1 research outputs found
Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization
The optimization of organic reaction conditions to obtain
the target
product in high yield is crucial to avoid expensive and time-consuming
chemical experiments. Advancements in artificial intelligence have
enabled various data-driven approaches to predict suitable chemical
reaction conditions. However, for many novel syntheses, the process
to determine good reaction conditions is inevitable. Bayesian optimization
(BO), an iterative optimization algorithm, demonstrates exceptional
performance to identify reagents compared to synthesis experts. However,
BO requires several initial randomly selected experimental results
(yields) to train a surrogate model (approximately 10 experimental
trials). Parts of this process, such as the cold-start problem in
recommender systems, are inefficient. Here, we present an efficient
optimization algorithm to determine suitable conditions based on BO
that is guided by a graph neural network (GNN) trained on a million
organic synthesis experiment data. The proposed method determined
8.0 and 8.7% faster high-yield reaction conditions than state-of-the-art
algorithms and 50 human experts, respectively. In 22 additional optimization
tests, the proposed method needed 4.7 trials on average to find conditions
higher than the yield of the conditions recommended by five synthesis
experts. The proposed method is considered in a situation of having
a reaction dataset for training GNN