Field-based similarity

Science | Better Molecular Description | Alignment | Similarity | Machine Learning | Applications

In order to compare two different compounds in 3D in-silico methods, most software packages rely on maximizing shape similarity of the compounds and use pharmacophoric points that represent specific physicochemical properties. This approach, however, relies heavily on atomic positions, which greatly constrains the search space to similar molecular structures.

Nonetheless, the atoms themselves and their distribution within the molecule do not determine the interaction of a ligand with the receptor. Interaction fields, derived from the global structure of the molecule, govern the activity of ligands and receptors. Different atomic distributions can lead to similar interaction fields and, therefore, interact similarly with the receptor. However, these chemotypes cannot be uncovered if molecular alignment and scoring techniques heavily tied to atomic positions are used.

We compute a similarity score by combining the Tanimoto/Tversky indexes of the selected fields of interaction, with the goal of finding more chemical diversity. PharmScreen and PharmQSAR are capable of generating different fields of interaction to compare molecules and evaluate their similarity. Additionally, the user can adjust the field-based model configuration taking into account experimental data and receptor information.

field-based and stick representation of two molecules colored in red and blue and aligned by their hydrophobic molecular fields