MolTaut

MolTaut generates and evaluates tautomeric states for drug-like molecules in aqueous environments to determine favorable tautomers by analyzing pharmacophore features and physical properties that influence biological activity.

Key Features:

• Tautomer enumeration: Systematically enumerates possible tautomeric forms using established transformation rules.
• Energy-based ranking: Ranks tautomers by calculating internal and solvation energies.
• AI-accelerated DFT approximation: Uses AI-based models trained to approximate DFT methods (wB97X/6-31G*//M062X/6-31G*/SMD) for energy calculations.
• Ionization state prediction: Predicts preferred ionization states by estimating microscopic pKa values.
• Pharmacophore and property analysis: Analyzes pharmacophore features and physical properties to assess tautomer favorability for biological activity.
• Substitution effect assessment: Predicts the effect of substituents on tautomeric equilibrium.
• Validation: AI-based scoring has been validated to achieve ranking accuracy comparable to traditional DFT methods.

Scientific Applications:

• Ligand design: Clarifies how structural modifications influence tautomerization behavior and interactions with biological targets.
• pH-dependent behavior prediction: Determines molecular behavior across different pH environments by estimating microscopic pKa values and preferred ionization states.
• Drug discovery workflows: Provides tautomer rankings and assessments suitable for integration into drug discovery, with accuracy comparable to DFT-based approaches.

Methodology:

Systematic enumeration of tautomeric states using transformation rules; ranking by internal and solvation energy calculations; use of AI-based models trained to approximate DFT (wB97X/6-31G*//M062X/6-31G*/SMD); prediction of preferred ionization states via microscopic pKa estimation.