A Dataset of Computational Reaction Barriers for the Claisen Rearrangement: Chemical and Numerical Analysis. Issue 3 (18th October 2021)

• Record Type: Journal Article
• Authors: Okada, Hiroaki; Maeda, Satoshi
• Is Part Of: Molecular informatics. Volume 41:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-18
• Subjects: Transition state -- Claisen rearrangement -- Genetic algorithm -- Quantum chemical calculation -- Reaction barrier dataset; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202100216
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: Theoretical reaction screening based on Gibbs energy barriers would be promising to accelerate chemical reactions mining. The number of quantum chemical calculations can be reduced by using an optimization algorithm such as genetic algorithm (GA) and Bayesian optimization (BO). The focus of this study is to generate a dataset of reaction barriers of size ∼100000. Such a dataset would be useful to quickly evaluate various implementations of an optimization algorithm such as GA and BO. The dataset includes Gibbs energy barriers of the Claisen rearrangement for ∼100000 molecules computed on the basis of a semiempirical theory PM7. After evaluating its chemical and numerical features, it is found that the dataset well reflects chemical trends of various substitutions and is useful in testing various implementations of an optimization algorithm. The dataset is available in the supplementary material of this paper. Abstract :