtual
molecules has made large progress, computer-assisted synthesis planning (CASP)
to realize physical molecules is still in its infancy and lacks a performance
level that would enable large-scale molecule discovery. CASP supports the
search for multi-step synthesis routes, which is very challenging due to high
branching factors in each synthesis step and the hidden rules that govern the
reactions. The central and repeatedly applied step in CASP is reaction
prediction, for which machine learning methods yield the best performance. We
propose a novel reaction prediction approach that uses a deep learning
architecture with modern Hopfield networks (MHNs) that is optimized by
contrastive learning. An MHN is an associative memory that can store and
retrieve chemical reactions in each layer of a deep learning architecture. We
show that our MHN contrastive learning approach enables few- and zero-shot
learning for reaction prediction which, in contrast to previous methods, can
deal with rare, single, or even no training example(s) for a reaction. On a
well established benchmark, our MHN approach pushes the state-of-the-art
performance up by a large margin as it improves the predictive top-100 accuracy
from $0.858\pm0.004$ to $0.959\pm0.004$. This advance might pave the way to
large-scale molecule discovery.
