A Dataset of Computational Reaction Barriers for the Claisen Rearrangement: Chemical and Numerical Analysis

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.

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

“…To perform virtual experiments and computations, we use the Gibbs energy barrier data set of the Claisen rearrangement. 68 The Claisen rearrangement is a [3,3] sigmatropic rearrangement of an allyl vinyl ether, as illustrated in Figure 4a. This data set includes theoretical Gibbs energy barriers of the Claisen rearrangement for 100,000 molecules computed based on a semiempirical theory PM7.…”

“…70,71 Furthermore, after extensive numerical analysis, it has been shown that the data set reflects well the chemical trends of different substitutions and is useful for testing different implementations of an optimization algorithm. 68 In BO and random forest regression, each substituent is represented by a descriptor variable. As descriptor variables, eight parameters, HOMO energy, LUMO energy, HOMO− LUMO gap, NBO charge, Hammett constant, 72 and Sterimol parameters (substituent length: L, minimum radius: B 1 , and maximum radius: B 5 ), of each substituent capped by a hydrogen atom are used in this study.…”

“…For this purpose, these data sets can be anything as long as they are not too chemically unrealistic. Although the data set we employed is based on semiempirical PM7 theory, it was carefully examined in the former publication (ref ) and was shown to well describe the substituent dependence of the reactivity of the Claisen rearrangement. Therefore, the following numerical experiments using the PM7 data set are sufficient for the present purpose.…”

“…As a case study, this study focuses on the Claisen rearrangement and uses its theoretical Gibbs energy barrier data set based on semiempirical PM7 theory reported in the literature. 68 The data set contains Gibbs energy barriers of the Claisen rearrangement of allyl vinyl ether (Figure 4a) with ∼100,000 different substitutions (Figure 4b). Even though all of the barriers are for the Claisen rearrangement, these barriers have a wide distribution, as illustrated in Figure 4c.…”

Editorial: Chemical Reactions Mining

“…Genheden et al [5] proposed an interesting approach for predicting Buchwald-Hartwig reaction conditions, such as ligand, base, solvent, and (pre-)catalyst. In order to identify the most promising reactants for the Claisen reaction, Okada et al [6] applied a genetic algorithmbased approach coupled with quantum chemical reaction barrier estimation. Focusing on de novo design of synthetically available molecules, Ghiandoni et al [7] proposed reaction-based tool RENATE for reaction-based structure generation that can fragment and assemble molecules following a chemical logic.We hope that this special issue wil be of interest of the readers of Molecular Informatics.…”