Physical and Chemical Properties of Substances

This dataset is an extended version of the "Wikipedia Molecules Properties Dataset" with added SMILES representations, additional physicochemical properties calculated using the thermo library, and chemical classification according to the ClassyFire system for approximately 4,200 compounds.

Key Features

Original structural formulas taken from the Wikipedia Molecules Properties Dataset (15,000+ molecules)

SMILES representations obtained for ~4,200 compounds extracted from structural formulas using the thermo library

Calculated physicochemical properties (melting point, boiling point, etc.) obtained using the thermo library

Chemical classification according to the ClassyFire system (Kingdom, Superclass, Class, Subclass, etc.)

Feature Description

name: Name of the chemical compound

formula: Chemical formula of the compound

CAS: Unique CAS (Chemical Abstracts Service) identification number

smiles: Molecular structure representation in SMILES format (Simplified Molecular Input Line Entry System)

InChI: International Chemical Identifier

InChIKey: Hashed version of InChI for quick searching

molecular_weight: Molecular weight of the compound (g/mol)

melting_point_K: Melting point in Kelvin

boiling_point_K: Boiling point in Kelvin

heat_of_fusion: Heat of fusion (enthalpy of fusion), J/mol

heat_of_vaporization: Heat of vaporization (enthalpy of vaporization), J/mol

critical_temperature: Critical temperature, K

critical_pressure: Critical pressure, Pa

flash_point: Flash point, K

logP: Octanol-water partition coefficient (measure of lipophilicity)

improved_name: Improved/standardized name of the compound

kingdom: Kingdom in chemical taxonomy

superclass: Superclass of the compound

class: Class of the compound in chemical taxonomy

direct_parent: Direct parent class of the compound

substituents: Substituents and functional groups in the compound

Tools and Resources Used

Wikipedia Molecules Properties Dataset: the dataset used as the foundation

Thermo: A library for calculating thermodynamic and transport properties of chemicals

ClassyFire: A chemical taxonomy system and classifier for small molecules

License

CC0: Public Domain - This dataset is in the public domain. You can copy, modify, distribute and perform the data, even for commercial purposes, all without asking permission.

Citation

When using this dataset, please cite:

Original dataset: Wikipedia Molecules Properties Dataset

This extended dataset: "Ivan Yakovlev G. (2024). Physical and Chemical Properties of Substances. Kaggle."

Contact Information

Ivan YakovlevEmail: [e-mail]: www.linkedin.com/in/ivanyakovlevg

Version 3.0.0 (2025-04-28)

Major Enhancements

Added functional group classification: Each compound is now classified according to 26 different functional groups including alcohols, alkanes, aromatics, ethers, and more

SMILES representation: Added canonical SMILES strings for all compounds with valid InChI identifiers

Organic/inorganic classification: Each compound is now labeled as organic or inorganic based on chemical structure

Data Processing Improvements

InChI standardization: Fixed inconsistent InChI prefixes, ensuring all follow the standard "InChI=1S/" format

Structure validation: All molecular structures have been validated using RDKit's chemical informatics toolkit

Missing data handling: Improved handling of compounds with invalid or missing structural identifiers

Technical Details

Used RDKit for chemical structure manipulation and SMILES conversion

Applied Thermo library for functional group classification

Conversion success rate: 98.7% of compounds with valid InChI were successfully converted to SMILES

Functional group identification completed for 97.9% of valid structures

Dataset Statistics

Top 5 functional groups identified:

Organic compounds: 76.4%

Hydrocarbons: 34.2%

Aromatics: 23.7%

Alcohols: 19.8%

Ethers: 14.3%

Known Limitations

Approximately 1.3% of InChI strings could not be converted to SMILES due to structural inconsistencies

Some complex metal-organic compounds may have inconsistent functional group classification

Polymers and mixtures may have limited functional group detection accuracy

Potential Applications

Enhanced structure-activity relationship modeling

More precise chemical similarity searches

Improved filtering and grouping based on chemical functionality

Better substructure-based analysis for drug discovery and material science