Why Use this product?
Revilico’s Pharmacophore Analysis engine is a tool that can automatically detect and map critical binding features, generate 2D interaction diagrams, comprehensive text reports, and interactive 3D visualizations that reveal the pharmacophoric requirements for target binding and guide lead optimization strategies. This engine is best used when you need to visualize and analyze protein-ligand binding interactions to understand molecular recognition patterns for structure-based drug design.Utilizing this engine paints a picture of binding mapping within the protein’s pharmacophore and allows for chemists to make more informed decisions on medicinal chemistry transformations they’d like to optimize for in subsequent lead optimization campaigns.
A pharmacophore defines binding interactions between each atom of a protein and ligand, and represents the minimum set of chemical features and binding modalities a molecule exhibits to bind well to a target. Pharmacophore nad binding analysis also can help to match the behavior of known active molecules with defined interaction sets. Along with focusing on exact atom-atom interactions, it focuses on defining roles across the engineered molecule like hydrogen-bond donor, hydrogen-bond acceptor, hydrophobic region, aromatic ring, and positive or negative charges. Essentially many different molecules can share similar pharmacophores if they present the same interaction features in roughly the same 3D arrangements. Pharmacophores are used to help chemists find new scaffolds that still fit the binding site, and to allow for chemists to use that information to make well defined alterations of their leads to optimize the interactions for the protein pocket and amino acids of interest. Now how does it work? We take our protein-ligand complex defined and calculated by our virtual screening engine or co-folding algorithms and convert the molecules into decomposed 3D structures. Here the engine is able to identify interaction features on each structure (i.e. H-bond donors / acceptors, aromatic center, hydrophobic regions, and charged centers). It does this by parsing the inputted file for atoms and bonds, where it will apply chemical feature rules (i.e. N-H or O-H groups are considered H-bond donors). Then each feature is given a position in space (i.e. a single atom is given atom coordinates, a ring is given a ring centroid, and a functional group is given a geometric center). It will also check which ligand features are within interaction distance of protein residues to ensure physically rational assessments. This will confirm H-bond geometries, salt bridges, π - π stacking, and hydrophobic contacts. This will result in a set of labeled points in 3D space representing our ligand interaction map. The next step is alignment and selecting key pharmacophore features. For this, the model will capture only the interactions that stabilize binding, not every possible feature the ligand has. We have our panel of features, and we can then filter down this panel of features to relevant interactions and binding patterns primarily based on distance (e.g. whether the donor or acceptor is within hydrogen-bonding range), orientation (e.g. whether the angle is reasonable for a H-bond), environment (e.g. whether the hydrophobic feature is near hydrophobic residues), and redundancy (e.g. whether multiple features are describing the same interaction). We then conduct feature merging, where we merge multiple atoms contributing to the same type of interaction into one interaction (e.g. a phenyl ring that has many carbons can be merged to form one aromatic interaction to reduce redundancies in our reporting). In the end we will return a set of engagement patterns in 3D space where each interaction has a feature type (i.e. hydrogen-bond donor, hydrogen-bond acceptor, hydrophobic region, aromatic ring, positive or negative charge), 3D coordinates (where the interaction occurs in space), and tolerance radius (how much flexibility is allowed around that position). The Pharmacophore engine defines what the interaction is, where the interaction is taking place, and how much flexibility we have in that particular location. Credit to Revilico Advisor Pritam Kumar Panda: Pritam Kumar Panda. (2025). Protein AND ligAnd interaction MAPper: A Python package for visualizing protein-ligand interactions with 2D ligand structure representation. GitHub repository. https://github.com/pritampanda15/PandaMap