The introduction of a new drug to the market is often the culmination of a long and arduous process of laboratory experimentation, lead-compound discovery, animal testing and preclinical and clinical trials -a process which can typically take as long as 10-15 years from hit to lead to marketable drug. On average, 9 out of 10 promising leads fail, often at an advanced stage in the drug discovery pipeline, because of adverse ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. One of the most attractive strategies for streamlining and accelerating the process of drug discovery is virtual high-throughput screening (VHTS), employing quantitative structure-activity/property relationships (QSAR/QSPR) modeling. The goal of QSAR/QSPR is the development of correlations between molecular structure and pharmaceutical properties, thereby transforming the search for compounds with specific properties, by use of chemical intuition and experience, into a mathematically quantified and computerized form. When a correlation between structure and activity/property is found and validated, any number of compounds from large pharmaceutical databases, including those not yet synthesized, can be virtually screened on the computer to select structures with the desired properties. Virtual screening using ADMET filters can eliminate compounds likely to have adverse side-effects, identifying the ''losers'' early in the process, to achieve the desired objective of ''fail early, fail cheaply''. The most promising compounds can then be chosen for laboratory synthesis and preclinical testing, thereby conserving resources and accelerating the process of drug discovery.
QSAR and QSPR have proved highly effective within homologous sets of molecules, as is apparent from the extensive literature on the subject [1, 2]. Traditional QSAR methods have not, however, been as successful when applied to more structurally diverse sets of data. This difficulty is partly because of the type of molecular property descriptors used and partly because of the complexity of chemistry space. Descriptors representing simple molecular properties were