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ADME PK

chemTarget™

Predicts biological target interaction directly from chemical structure

  • Predicts binding affinity, inhibition constants or other measures of interaction with biological targets, directly from chemical structure.
  • Uses Cyprotex’s unique pattern recognition software to build models from existing data sets (provided by the customer or from the literature).
  • Analyzes approximately 10,000 descriptors using linear, random forest, neural network and nearest neighbor methods.
  • Provides clinically relevant binding/inhibition/activation when used in combination with the pharmacokinetic predictor, chemPK™.
  • Provides an early-stage filter for directing chemistry and prioritizing screening.
A virtual screening tool for predicting in vitro biological target interaction from chemical structure alone.

 

chemTarget™ Input Requirements
  • Chemical structure, e.g., SMILES, mol or sdf.
  • Locations of in vivo target expression.
  • Existing target interaction data (e.g. IC50, AC50 or Ki).
chemTarget™ Data Delivery
  • Predicted target interaction.
  • Predicted engagement in vivo for specified dose-regimen(s) (minimum, maximum, average) if used in combination with chemPK™.
 

How does it work?

Drug interaction with a target in vivo depends on strength of interaction with the target and concentration of the drug at the target binding site. Models produced by chemTarget™ predict the strength of interactions, whilst chemPK™ can be used to predict drug concentrations in organs and tissues. Together, these technologies enable structure-based screening of in vivo target engagement.

chemTarget - receptor interaction prediction from chemical structure
Figure 1
Schematic illustrating how chemTarget can be integrated with chemPK™ to predict clinically relevant biological target binding affinity.

Data

Data from chemTarget™

In silico recepter interaction prediction

RMSE* 0.68
R2 0.76
Spearman rank correlation coefficient 0.85
*RMSE = root mean square error

Figure 2
Prediction of JNK3 binding affinity from chemical structure. Results are from 10 repeats of 10-fold cross-validation for a set of 697 compounds.

JNK3 is a potential therapeutic target for several neurodegenerative disorders. chemTarget™ predicts JNK3 inhibition directly from structure with a repeated cross-validation R2 of 0.76 for a set of 697 compounds.
In silico receptor interaction prediction

RMSE* 0.64
R2 0.72
Spearman rank correlation coefficient 0.85
*RMSE = root mean square error

Figure 3
Prediction of MK2 binding affinity from chemical structure. Results are from 10 repeats of 10-fold cross-validaton using a set of 670 compounds.

MK2 (mitogen-activated protein kinase (MAPK)-activated protein kinase 2) is a potential therapeutic target in inflammatory disease. chemTarget™ predicts MK2 inhibition directly from structure with a repeated cross-validation R2 of 0.72 for a set of 670 compounds.

Contact enquiries@cyprotex.com to discuss your project.

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Europe: +44 1625 505100

 

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