QSAR Prediction

Our Services

The early linear free-energy approaches developed by Hansch and Free-Wilson have provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure-activity relationships (QSAR). QSAR prediction methods are attempting to predict the toxicological endpoint and breadth of mechanisms which are complicated. The quality and quantity of the available biological toxicology data could be another obstacle in the modeling process.
Application of QSAR Prediction
Over recent years QSAR techniques have been applied to a wide variety of toxicological endpoints from the prediction of LD50 and maximum tolerated dose (MTD) values to Salmonella typhimurium (Ames) assay results, carcinogenic potential and developmental toxicity effects. However, toxicological endpoints such as carcinogenicity, reproductive effects and hepatotoxicity are mechanistically ill-defined leading to added complexity when trying to predict these endpoints. And QSAR prediction can be used to help identify missing comparison values in a substance’s database.
Available QSAR Prediction Services
  • 1D-QSAR: correlating molecular activity with molecular properties like pKa, log P, etc.
  • 2D-QSAR: correlating activity with structural 2D patterns like connectivity indices, 2D-pharmacophores.
  • 3D-QSAR: correlating activity with non-covalent interaction fields surrounding the molecules.
  • 4D-QSAR: additionally including ensemble of ligand configurations in 3D-QSAR.
  • 5D-QSAR: explicitly representing different induced-fit models in 4D-QSAR.
  • 6D-QSAR: further incorporating different solvation models in 5D-QSAR.
Our Services
The computer-assisted prediction tools already today play a complementary role in the toxicological repertoire for the assessment of chemicals. Lead Sciences has developed several QSAR techniques to help you obtain the best QSAR models for hit to lead process. Lead Sciences streamlines the organization of QSAR datasets, QSAR models, and QSAR predictions. With our one-stop service, you can work more efficiently and effectively. For more detailed information, please feel free to contact us.
References:
  1. Greene, N. (2002). Computer systems for the prediction of toxicity: an update. Advanced Drug Delivery Reviews, 54(3), 417-431.
  2. Demchuk, E., Ruiz, P., Chou, S., & Fowler, B. A. (2011). SAR/QSAR methods in public health practice. Toxicology and applied pharmacology, 254(2), 192-197.
  3. Simon-Hettich, B., Rothfuss, A., & Steger-Hartmann, T. (2006). Use of computer-assisted prediction of toxic effects of chemical substances. Toxicology, 224(1-2), 156-162.