Pharmacophore Model Construction Service

Computer-aided drug design mainly includes two methods: 1. Receptor-based drug design. 2. Drug design based on ligand. When the crystal structure and binding site of the protein are known, receptor-based drug design methods such as molecular docking can be used. However, the molecular docking method has some shortcomings, such as the inability to correctly handle the induced fit effect, the solvation effect, and the poor ranking ability of the scoring function. In addition, the crystal structure of a large number of proteins is still unknown, especially for membrane proteins. The extremely hydrophobic nature of membrane proteins makes it difficult to purify and crystallize. For targets with unknown crystal structure, when there are multiple ligands with similar structures, pharmacophore-based drug design methods can be used.

Overall solutions

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In order to obtain an accurate pharmacophore model, first we use the 3D structure of the correct compound. Therefore, factors such as valence, bond level, protonation state, tautomerism, and stereoisomerism must be carefully examined. In addition, another prerequisite for obtaining an accurate pharmacophore model is that the compounds used to construct the model have similar binding modes.

The process of constructing a pharmacophore is:

  • Select-group ligands that are active to the same binding site in a specific target.
  • Perform conformation analysis of all ligands.
  • Specify the characteristics of the pharmacophore.
  • Superimpose the conformation of the ligand to obtain the pharmacophore model.

Virtual screening method based on pharmacophore

The pharmacophore-based virtual screening method is a reliable and fast virtual screening tool. The receptor-based virtual screening method is slow to screen millions of compound libraries. In addition to the speed factor, receptor-based virtual screening methods rarely consider protein flexibility, the influence of water molecules, solvation effects, and the conformational limitations of ligands.

3D-based pharmacophore model

MedAI can also provide 3D-based pharmacophore models. The elucidation methods of 3D pharmacophore can be divided into feature-based, sub-structure mode-based or molecular field-based, depending on the source of the pharmacophore feature. Feature-based methods derive pharmacophore characteristics by filtering geometric descriptors matching molecular interaction characteristics. Pattern-based methods can detect substructures of molecular chemical features. For example, all hydroxyl groups are defined as hydrogen bond donors and acceptors. In contrast, the molecular field-based method uses different chemical probes to sample the molecular surface of ligands or macromolecular targets and calculate interaction energy maps, which can be transformed into pharmacophore characteristics. The other difference between 3D pharmacophore generation methods is based on the type of data used. This may be a set of active ligands, the structure data of a ligand complex with its macromolecular target, or the structure data of a single macromolecular target.

Figure 2. 3D-based pharmacophore models.( Schaller D,et al.2020)

Figure 2. 3D-based pharmacophore models.( Schaller D,et al.2020)

Services items

Project name Pharmacophore Model Construction Service
Services

MedAI can provide you with the following service but not limited to:

  • Pharmacophore Model Service without Protein Structure and without Ligand Structure
  • Pharmacophore Model Service with Known Protein and Ligand Structure
  • Ligand-based Pharmacophore Model Service
  • Receptor-based Pharmacophore Model Service
Product delivery mode The simulation results provide you with the raw data and analysis results of molecular dynamics.
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Advantages of Multi-target drugs

  • Multi-target drugs can effectively regulate the complex system of the entire cell.
  • Multi-target drugs do not completely eliminate the relationship between members in the signaling system.
  • Multi-target drugs can improve efficacy and improve safety, acting on multiple targets related to disease, producing multiple pharmacological activities, obtaining the required diverse biological regulation functions, and reducing side effects.

In the past decade, the field of computational multi-objective drug design has developed rapidly. MedAI can provide you with a variety of calculation methods. In most cases, multiple target combinations can be used to control the disease network. These multiple target combinations will enable researchers to choose targets that are easily regulated by small molecules while achieving the same level of network control. If you have any needs in this regard, please feel free to contact us.

Application of the pharmacophore model

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  • nomain-title-log-pic2 The pharmacophore model can be used to discover the key pharmacodynamic characteristics of drug molecules and establish a clear structure-activity relationship.
  • nomain-title-log-pic2 Virtual screening of the compound library through the pharmacophore method to discover new scaffold compounds that are active on the target.
  • nomain-title-log-pic2 Target fishing is carried out through pharmacophore groups to predict the pharmacological action spectrum of the compound. By using the pharmacophore model, the main adverse reactions in the early stage of drug development can be predicted, thereby reducing the possibility of drug development failure.

MedAI offers a corresponding pharmacophore model construction service. Our service provide you with accurate approximations of real molecular behaviors, and have proven to be very useful in understanding the biochemical basis of physiological events at different stages of drug development, even in different fields such as materials science. Our expert team can provide simulation time of up to one millisecond for the system of your choice, so you don't have to worry about technical issues. We can also analyze these results for you. If you have any needs in this regard, please feel free to contact us!

References

  • Schaller D , Dora Šribar, Noonan T , et al. Next Generation 3D Pharmacophore Modeling. Wiley Interdiplinary Reviews: Computational Molecular ence, 2020.

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