ArticleACS omega2022
Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Bayesian Optimization of a Suzuki Coupling for the Industrial Synthesis of a Sartan Drug Intermediate.ACS omega · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- A Guide to Bayesian Optimization in Bioprocess Engineering.Biotechnology and bioengineering · 2026Review
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Local reaction condition optimization via machine learning.Journal of molecular modeling · 2025Review
- Deep Kernel learning for reaction outcome prediction and optimization.Communications chemistry · 2024Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Bayesian Optimization in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- AI-driven robotic chemist for autonomous synthesis of organic molecules.Science advances · 2023Article
- Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center.Research (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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