Applications of 2D Descriptors in Drug Design: A DRAGON Tale

Aliuska   Morales   Helguera; Robert   D.   Combes; Maykel   Perez   Gonzalez; M. Natalia   D.S.   Cordeiro

doi:10.2174/156802608786786598

Abstract

In order to minimize expensive drug failures, is essential to determine potential activity, toxicity and ADME problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of potential drug is advisable even before synthesis using computational techniques such as QSAR modeling. A great number of in silico approaches to activity/toxicity prediction have been described in the literature, using molecular 0D, 1D, 2D and 3D descriptors. Also these descriptors have been implemented in available computational tools such as DRAGON, SYBYL and CODESSA for it easy use. However, many of them only have been used to explain a few prediction problems. This review attempts to summarize present knowledge related to the computational biological activity prediction based in 2D molecular descriptors implemented in the DRAGON software. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse. Several topological molecular descriptors applications are described, ranging from simple topological indices to topological indices derived from matrices weighted with atomic and bond properties. Their advantages, limitations and its possibilities in drug design are also discussed.

Keywords: DRAGON software, 2D-descriptors, drug design, enzymatic inhibition, receptors affinity, anticancer, antimicrobial, ADME/Tox