Computation of the physio‐chemical properties and data mining of large molecular collections

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Abstract
Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD).* In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug‐like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a 1D representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood–brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process. © 2002 Wiley Periodicals, Inc. J Comput Chem 23: 172–183, 2002