Code for: Bridging Chemistry and Artificial Intelligence — A Hands-On Tutorial in ChemoInformatics (ESOL solubility pipeline)

Reproduction code for the hands-on aqueous-solubility pipeline described in Section 7 of the tutorial manuscript "Bridging Chemistry and Artificial Intelligence: A Hands-On Tutorial in ChemoInformatics" (submitted to KI - Künstliche Intelligenz, Springer). The pipeline loads the ESOL (Delaney) dataset from MoleculeNet, parses SMILES with RDKit, builds a feature matrix from physicochemical descriptors and 2048-bit Morgan fingerprints, trains a random-forest regressor with scikit-learn, and reports RMSE and R² on a held-out test split. A complete script (solubility_tutorial.py) and three numbered snippets matching the manuscript listings are included. Requirements: Python 3.10+, RDKit, pandas, NumPy, scikit-learn (see requirements.txt). Usage: pip install -r requirements.txt python solubility_tutorial.py The ESOL dataset is publicly available from MoleculeNet/DeepChem; a local copy (delaney-processed.csv) is bundled for convenience.