Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies

Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies

Abstract We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our a...

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Bibliographic Details
Main Authors: Christoph A. Bauer, Gisbert Schneider, Andreas H. Göller
Format: Article
Language:English
Published: BMC 2019-09-01
Series:Journal of Cheminformatics
Subjects:
Computational chemistry
Density functional theory
Hydrogen bond strength
Free energy prediction
Cheminformatics
Online Access:http://link.springer.com/article/10.1186/s13321-019-0381-4

Internet

http://link.springer.com/article/10.1186/s13321-019-0381-4

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