A pharmacophore is defined as the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure. In general, a useful pharmacophore involves important information about functional groups that interact with the target, and the type of noncovalent interactions and interatomic distances between these functional groups/interactions.
Figure 1. Typical ligand-based pharmacophore generation and screening workflow. (Stephani, J. Y. M.; et al. 2020)
Ligand-based Pharmacophore Modeling Strategies
In order to create a ligand-based pharmacophore, various active compounds are overlaid in such a way that a maximum number of chemical features overlap geometrically involving rigid 2D or 3D structural representations or incorporate molecular flexibility to determine overlapping sites. Alfa Chemistry applies two approaches to generate a ligand-based pharmacophore model:
a) Compute the conformational space of each ligand and create a general-purpose conformational model
b) Explore conformations by changing molecule coordinates as needed by the alignment algorithm
Ligand-based Pharmacophore Modeling Workflow
- Import a known ligand and set up a training set of compounds
- Generate 3D structure and perform ligands cluster
- Set up a test set of compounds
- Generate ligand-based pharmacophore models
Alfa Chemistry supports multiple methods to perform high-qualtiy pharmacophore modeling by employing pharmacophore generation algorithm:
- Superimposing active compounds to create a pharmacophore
1. We apply either a point-based or property-based technique to align molecules using Gaussian functions.
2. Rigid, flexible, and semiflexible method are used to efficiently sample conformational space.
3. Our teams apply molecular dynamics to perform conformational search during the alignment process.
4. We also use active analog approach to limit the exploration of conformational space.
- Pharmacophore feature extraction
In order to balance the generalizability with specificity, pharmacophore feature map is carefully constructed. In general, hydrogen bond acceptors and donors, acidic and basic groups, aliphatic hydrophobic moieties, and aromatic hydrophobic moieties are the most common features used to define pharmacophore maps. Moreover, features are commonly implemented as spheres with a certain tolerance radius for pharmacophore matching.
- Pharmacophore algorithms and software packages
Alfa Chemistry supports various software packages for ligand-based pharmacophore generation and they apply different approaches to molecular alignment, flexibility, and feature extraction.
1. Phase: It supports a tree-based partitioning algorithm and an RMS deviation-based scoring function that considers the volume of heavy atom overlap. In addition, a Monte Carlo or torsional search is applied to incorporate molecular flexibility.
2. Catalyst: it uses two algorithms for pharmacophore construction, and performs alignment and feature extraction by identifying common chemical features arranged in certain positions in three-dimensional space.
3. DISCO: It fully considers multiple conformations and uses a clique-detection algorithm for scoring alignments.
4. GASP: It uses a genetic algorithm with iterative generations of the best models for pharmacophore construction. Flexibility can be handled during the alignment process using random rotations and translations, and conformations are optimized by fitting them to similarity constraints and weighing the conformations.
- Several pharmacophoric techniques and alignment methods based on ligand shape and electrostatic similarity can be used to compare different series of compounds and their structure-activity relationship (SAR) data or for virtual screening to rapidly identify novel potentially active compounds.
- Our professional pharmacophore modeling and virtual screening team can identify pharmacophore models from a series of compounds with similar binding modes.
Our ligand-based pharmacophore modeling services remarkably reduce the cost, promote further experiments, and accelerate the process of drug design for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!
- Stephani, J. Y. M.; et al. In Silico Strategies in Tuberculosis Drug Discovery. Molecules. 2020, 25(3): 665.