Rapid Ranking of Heterocycle-Side Chain Stacking Interactions Based on New Molecular Descriptors

Despite the importance of stacking interactions between ligands and aromatic amino acid side chains in binding sites, it is still unclear how such can be optimized context design. I will present highly-accurate computed interaction energies for parallel stacked dimers pharmaceutically relevant heterocycles. The trends that are observed described using a combination known descriptors dispersion new electrostatic potential (ESP) based heterocycle we have developed which been used to generate multi-parameter predictive model energy. These on statistical features electric field ESP plane near directly related number arrangement heteroatoms. This relationship provides means rapidly evaluating these descriptors, consequently energies, from simple molecular representations (e.g. SMILES, connectivity matrix). enables rapid ranking large sets heterocycles their ability.