Prediction of ADMET Properties.
This Review describes some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties. The article also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intension of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development.
Norinder
Ulf
U
AstraZeneca Research and Development Södertälje, Södertälje, Sweden. ulf.norinder@astrazeneca.com
Bergström
Christel A S
CA
D000818:Animals; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual; D023281:Genomics; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010599:Pharmacokinetics; D010600:Pharmacology / Q000379:methods* / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D040901:Proteomics; D014116:Toxicology / Q000379:methods* / Q000706:statistics & numerical data
D004364:Pharmaceutical Preparations