Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors

This study employs machine learning (ML) to assess the predictive power of electronic descriptors derived from natural bond orbital (NBO) analysis for hydrogen bond acceptance. Using a data set of 979 hydrogen bond complexes, each formed by a hydrogen bond acceptor and 4-fluorophenol as the donor, w...

Full description

Saved in:

Bibliographic Details
Main Author:	Diego Ulysses Melo (17676683) (author)
Other Authors:	Leonardo Martins Carneiro (21668282) (author), Mauricio Domingues Coutinho-Neto (17271623) (author), Paula Homem-de-Mello (1803232) (author), Fernando Heering Bartoloni (2015380) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Cell Biology Pharmacology Biotechnology Marine Biology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified reflect electron delocalization natural bond orbital machine learning model hydrogen bond acceptor hydrogen bond acceptance canonical resonance structures standalone ml descriptor generalizable ml models electronic descriptors derived 4 kcal mol bhx </ sub used heterogeneous descriptors >< sub k </ e </ values served small set results highlight predictive power point calculations physically meaningful including quantum first work different techniques dft single decision tree data set chemical data building accurate
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors

Similar Items