Project Overview

This project focuses on predicting the aqueous solubility of chemical compounds, a critical parameter in drug discovery and development. Poor solubility can hinder a drug's absorption and bioavailability, making early prediction essential. This application leverages machine learning to build a robust predictive model based on molecular structures.

The core of the project involves using the RDKit library to convert chemical structures (represented as SMILES strings) into numerical features known as molecular fingerprints or descriptors. These features capture the structural properties of the molecules, which are then used to train a Scikit-learn model.

Key Technologies & Concepts

• Python: The primary programming language for data processing and modeling.
• RDKit: An open-source cheminformatics toolkit used for generating molecular descriptors from SMILES strings.
• Scikit-learn: For implementing the machine learning pipeline, including model training (e.g., Random Forest) and evaluation.
• Pandas & NumPy: For efficient data manipulation and numerical operations.
• Model Evaluation: The model's performance is assessed using metrics like R-squared and Root Mean Squared Error (RMSE) to ensure its predictive accuracy.

The goal is to create a reliable tool that can quickly estimate the solubility of new, unseen molecules, thereby accelerating the process of identifying promising drug candidates.