Ligand- and Pharmacophore-based Design

ASSIST IN DE NOVO DRUG DESIGN, MULTI-TARGET DRUG DESIGN AND ACTIVITY PROFILING

 

 

The binding interactions of proteins and ligands arise from a variety of steric and electrochemical factors. Understanding these interactions can help researchers more rapidly identify promising new therapeutic candidates.

Pharmacophores map these features in 3D space, providing a simple means to screen libraries of compounds virtually, characterize the binding of existing leads and optimize their performance.

BIOVIA Discovery Studio utilizes the CATALYST Pharmacophore Modeling and Analysis toolset to assist in the assessment of small molecule therapeutics with or without target-structured data. It supports de novo drug design, multi-target drug design and activity profiling to drive small molecule R&D.

    Build
    • Automatically generate pharmacophores from the data available
      • Sets of active ligands
      • Receptor binding sites
      • Receptor-ligand complexes
    • Perform rigorous Pharmacophore validation based on sets of control compounds with known activity.
    • Hypotheses can include
      • Geometric, feature-based queries
      • Shape-similarity
      • “Forbidden” space
    • Go beyond the limitations of classical pharmacophore elucidation algorithms by exploring Ensemble Pharmacophores for very large/diverse compound sets with a risk of multiple modes of action
    Apply

    Conduct robust Pharmacophore screening studies

    • Build and search databases of 3D conformations
    • Consider and analyze the full conformational space of your ligands
    • Explore off-target activity and drug repurposing using the PharmaDB* database

    Discovery Studio now includes the most extensive reported database for ligand profiling. Built from, and validated using, the scPDB*, the PharmaDB contains approximately 240,000 receptor-ligand pharmacophore models.
    Learn more

    Design

    Design and characterize your ligands and combinatorial libraries

    • Enumerate reaction- or core-based libraries
    • Enumerate ionization states, tautomers and isomers
    • Filter poor candidates with undesirable functional groups and Lipinski and Veber rules or your own criteria
    • Calculate numerous physicochemical and fingerprint properties
    • Optimize combinatorial libraries using Pareto optimization, diversity and similarity analysis
    • Clustering tools and 3D visualization using PCA analysis