Dataset and Computational Workflow for: Solubility modeling of pazopanib in cosolvent mixtures evaluating thermodynamics versus artificial intelligence

This repository contains the experimental data and the complete computational Python workflows used for the thermodynamic and machine learning modeling of the solubility of the antineoplastic drug pazopanib. The dataset includes equilibrium solubility data in four binary cosolvent systems: (ethanol + acetonitrile), (n-propanol + acetonitrile), (2-propanol + acetonitrile), and (1-butanol + acetonitrile) at temperatures ranging from 288.15 K to 328.15 K.The provided Python scripts (designed for Google Colab) execute a comparative chemometric analysis between classical thermodynamic approaches and artificial intelligence algorithms. The workflow includes: 1) Ideal Solubility Model: Calculations based on the enthalpy and melting temperature of the crystalline lattice. 2) van 't Hoff Semi-empirical Model: Linear regression models to estimate apparent thermodynamic properties of solution (enthalpy and entropy) and phase behavior. 3) Random Forest (RF) Regressor: Machine learning architectures (both generalized and individualized by cosolvent) trained exclusively with extreme pure-solvent data to predict complex non-linear molecular interactions. This dataset aims to facilitate the reproducibility of the statistical metrics (such as R^2, AARD%, RMSE, and MAE), parity plots, and thermodynamic analyses presented in the associated manuscript.