Editorial [Hot Topic: Computational Approaches in Medicinal Chemistry and Drug Discovery (Guest Editor: Dr. Fredy Sussman)]

The present CTMC issue entitled ‘Computational Approaches in Medicinal Chemistry and Drug Discovery’, covers a wide range of methodologies and applications that are having an increasing impact in the search for drug leads both in academic and pharmaceutical industry settings. These reviews deal with the wide gamut of methodologies used in this field ranging from the application of QSAR based techniques (see contributions by González-Diaz et al. and Volarath et al.) to receptor based binding predictions of Cavasotto and Orry, Villaverde et al. and Volarath et al. The latter algorithms have spanned the Structure-Based Virtual Screening (SBVS) of novel protein ligands, an in silico technique that can be used successfully in synergy with the wet lab High Throughput Screening protocols (HTS), in the search for drug leads with novel chemical moieties, as shown by Cavasotto and Orry. It is worth noticing that the computer assisted methods presented here could also have a bearing on a large number of disciplines (including proteomics) assisted and supported by the use of QSAR protocols based on topological indexes (see review by González-Diaz et al.). The drug targets are also well represented. They include the HIV-1 PR protease (see review by Volarath et al.), arguably the most successful receptor based rational lead design endeavor in the history of drug discovery. As it is well known, drugs based on inhibitors of this enzyme have made a huge impact in the life span and quality of life of AIDS patients. The experience gathered for an enzymatic target could be employed in targeting other enzymes of the same family. That has been the case of β-secretase (a member of the same protease family as the HIV-1 protease) which is crucial for the development of amyloid plaques in the brain of Alzheimer diseased patients. Although the inhibitor sequence specificities are quite different for both enzymes, similar kinds of isosteres, as those employed for HIV-1 PR, are currently being used for the design of β-secretase inhibitors. Based on our current involvement in a β-secretase inhibitor discovery program we reviewed this burgeoning field in the light of computer assisted approaches on drug discovery (see review by Villaverde et al.). No review issue on computer assisted drug design could be complete without a chapter on G-coupled protein receptors (GCPRs), one of the largest protein families whose functioning is activated by a wide range of external signals (odor, light, etc) as well as internal signals (ions, hormones, neurotransmitters, etc). These proteins are the target of about 40% of the prescribed drugs and of around 25% of the topselling drugs. GPCRs interact with an extraordinary diversity of ligands by means of their extracellular domains and/or the extracellular part of the transmembrane (TM) segment. Each receptor subfamily has developed specific sequence motifs to adjust the structural characteristics of its cognate ligands to a common set of conformational rearrangements of the TM segments near the G protein binding domains during the activation process. This adaptation has been achieved during evolution by customizing a preserved 7TM scaffold through conformational plasticity. Deupi et al. have contributed to this issue a review on this subject that helps to explain the functional versatility of these molecules. A large number of diseases can be explained nowadays in terms of protein aggregation. These include degenerative CNS diseases, some types of diabetes, etc. The group headed by Nussinov has been at the forefront of the computer assisted study of protein-protein interactions and has contributed many new insights to this field. In this issue Ma and Nussinov review their seminal work on the detection of residues that constitute hot spots for protein interaction and aggregation, a crucial issue in the design of drugs targeted against the formation of plaques that are thought to be the causal source of these diseases.