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Structure-based Pharmacophore Modeling


Nowadays, molecular docking technology is still the most popular structure-based drug design method, which makes full use of the protein-ligand interaction information. However, compared with molecular docking, structure-based pharmacophore methods show obvious advantages in terms of calculation cost and accuracy in virtual screening. Studies have shown that the structure-based pharmacophore model plays an essential role in the discovery of new active molecules in virtual screening. Structure-based pharmacophores are also successfully applied in molecular docking procedures to better distinguish between incorrect and correct conformations, thereby increasing the success rate of molecular docking. At Alfa Chemistry, the structure-based pharmacophore modeling generates chemical features of the active site and the sterical relationships from 3D structure of macromolecular target or macromolecule-ligand complex. It probes the possible interaction sites between the macromolecular target and the ligands.

Structure-based pharmacophore model generation and application.Figure 1. Structure-based pharmacophore model generation and application. (Sanders, M.; et al. 2012)

Application of Structure-based Pharmacophore Modeling

  • Virtual screening
  • ADME-tox prediction
  • Lead optimization
  • Drug target identification

Structure-based Pharmacophore Modeling Workflow

The structure-based pharmacophore construction process includes the following steps:

  • Identify and select the correct ligand
  • Recognize the active site
  • Check and correct the chemical structure of the ligand
  • Building a structure-based pharmacophore model

Our Services

Alfa Chemistry has utilized most state-of-the-art techniques and software tools for structure-based pharmacophore modeling. Depending on the situation and the type of experiment, multiple strategies are available to construct pharmacophore models. Our rapid and high-quality services are as follow:

  • Structure-based 3D pharmacophore identification

Structure-based 3D pharmacophore identification can be performed based on two types of atomic models:

1. In the macromolecule-ligand complex, the ligand is located at the binding site of the target molecule. Ligands are either co-crystals or docked to the target binding site to obtain a complex structure with macromolecules. In addition, we can also explore new chemical spatial regions within the same binding cavity. In this case, a new 3D pharmacophore can be generated for the same active site without being affected by existing ligands.

2. Our experts can also use the atomic model to derive the 3D pharmacophore model when macromolecule-ligand complex structure is not available, or there is no known ligand for the binding site. Ab initio algorithm is applied to arrange the pharmacophore features at the binding site, and the scaffold hopping is able to be generated by the arrangement of abstract features that are not combined with any specific ligand structure.

  • Pharmacophore modeling based on different targets

Even though protein is the most common drug target, protein is not the only macromolecular structure analyzed in the development of 3D pharmacophores. We can generate 3D pharmacophore models based on nucleic acids. For example, a 3D pharmacophore model is generated based on the binding site of the DNA-ligand complex structure.

  • Feature-based pharmacophore modeling

Feature-based methods can be used for the analysis of macromolecule-ligand complexes and empty binding sites. We use the feature-based program to analyze the target-ligand complex, and create a set of chemical and geometric rules to identify and classify the target-ligand interaction which then form the pharmacophore feature. We dock the fragments to the binding site, and select the most promising fragment docking pose to construct a 3D pharmacophore model.

  • Molecular field-based pharmacophore modeling

At Alfa Chemistry, we use molecular interaction fields (MIF) to identify pharmacophore characteristics. In this modeling method, we place spaced grids evenly in the predefined binding cavity and place probes to sample the binding sites. These probes are selected as various molecular fragments, representing the most likely interaction between the macromolecule and the ligand functional group. Then, we calculate the energy between the probe and target structure atoms to determine the interaction site. These probes can identify sites that interact with macromolecules and produce the MIF, which describes how the interaction energy between the target and a given probe changes with the surface of the target in the form of an energy isograph. The molecular field-based program takes the point of the local minimum of MIF energy as a 'hotspot', and converts it into a pharmacophore feature according to the type of probe with the most favorable energy interaction at this point.

Application of Our Structure-based Pharmacophore Models

  • Discover the key pharmacodynamic characteristics of drug molecules to establish a clear structure activity relationship (SAR).
  • Scaffold hopping: Virtual screening of compound libraries through pharmacophore models to discover new scaffold compounds that are active on the target.
  • Target fishing: Predict the pharmacological action spectrum of the compound by using the pharmacophore model.

Features of Our Structure-based Pharmacophore Modeling

  • 2D or 3D pharmacophore model building methods
  • Pharmacophore-based virtual screening with high performance computing
  • Advanced machine learning algorithms with various pharmacophore fingerprints of target ligand-binding pockets
  • Diverse ligand alignment algorithms

Our Advantages

  • The service of pharmacophores modeling is highly customizable according to the specific requirements from the customers.
  • We provide additional services of streamlining the initial 3D pharmacophore for optimization.
  • At Alfa Chemistry, we can select features based on the information of the binding site and the inner atoms of the binding site.
  • Our feature reduction can not only be performed manually, but also machine learning can be used to reduce the number of initial 3D pharmacophore features.

Our structure-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!


  • Sanders, M.; et al. From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling. MedChemComm. 2012, 3(1): 28-38.

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