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4 results for “Solvation Free Energy”
ΔG-RDKit: Solvation Free Energy Database
<p>We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy".</p> <p>This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy Δ<em>G</em><sub>solv</sub> values. Data entries were collected from two separate databases. The <a href="https://link.springer.com/article/10.1007/s10822-014-9747-x">FreeSolv</a> library, with 642 experimental aqueous Δ<em>G</em><sub>solv </sub>determinations and the <a href="https://mediatum.ub.tum.de/1452571?v=1">Solv@TUM</a> database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties.</p> <p>Experimental Δ<em>G</em><sub>solv</sub> values range from -14 to 4 kcal mol<sup>-1</sup> and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using <a href="http://http://www.rdkit.org/">RDKit</a> software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<em>G</em><sub>solv</sub>.</p> <p>Through Machine Learning regression algorithms, our models were able to make Δ<em>G</em><sub>solv</sub> predictions with high accuracy, based on the information encoded in each chemical feature.</p>
Underlying data for "Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field"
<p>This dataset provides the parameters and results generated for the study "Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field"</p> <p>Contents of dataset:<br /> PARAMETERS.tar.gz<br /> A gzipped and tar'd directory with the parameters of all solutes sorted by solvent for AMOEBA and GAFF force field. This parameters directory is distributed to 4 different subdirectories: a)chloroform (21 solutes) b)toluene (21 solutes) c)dmso (6 solutes) d)acetonitrile (6 solutes). Each solvent includes two set of parameters for each solute (AMOEBA and GAFF).</p> <p>RESULTS.tar.gz<br /> A gzipped and tar'd directory with the solvation free energy results of all solutes in kcal/mol, with AMOEBA and GAFF force fields, for each solvent, in text format. A single set of conditions for a single solute is one row in the text files. The first column represents the experimental data, the remaining columns (2, 3 and 4) correspond to the three repeat simulations.</p>
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 "Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy". <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> </p> <p>Regarding "<strong>Solvation_data-1.0.0.zip</strong>":</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 "README.txt" file of the zip file.</p> <p> </p> <p>Regarding "<strong>ML_model_files.zip</strong>":</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> </p>
Inputs to calculate the solvation free energies of ECC ions in SPCE water
<p>Scripts and input files for the calculation of the solvation free energies of selected cations with the ECC model. The simulation box contains one cation and 800 water molecules. The ion topologies are from https://bitbucket.org/hseara/ions. Notably, before comparison with experimental values, the electronic solvation free energy contribution must be added to the result [1], as well as the correction due to the use of neutralizing charge density [2].</p> <p>In each folder, ./run.sh creates the folders representing different lambda values in the free energy perturbation approach and performs the respective calculation in each folder. There are a total of 31 windows (11 for electrostatics and 20 for Lennard-Jones). After running the script, the free energy can be extracted by</p> <p>gmx bar -f ?/md.xvg ??/md.xvg -temp 298 -o -b 100</p> <p>The results are reported in DOI: [ADD]</p> <p>[1] DOI: 10.1021/ct9005807</p> <p>[2] DOI: 10.1080/08927022.2015.1121544</p>
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