Atom surface fragment contribution method for predicting the toxicity of ionic liquids.

Area of Science:

• Computational Chemistry
• Toxicology
• Materials Science

Background:

• Assessing compound properties, particularly toxicity, is crucial for chemical development.
• Ionic liquids (ILs) present unique properties but require robust toxicity evaluation.
• Existing methods may lack the precision needed for diverse IL applications.

Purpose of the Study:

• To introduce a novel computational method, Atom Surface Fragment Contribution (ASFC), for property prediction.
• To develop and validate a predictive model for estimating the toxicity of ionic liquids (ILs).
• To leverage sigma surface areas of molecular fragments for enhanced predictive accuracy.

Main Methods:

• The Atom Surface Fragment Contribution (ASFC) method was developed.
• Sigma surface areas (S_σ-surface) of atomic fragments were calculated using COSMO profiles.
• A predictive model was trained and validated using a dataset of 140 ILs against the ICP-81 cell line.

Main Results:

• The ASFC model achieved a high coefficient of determination (R²) of 0.924.
• The model demonstrated a low Mean Squared Error (MSE) of 0.071, indicating high reliability.
• The developed model accurately predicted the toxicity of ionic liquids.

Conclusions:

• The ASFC method offers a highly accurate and reliable approach for toxicity prediction in ILs.
• This novel method shows significant potential for estimating diverse properties of various compounds.
• ASFC provides a valuable tool for computational chemistry and materials science research.