Editorial [Hot Topic: Structure-Based Virtual Ligand Screening (Guest Editor: Bruno O. Villoutreix)]

The last two decades have witnessed the dawn of a new era of “in silico-based” biology. These methods have been playing a major role, from investigation of the genomes to the design of new therapeutic compounds or prediction of protein structures. Lately, as computer technology has become cost-effective, new ideas and concepts have emerged, such as in silico virtual ligand screening with considerations of ligand and/or receptor flexibility. These computer-assisted drug discovery methods have been important in the past and are now part of most drug discovery campaigns. There are many reasons for that, for instance, Structural Genomics projects have enabled the determination of many high quality protein structures, and several of them are indeed potential drug targets. Further, parallel synthesis allows for the production of millions of “drug-like” molecules, thus potential drug candidates that could obstruct active sites, impede macromolecular interactions or induce conformational changes. In addition, Genome Projects have identified over 10,000 targets believed to be involved in the pathogenesis of diseases; some of these targets should be investigated rapidly as there obviously remains a significant number of unmet clinical needs in many disease indications. Many scientists around the World believe that, to use available data most effectively for drug discovery projects, it is essential to develop/apply reliable in silico high throughput screening methods. Thus, in 2005, I thought that it could be interesting to compile an issue about in silico screening methods based on knowledge about the 3D structure of protein targets. I am now delighted to present in this issue of Current Protein and Peptide Science review papers pertaining to the continuously evolving field of in silico screening and drug design. The contributions cover a broad range of topics, from pocket definition to docking, scoring and applications to homology models. There should be something to interest everyone who is involved with drug design. Because of the freedom of style and subject matter afforded to the contributors, it was felt necessary to open this issue with an introduction to the field. Thus, in my laboratory, we wrote a review introducing structure-based in silico screening and it is my hope that we have met this requirement in full. Then, Laurie and Jackson provide a highly readable introduction to pocket prediction while Jain describes the science (art) of scoring including his new approach as integrated in Surflex. Zsoldos et al. report for the first time on the exciting use of eHiTS and compare their approach with other tools. To the end of this issue, Rockey and Elcock discuss the use of homology models for receptor-based in silico screening with a special emphasis on the protein kinase family. I believe that this special issue contains much valuable information for protein scientists engaged in drug discovery campaigns. Not all topics could be covered but the readers should be able to find additional information in the references cited in each review article. I would like here again to thank the contributors for their work and the reviewers for their suggestions. It has been great pleasure to put this issue together. I am grateful to Dr. Maria Miteva (Inserm, Paris, France), Dr. Frederic Cazals (Inria, Sophia-Antipolis, France), Dr. Ajay Jain (San Francisco, USA), Dr. Xueliang Fang (Ann Arbor, USA), Dr. Wen Lee (Oxford, UK) and Dr. Anthony Nicholls (OpenEye, USA) for comments about this special issue. Finally, last but not least, I would like to give special thanks to Dr. Ben Dunn and Mr. M. Ilyas (Senior Manager Publications, Bentham Science Publishers Ltd.) for helping me bringing this special issue to completion.