cgbind

cgbind predicts binding affinities and analyzes DFT-modeled Pd2L4 metallocages to assess binding and catalytic proficiency.

Key Features:

• Accurate Binding Affinity Calculations: Employs a DFT-based methodology validated against experimental data with a reported mean absolute deviation (MAD) of 1.9 kcal/mol for binding affinity predictions.
• Catalytic Proficiency Identification: Distinguishes catalytically active and inactive Pd2L4 metallocages with over 90% accuracy by analyzing their interactions with substrates.
• Structural Dynamics Analysis: Analyzes structural dynamics and flexibility to identify how subtle variations in the cage framework affect binding and catalysis, emphasizing transition state distortion energy.
• Automation for Novel Architectures: Automates screening of novel Pd2L4 metallocage architectures for computational exploration.

Scientific Applications:

• Catalysis Research: Predicts binding and catalytic proficiency of Pd2L4 metallocages to inform design and selection of bio-inspired catalysts.
• Supramolecular Chemistry: Assesses how different metallocage configurations influence molecular interactions and assembly behavior.

Methodology:

Uses DFT-based modeling and simulation of Pd2L4 metallocages, calculation of binding affinities, analysis of structural dynamics including transition state distortion energy, and automated screening of metallocage architectures.