ThermoPred: AI-Enhanced Quantum Chemistry Data Set and ML Toolkit for Thermochemical Properties of API-Like Compounds and Their Degradants

In this work, we present an open-access quantum-chemistry
database
of more than 14,500 API-like molecules and their degradation products,
all optimized at the M06-2X/6-31G(d) compound model.
The data set delivers a comprehensive suite of thermochemical and
quantum descriptorsincluding Gibbs free energy, enthalpy,
electronic energy, vibrational frequencies and Cartesian geometriestailored
for large-scale modeling. Leveraging these data, we trained and validated
three machine-learning models (XGBoost, Random Forest and Multi-Layer
Perceptron) to enable rapid, accurate prediction of Gibbs free energy
and enthalpy. These models are bundled in ThermoPred, an open-source
Python package that offers a scalable, computationally efficient alternative
to traditional quantum-chemical calculations. All data sets, models
and source code are freely available to support reproducibility and
foster community-driven development.