  Finding appropriate reaction conditions that yield high product rates in
chemical synthesis is crucial for the chemical and pharmaceutical industries.
However, due to the vast chemical space, conducting experiments for each
possible reaction condition is impractical. Consequently, models such as QSAR
(Quantitative Structure-Activity Relationship) or ML (Machine Learning) have
been developed to predict the outcomes of reactions and illustrate how reaction
conditions affect product yield. Despite these advancements, inferring all
possible combinations remains computationally prohibitive when using a
conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU)
to tackle these large-scale optimization problems more efficiently by solving
Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models
are constructed in this work: one using quantum annealing and the other using
ML. Both models are built and tested on four high-throughput experimentation
(HTE) datasets and selected Reaxys datasets. Our results suggest that the
performance of models is comparable to classical ML methods (i.e., Random
Forest and Multilayer Perceptron (MLP)), while the inference time of our models
requires only seconds with a DAU. Additionally, in campaigns involving active
learning and autonomous design of reaction conditions to achieve higher
reaction yield, our model demonstrates significant improvements by adding new
data, showing promise of adopting our method in the iterative nature of such
problem settings. Our method can also accelerate the screening of billions of
reaction conditions, achieving speeds millions of times faster than traditional
computing units in identifying superior conditions. Therefore, leveraging the
DAU with our developed QUBO models has the potential to be a valuable tool for
innovative chemical synthesis.
