Converting SMILES to Stacking Interaction Energies.

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Accurate prediction of stacking interactions between heterocycles and aromatic amino acids is essential for structure-based drug design.
• Current methods, like quantum chemical computations, provide high accuracy but are computationally expensive.
• Existing quantitative predictive models rely on electrostatic potential (ESP) descriptors derived from density functional theory (DFT).

Purpose of the Study:

• To develop a method for predicting heterocyclic stacking interaction energies without computationally intensive calculations.
• To demonstrate that ESP-based descriptors can be derived directly from atom connectivity.
• To enable rapid assessment of stacking potential for large sets of heterocycles.

Main Methods:

• Utilizing previously developed quantitative predictive models based on ESP descriptors.
• Evaluating ESP-based descriptors directly from heterocycle atom connectivity.
• Employing a freely available online tool for converting molecular representations (SMILES) to stacking interaction energies.

Main Results:

• ESP-based descriptors for stacking interactions can be reliably calculated solely from atom connectivity.
• This approach bypasses the need for quantum chemical computations on stacked dimers.
• The method allows for accurate prediction of stacking interaction energies.

Conclusions:

• A computationally inexpensive method for predicting heterocyclic stacking interactions has been established.
• This method facilitates the rapid screening and ranking of heterocycles based on their stacking abilities.
• The findings significantly advance the application of computational tools in drug design and discovery.