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Datasets for: Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy - MaRDI portal

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Datasets for: Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

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DOI10.5281/zenodo.5792296Zenodo5792296MaRDI QIDQ6693393

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 19 January 2022

Copyright license: No records found.



Description

The datasets and supplementary materials for the manuscript Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy. Citations should refer directly to the manuscript (refer to the DOI 10.1021/acs.jcim.1c01103). The preprint version of of the manuscript is also available at: 10.33774/chemrxiv-2021-djd3d-v2 Regarding Solvation_data-1.0.0.zip: The datasets include the curated data for: (1) Abraham solute parameters, (2) solvation free energy, (3) solvation enthalpy, (4) gas-water partition coefficient (logKw), (5) water-1-octanol partition coefficient (logPow). The fitted Abraham and Mintz solvent parameters are also included. Detailed information can be found in the README.txt file of the zip file. Regarding ML_model_files.zip: This contains the machine learning model files for SoluteML and DirectML. For the instruction on how to use it, please refer to the chemprop_solvation git repository (https://github.com/fhvermei/chemprop_solvation)






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