HSP Parameter & Affinity Estimator

Theoretical Introduction & Instructions

The Core Principle: The Group Contribution Method

The basic idea is that every molecule, no matter how complex, is simply an assembly of simpler chemical "building blocks" (the functional groups). Each of these blocks contributes in a predictable and additive way to the final properties of the molecule, much like LEGO® bricks contribute to a final structure.

This tool uses this principle to estimate a molecule's properties based solely on its chemical structure, without needing experimental data.

The Workflow: From SMILES to Affinity

1. The Blueprint (SMILES String): You provide a SMILES string (e.g., CCO for Ethanol), which is a text-based blueprint of the molecule's structure.
2. The Parser (RDKit.js): The tool uses a chemoinformatics library to read this blueprint and create a digital map of the molecule, identifying all its functional groups.
3. The Calculation: The tool has a database of "contributions" for each functional group. It sums these contributions to estimate the molecule's Hansen Solubility Parameters (δD, δP, δH) and its Molar Volume (Vₘ).
4. The Prediction (Affinity Ranking): This method can accurately predict the *center* of the Hansen Sphere but not its *size* (the Interaction Radius, R₀). Therefore, instead of a simple "soluble/insoluble" answer, this tool provides something more powerful: an **Affinity Ranking**. It calculates the **Hansen Distance (Ra)** between your solute and every solvent in the database.
   Rule of thumb: A lower `Ra` indicates a better chemical match and a higher probability of solubility. This allows you to rank potential solvents and prioritize your real-world experiments.