Collaborative Science

Accelerating time to market and driving innovation with collaboration, knowledge based under-standing and prediction.

QSAR, ADMET and Predictive Toxicology

Understanding and quantifying structure-activity relationships can significantly impact lead optimization and drug development by minimizing tedious and costly experimentation.

  • Ligand and data set preparation
    • Generate training and test datasets with diverse splitting methods
    • Prepare response property (scaling and binning)
    • Calculate numerous physicochemical, topological, electronic, geometric. fingerprint and Quantum Mechanics based descriptors
  • Generate and study SAR models
    • Create advanced statistical models including Bayesian models, MLR (Multiple Linear Regression), PLS (Partial Least Squares), GFA (Genetic Functional Analysis), and NN (Neural Networks)
    • Analyze and validate models using model applicability domains (MAD), automatic test set validation, cross validation and statistical metrics
    • Identify Matched Molecular Pairs (MMPs) transformations and study activity cliffs

ADMET Descriptors

Get an early assessment of your compounds by calculating the predicted absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for collections of molecules such as synthesis candidates, vendor libraries, and screening collections. Use the calculated results to eliminate compounds with unfavorable ADMET characteristics and evaluate proposed structural refinements, designed to improve ADMET properties prior to synthesis. ADMET descriptors include:

  • Human intestinal absorption
  • Aqueous solubility
  • Blood brain barrier penetration
  • Plasma protein binding
  • CYP2D6 binding
  • Hepatotoxicity
  • Filter sets of small molecules for undesirable function groups based on published SMARTS rules

Predictive Toxicology

Evaluate your compounds’ performance in experimental assays and animal models. Compute and validate assessments of the toxic and environmental effects of chemicals solely from their molecular structure. TOPKAT (TOxicity Prediction by Komputer Assisted Technology) employs robust and cross-validated Quantitative Structure Toxicity Relationship (QSTR) models for assessing various measures of toxicity and utilizing the patented Optimal Predictive Space validation method to assist in interpreting the results.

  • Ames mutagenicity
  • Rodent carcinogenicity (NTP and FDA data)
  • Weight of evidence carcinogenicity
  • Carcinogenic potency TD50
  • Developmental toxicity potential
  • Rat oral LD50
  • Rat maximum tolerated dose
  • Rat inhalation toxicity LC50
  • Rat chronic LOAEL
  • Skin irritancy and sensitization
  • Eye irritancy
  • Aerobic biodegradability
  • Fathead minnow LC50
  • Daphnia magna EC50
  • Log P

Read the Discovery Studio QSAR, ADMET and Predictive Toxicology Datasheet