JRgui

JRgui estimates physicochemical properties of molecules using the Joback and Reid group contribution method and detects functional groups from SMILES to generate molecular descriptors.

Key Features:

• Joback and Reid group contribution: Implements the Joback and Reid method to estimate molecular physicochemical properties by summing group contributions.
• Manual input of functional groups: Accepts user-provided types and occurrences of functional groups for property calculation.
• Automatic functional-group detection from SMILES: Uses SMILES strings to automatically identify functional groups for group-contribution calculations.
• Batch processing of SMILES: Parses multiple SMILES from a text file to process many molecules consecutively.
• Predicted properties and descriptors output: Produces predicted physical properties and approximately 200 molecular descriptors and saves them in a spreadsheet format.
• Computational libraries: Utilizes Python with RDKit for chemoinformatics processing and pandas for data handling and output.

Scientific Applications:

• Drug discovery: Rapid estimation of physicochemical properties and descriptors to support compound evaluation and prioritization.
• Materials science: Prediction of molecular properties relevant to material design and selection.
• Chemical engineering: Estimation of thermophysical properties for process modeling and design.
• High-throughput screening: Batch processing of large SMILES sets to enable screening of compound libraries.
• Individual molecule analysis: Manual specification of functional groups for detailed analysis of single compounds.

Methodology:

Calculations apply the Joback and Reid group contribution method by summing contributions from identified functional groups; RDKit is used for functional-group detection from SMILES and parsing, multiple SMILES can be read from a text file for batch processing, and pandas is used for data handling and saving outputs to spreadsheet format.