Please refer to the git repository (add a link to https://github.com/fhvermei/SolProp_ML) for detailed installation guide, sample code, citation, and license information

General information: <\b>

Machine learning and thermodynamics for the prediction of solubility of pharmaceuticals in organic solvents and water. Package includes code and trained ML models.

Github code: https://github.com/fhvermei/SolProp_ML

Read & cite publication:

Web-interface: https://rmg.mit.edu/database/solvation/search/

To install: <\b>

conda install -c fhvermei -c conda-forge solprop_ml

For temperature dependent calculations, additional install required:

pip install git+https://github.com/bp-kelley/descriptastorus

To run (more info on github):<\b>

import solvation_predictor.calculate_solubility as calc

results = calc.calculate_solubility(path=path, df=df, validate_data_list=['solute', 'solvent', 'reference_solvent', 'temperature'], calculate_aqueous=True, calculate_Hdiss_T_dep=True, reduced_number=False, export_csv='./results_test.csv', export_detailed_csv=True, solv_crit_prop_dict=None, logger='./test.log')