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

<p>The datasets and supplementary materials for the manuscript &quot;Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy&quot;. <strong>Citations should refer directly to the manuscript (refer to the DOI </strong><a href="https://doi.org/10.1021/acs.jcim.1c01103">10.1021/acs.jcim.1c01103</a><strong>)</strong>.</p> <p>The preprint version of of the manuscript is also available at: <a href="https://doi.org/10.33774/chemrxiv-2021-djd3d-v2">10.33774/chemrxiv-2021-djd3d-v2</a></p> <p>&nbsp;</p> <p>Regarding &quot;<strong>Solvation_data-1.0.0.zip</strong>&quot;:</p> <p>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.</p> <p>Detailed information can be found in the &quot;README.txt&quot; file of the zip file.</p> <p>&nbsp;</p> <p>Regarding &quot;<strong>ML_model_files.zip</strong>&quot;:</p> <p>This contains the machine learning model files for SoluteML and DirectML. For the instruction on how to use it, please refer to the <em>chemprop_solvation</em> git repository (<a href="https://github.com/fhvermei/chemprop_solvation">https://github.com/fhvermei/chemprop_solvation</a>)</p> <p>&nbsp;</p>