Comprehensive Interrogation on Acetylcholinesterase Inhibition by Ionic Liquids Using Machine Learning and Molecular...

Area of Science:

• Computational Chemistry
• Toxicology
• Biochemistry

Background:

• Quantitative structure-activity relationship (QSAR) models predict chemical toxicity but often lack reliability.
• Existing QSAR models for ionic liquids (ILs) frequently use single machine learning methods, overlooking complex biological interactions.
• Understanding IL interactions with biological targets like acetylcholinesterase (AChE) is crucial for developing safer alternatives.

Purpose of the Study:

• To develop more reliable and interpretable QSAR models for predicting IL toxicity.
• To elucidate the molecular mechanisms underlying the inhibition of AChE by ILs.
• To establish a systematic approach for QSAR modeling and molecular mechanism investigation.

Main Methods:

• Systematic analysis of 153 ILs using machine learning and molecular modeling techniques.
• Application of multiple machine learning approaches to build and validate QSAR models.
• Molecular docking simulations and binding free energy calculations to investigate IL-AChE interactions.

Main Results:

• Combined QSAR models achieved high reliability and stability (R² > 0.85 for cross-validation and external validation).
• Molecular docking revealed noncovalent interactions (π interactions, hydrogen bonds) between IL components and AChE amino acid residues.
• Electrostatic interactions were identified as the primary driving force for IL binding to AChE (ΔEele < -285 kJ/mol).

Conclusions:

• A systematic, multi-method approach significantly enhances the reliability and interpretability of QSAR models for IL toxicity.
• Understanding specific binding interactions provides insights into the molecular mechanisms of IL inhibition.
• This comprehensive strategy is essential for designing next-generation, biosafely engineered ionic liquids.