ADMET properties computational prediction

Clark, D.E. and Grootenhuis, P.D. Progress in computational methods for the prediction of ADMET properties. Curr. Opin. Drug. Disc. Dev. 2002, 5, 382-390. 

Clark, D.E. Computational prediction of ADMET properties Recent developments and future challenges. Ann. Reports Comp. Chem. 2005,1,133-51. 

Computational alerts can be extended from having rules that predict absorption properties only to customized rules that include distribution and toxicity. Here, we will consider in silico models for four types of toxicity. When the hits in the ADM E rules are combined with additional rules for estimated toxicity, we generate a more general computational ADMET Risk. ... 

The most appropriate and widely used method for extracting information from large data sets is QSAR and its relatives, quantitative structure-property relationships (QSPR) for property modeling, and quantitative structure-toxicity relationships (QSTR) for toxicity modeling. QSAR is a simple, well validated, computationally efficient method of modeling first developed by Hansch and Fujita several decades ago (30). QSAR has proven to be very effective for discovery and optimization of drug leads as well as prediction of physical properties, toxicity, and several other important parameters. QSAR is capable of accounting for some transport and metabolic (ADMET) processes and is suitable for analysis of in vivo data. 

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