3D-QSAR predictions for α-cyclodextrin binding constants using quantum mechanically based descriptors.

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Supramolecular Chemistry

Background:

• Host-guest complexation, exemplified by organic chemical binding to alpha-cyclodextrin (αCD), is governed by the three-dimensional structures of both host and guest.
• Accurate prediction of binding constants is crucial but challenging, requiring precise representation of molecular interactions in 3D space.

Purpose of the Study:

• To evaluate a novel 3D-QSAR model utilizing quantum mechanically derived local sigma profiles (LSPs) from COSMOsar3D for predicting αCD binding constants.
• To compare the predictive performance of this new 3D-QSAR model against standard comparative molecular field analysis (CoMFA) and a reference 2D-QSAR model.

Main Methods:

• Development and application of a 3D-QSAR model incorporating LSPs derived via the COSMOsar3D method.
• Validation against existing literature data for αCD binding constants.
• Comparative analysis with established 2D-QSAR and CoMFA methodologies.

Main Results:

• The new 3D-QSAR model demonstrated superior predictive power compared to both reference models, achieving a lower RMSE (0.45 vs. 0.53/0.52) and higher R² (0.70 vs. 0.53/0.68).
• The model successfully differentiated binding constants between structural isomers, such as aliphatic alcohols, a capability lacking in the reference models.
• The model exhibited extrapolation capabilities to an independent literature dataset, highlighting its robustness.

Conclusions:

• The 3D-QSAR model employing LSPs offers a highly predictive and theoretically robust approach for modeling host-guest complexation.
• This modeling strategy shows significant promise for application to other binding phenomena, including protein-ligand interactions.