The Electrotopological State: Structure Modeling for QSAR and Database Analysis

Abstract

Today chemists are blessed with the availability of large numbers of molecular structures to consider and faced with the challenge of analyzing the plethora of structures and experimental data. Combinatorial chemistry has caused an explosion in the drug design scene, making available huge numbers of compounds for lead discovery. Earlier strategies only per­ mitted limited numbers because compounds were synthesized one-at-atime. D ata sets for quantitative structure-activity analyses (QSAR) were counted in the dozens at most. Suddenly, with this new technology, it is possible to prepare and test (with high throughput screening) hundreds, even thousands of compounds in a very short time. This vast amount of information is worthless unless there is some systematic and relatively simple way of identifying and categorizing the molecules synthesized and tested. The development of useful structure-activity models and their exploitation is the essential sequel to combinatorial chemistry. The models are necessary for the rational prediction of candidate compounds for more detailed analyses. The generation of this vital information depends upon the availability of methods to represent structure, both at the molecular and atoms-in-molecule level. These methods should be non-empirical, coherent, and simple to use at high speed. This kind of information is also of great importance for the design and evaluation of new combinatorial libraries both real and virtual. The electrotopological state index is an ideal candidate for the applications needed to maximize the value of combina­ torial chemistry and to create QSAR models.