Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding DOI
Daniel P. Devore, Kevin L. Shuford

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(21), P. 8201 - 8214

Published: Oct. 29, 2024

The ability to predict the strength of halogen bonds and properties bond (XB) donors has significant utility for medicinal chemistry materials science. XBs are typically calculated through expensive ab initio methods. Thus, development tools techniques fast, accurate, efficient property predictions become increasingly more important. Herein, we employ three machine learning models classify XB complexes by their principal atom as well values maximum point on electrostatic potential surface (VS,max) interaction a molecular fingerprint data-based analysis. analysis produces root-mean-square error ca. 7.5 5.5 kcal mol–1 while predicting VS,max halobenzene haloethynylbenzene systems, respectively. However, prediction binding energy between ammonia acceptor is shown be within 1 density functional theory (DFT)-calculated energy. More accurate can made from precalculated DFT data when compared

Language: Английский

Computational Design of Bidentate HypervalentIodine Catalysts in Halogen Bond‐MediatedOrganocatalysis DOI Creative Commons

James A O’Brien,

Nika Melnyk, R. Lee

et al.

ChemPhysChem, Journal Year: 2024, Volume and Issue: 25(22)

Published: July 8, 2024

In recent years, halogen bond-based organocatalysis has garnered significant attention as an alternative to hydrogen-based catalysis, capturing considerable interest within the scientific community. This transition witnessed evolution of catalytic scaffolds from monodentate bidentate architectures, and monovalent hypervalent species. this DFT-based study, we explored a iodine(III)-based system that already undergone experimental validation. Additionally, explore various functionalisations (-CF

Language: Английский

Citations

1
Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding DOI
Daniel P. Devore, Kevin L. Shuford

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(21), P. 8201 - 8214

Published: Oct. 29, 2024

The ability to predict the strength of halogen bonds and properties bond (XB) donors has significant utility for medicinal chemistry materials science. XBs are typically calculated through expensive ab initio methods. Thus, development tools techniques fast, accurate, efficient property predictions become increasingly more important. Herein, we employ three machine learning models classify XB complexes by their principal atom as well values maximum point on electrostatic potential surface (VS,max) interaction a molecular fingerprint data-based analysis. analysis produces root-mean-square error ca. 7.5 5.5 kcal mol–1 while predicting VS,max halobenzene haloethynylbenzene systems, respectively. However, prediction binding energy between ammonia acceptor is shown be within 1 density functional theory (DFT)-calculated energy. More accurate can made from precalculated DFT data when compared

Language: Английский

Citations

0