3D-QSAR

Introduction of 3D-QSAR

In recent years, with the further development of structure-activity relationship theory and statistical methods, a diversity of three-dimensional quantitative structure-activity relationship (3D-QSAR) methods have emerged. 3D-QSAR is a technology that introduces three-dimensional structure information of molecules to carry out QSAR research. The theoretical basis of 3D-QSAR technology is that the active conformation of the compound molecule will affect its effect on the receptor and self-activity. This method indirectly reflects the non-bonded interaction characteristics between the molecules and biological macromolecules during the interaction process. 3D-QSAR are calculated starting from a geometrical or 3D representation of a molecule using multiple descriptors such as molecular surface, molecular volume and other geometrical properties.

Advantages of 3D-QSAR

• Most of the molecular descriptors of 2D-QSAR are used to describe the changes between atomic chemical bonds. However, when these molecular descriptors are not enough to accurately describe the molecular structure, field and space factors need to be considered. Therefore, 3D-QSAR technology using three-dimensional structure is applied to more accurately describe molecular structure.
• Compared with 2D-QSAR, the 3D-QSAR model is more appropriate and intuitive, and the structural information is more effective and accurate.
• In more advanced 3D QSAR methods, in addition to physical and geometric features of active drug molecules, quantum chemical features are also used.

Our 3D-QSAR Analysis Process

• Database search

The active molecules that bind to the desired drug target and their activities are identified.

Molecule mining approaches based on a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures are also available.

• Molecular features identification

Identify the structural or physicochemical molecular features (fingerprint) affecting biological activity such as bond, atom, functional group counts, surface area etc.

• 3D-QSAR models establishment

Build a 3D-QSAR between the biological activity and the identified features of the drug molecules.

• Validation of the QSAR biological activity prediction
• Use the 3D-QSAR model to optimize the known active compounds to improve the biological activity
• Test the new optimized drug molecule by carrying out experiments

Matched molecular pair analysis or prediction driven MMPA that is coupled with QSAR model is carried out to identify activity cliffs.

Figure 1. 3D-QSAR analysis results of TGFb inhibitory activities of DHPs. a) Predicted versus experimental inhibition of the trainings dataset (black circles, black line) and of the test dataset (red triangles). b) Statistical parameters of the final 3D-QSAR model. c) Alignment of training data set and Van-der-Waals and electrostatic contour maps for 3D-QSAR model. d) Correlation plot of calculated logP (clogP) versus TGFb inhibition. (Daniel, L.; et al. 2015)

Our Methodologies

There are various 3D-QSAR methods including molecular shape analysis (MSA), distance geometry (DG) and comparative molecular field analysis (CoMFA) methods. Among these 3D-QSAR methods, CoMFA method is currently the most mature and widely used method.

• CoMFA method

The basic principle of CoMFA is: if a group of similar compounds act on the same target in the same way, their biological activity will depend on the difference in the molecular field around each compound, which can reflect the non-bonded interaction  characteristic between the drug molecule and the target.

At Alfa Chemistry, the CoMFA method is performed in the following steps:

1) First, determine the active conformation of the drug molecule, and perform the superposition of the drug molecule according to a certain rule (usually frame superposition or field superposition).

2) Then, define a certain step size around the superimposed molecules to evenly divide to generate grid points, and use a probe ion on each grid point to evaluate the molecular field characteristics on the grid point (usually electrostatic field and three-dimensional field, sometimes including hydrophobic field and hydrogen bond field).

3) Finally, the relationship between compound activity and molecular field characteristics is established by partial least square method, and the equipotential energy surface of various molecular surfaces is given.

• Comparative molecular similarity indices analysis (CoMSIA)

CoMSIA method defines the characteristics of five molecular fields, including three-dimensional field, electrostatic field, hydrophobic field, and hydrogen bond field (including hydrogen bond donor field and hydrogen bond acceptor field). The results obtained under the spatial orientation are much more stable and we therefore use CoMSIA calculation to create a more satisfactory 3D-QSAR model.

Application of Our 3D-QSAR Services

• Predict the properties and activities of untested compounds
• Optimize the properties of the lead compound
• Evaluate the receptor binding site model
• Generate hypotheses related to the nature of receptor binding sites
• Synthesize and screen out sequence of priority compounds
• Determine the key structure necessary for ligands with high affinity to the receptor

Features of Our 3D-QSAR

• Various statistical methods with predictive models including PCA, PLS and SIMCA
• Hierarchical clustering of compounds based on properties
• A large number of built-in 3D descriptors and property calculators including EVA
• Automatic calculation of CoMFA and CoMSIA molecular fields
• Calculate or import custom descriptors
• Support multiple conformations of each molecule
• Automatic structure overlay for 3D-QSAR analysis

Alfa Chemistry's Advantages

• Fast screening speed and good versatility
• Fully consider both water and solvation effects
• High-performance server
• Compound database compliant with predefined filtering rules

3D-QSAR is used to design new molecules and to predict their bioactivity using the developed models. Our 3D-QSAR 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!

Reference

• Daniel, L.; et al. Design, synthesis and 3D-QSAR studies of novel 1,4-dihydropyridines as TGFβ/Smad inhibitors. European Journal of Medicinal Chemistry. 2015, 95: 249-266.