3D-QSAR refers to the application of force field calculations, requiring the three-dimensional structure of a given set of small molecules (training set) with known activities. The training set needs to be superimposed (aligned) by experimental data (based on ligand-protein crystallography) or molecular superposition software. It uses calculated potentials, such as Lennard-Jones potentials rather than experimental constants, but related to the entire molecule, rather than individual substituents. It studies the spatial field (the shape of the molecule) and the electrostatic field related to each other through partial least squares regression (PLS).
Figure 1.3D-QSAR.
Overall solutions
Molecule mining approaches is a special case of structured data mining approaches. It applies a similarity matrix based prediction or an automatic fragmentation scheme into molecular substructures. There are also other approaches using maximum common subgraph searches or graph kernels.
- Matched molecular pair analysis
Typical QSAR models derived from nonlinear machine learning are seen as a "black box," which fails to guide medicinal chemists. There is a relatively new concept of matched molecular pair analysis or prediction driven MMPA that is coupled with QSAR model to identify activity cliffs.
Our services
| Project name |
3D-QSAR Service |
| Samples requirements |
Our 3D-QSAR service requires you to provide specific requirements. |
| Detection cycle |
Decide according to your needs. |
| Service including |
We provide you with raw data and analysis service. |
| Price |
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For validation of QSAR models, usually various strategies are adopted:
- Internal validation or cross-validation (actually, while extracting data, cross validation is a measure of model robustness, the more a model is robust (higher q2) the less data extraction perturb the original model).
- External validation by splitting the available data set into training set for model development and prediction set for model predictivity check.
- Blind external validation by application of model on new external data.
- Data randomization or Y-scrambling for verifying the absence of chance correlation between the response and the modeling descriptors.
Our advantages
- Several databases have been prepared for pharmacophore screening and molecular docking.
- User can specify the desired database for us to prepare.
- The industry's unique integration of natural product physical molecular libraries from multiple international natural product suppliers.
- Cost of service: Cost-effective service plan can be tailored accordingly.
ComputaBio can offer you but not limited to:
ComputaBio' virtual screening service can significantly increase the hit rate of lead compounds and reduce the cost of later experimental screening. Virtual screening technology service is a personalized and customized innovative scientific research service. Each project needs to be evaluated before the corresponding analysis plan and price can be determined. If you want to know more about service prices or technical details, please feel free to contact us.
* It should be noted that our service is only used for research, not for clinical use.
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