The search for new compounds with a given biological activity requires enormous effort in terms of manpower and cost. This effort arises from the large number of compounds that need to be synthesized and subsequently biologically evaluated. For this reason the pharmaceutical industry has shown great interest in theoretical methods that enable the rational design of pharmaceutical agents. In the last years bioinformatics has experienced a great evolution due to the development of specialized software and to the increasing computer power. The codification of the structural information of molecules through molecular descriptors and the subsequent data analysis allow establishing QSAR models (Quantitative Structure-Activity Relationship) that can be applied to the design and the virtual screening of new drugs. The development of sophisticated Docking methodologies also allows a more accurate predict of the biological activity of molecules. Moreover, through this type of computational techniques and theoretical approaches, it is possible to develop explanatory hypothesis on the mechanism of action of drugs. This work provides a brief description of a series of studies implemented in the software MOE (Molecular Operating Environment) with particular attention to the medicinal chemistry aspects.

Background Drug–drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very ch

The numerical encoding of chemical structure with Topological Indices (TIs) is currently growing in importance in Medicinal Chemistry and Bioinformatics. This approach allows the rapid collection, annotation, retrieval, comparison and mining of chemical structures within large databases. TIs can subsequently be used to seek quantitative structure-activity relationships (QSAR), which are models connecting chemical structure with biological activity. In the early 1990s, there was an explosion in the introduction and definition of new TIs. The Handbook of Molecular Descriptors by Todeschini and Consonni lists more than 1500 of these indices. At the end of the last century, researchers produced a large number of TIs with essentially the same advantages and/or disadvantages. Consequently, many researchers abandoned the definition of TIs for a time. In our opinion, one of the problems associated with TIs is that researchers aimed their efforts only at the codification of chemical connectivity for small-sized drugs. As a consequence, recently it seems that we have arrived at “Fukuyamas End of History in TIs definition”. In the work described here, we review and comment on the “quo vadis” and challenges in the definition of TIs as we enter the new century. Emphasis is placed on new chiral TIs (CTIs), flexible TIs for unifying QSAR models with multiple targets, topographic indices (TPGIs), TIs for DNA and protein sequences, TIs for 2D RNA structures, TPGIs and drug-protein or drug-RNA quantitative structure-binding relationship (QSBR) studies, TIs to encode protein surface information and TIs for protein interaction networks (PINs).

Combinatorial drug discovery will rely on new approaches to analyzing highly dimensional data resources.

Objective Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of phar