CzeekS: Rapid compound screening system based on CGBVS
Rapid compound screening system based on CGBVS
Ability to utilize in-house data using the CGBVS method
In Chemical Genomics-Based Virtual Screening (CGBVS*1,2,3), active compounds are predicted based on the binding patterns extracted from the interaction information (chemical genomics information) between proteins (biological space) and compounds (chemical space).
CzeekS enables pharmaceutical companies and research institutes to screen drug candidate compounds with high speed and accuracy using their own accumulated in-house assay data.
*1 Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol. Syst. Biol. 7, 472, 2011
*2 Systems biology and systems chemistry: new directions for drug discovery. Chem. Biol. 19(1), 23-8, 2012
*3 Unifying Bioinformatics and Bioinformatics and Computational Modeling, 99-120, 2011
- Perform screening calculations using compound-protein interaction machine learning method (CGBVS)
Provides a system to run the CGBVS on the command line. Enables high-speed in silico compound screening with high prediction accuracy.
- Enables compound screening via multi-target prediction
Scoring against multiple target proteins allows screening of compounds based on their selectivity.
- Enables target search for compounds
For each compound, a score calculation can be performed for all proteins included in the predictive model, which enables search for target proteins.
- Lineup of various prediction models
Eight protein families (GPCR, Kinase, Ion channel, Transporter, Nuclear receptor, Protease, Cytochrome P450, PPI) are available as standard predictive models.
- Ability to create predictive models by adding own data
Predictive model can be refined by adding own in-house assay data leading to improved prediction accuracy of the machine learning model.
- Support for multi-core processing (Parallel OpenMP)
Calculations can be performed faster when CzeekS is used on a machine with multi-core CPU.
CGBVS (Chemical Genomics Based Virtual Screening ) is a computational method developed by Professor Yasushi Okuno of Kyoto University's Graduate School of Pharmaceutical Sciences, which boasts of high speed and high prediction accuracy. Kyoto University has granted INTAGE Healthcare a license for the use the technology in its business.
System License Pricing
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The latest ChEMBL predictive model uses alvaDesc as the compound descriptor and PROFEAT 2016 or Multiple Sequence Alignment (MSA) as the protein descriptors. The standard model provided is created using data from the ChEMBL database. In addition to ChEMBL, the PPI model includes data from the TIMBAL database, which is a database dedicated to PPI data.
|Model||No. of Target proteins||No. of training data|
*Cytochrome and PPI models are offered since 2019.
List of proteins included in each predictive model.
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