Abstract—‘OMICS’ techniques have deeply changed the drug discovery process. The availability of many different potential druggable genes, generated by these new techniques, have exploited the complexity of new lead compounds screening. ‘Virtual screening’, based on the integration of different analytical tools on high performance hardware platforms, has speeded up the search for new chemical entities suitable for experimental validation. Docking is a key step in the screening process. The aim of this paper is the evaluation of binding differences due to solvation. We have compared two commonly used software, one of which takes into account solvation, on a set of small molecules (Morpholines, flavonoids and imidazoles) which are able to target the RAC1 protein - a cardiovascular target. We have evaluated the degree of agreement between the two different programs using a machine learning approach combined with statistical test. Our analysis, on a sample of small molecules, has pointed out that 35% of the molecules seem to be sensitive to solvation. This result, even though quite preliminary, stresses the need to combine different algorithms to obtain a more reliable filtered set of ligands. I. INTRODUCTION HE investigation of the molecular mechanism of the diseases (Molecular Medicine) is one of the major challenges in the post-genome era. From a pharmacological point of view the identification of genes, involved in a specific pathology, (markers) is an essential step. In addition, is it also important to elucidate the functional interactions among them. From a molecular point of view a disease is characterized by altered genomic and proteomic profiles [1]. Statistical analysis allows to determine the set of critical gene or proteins in a panel of samples. ‘Omics’ has heavily impacted the drug discovery process. The high throughput screening method, developed during the sequencing projects, has increased the capability to screen, in a parallel way, thousand potential targets. Despite the availability of all these new technologies, the productivity, as new potential drugs, of pharmaceutical companies is declined. The identification of new chemical compounds suitable for high throughput pharmacological screening has become computationally intensive. The chemoinformatic screening is also limited by data accessibility because many Manuscript received April 15, 2011. Patrizio Arrigo is with the CNR Institute of Macromolecular Studies (ISMAC), Section of Genoa, Via De Marini 6, 16149 Genoa, Italy (E-mail: arrigo@ge.ismac.cnr.it). All other authors are with the Department of Communication, Computer and System Sciences, Nanobiotechnology and Medical Informatics Laboratory University of Genoa, Via all’Opera Pia, 13, 16145 Genoa, Italy (*corresponding author, phone: +390103532991; fax: +390103532154; E- mail: carmel@dist.unige.it). compounds are under copyright and there not information about them. In the last decade the Computer Aided Drug Design (CADD) has increase its complexity including genetic information in the screening process (pharmacogenetics). In the past the role of genetic variability was not considered, now the effect of genetic variability on drug efficacy is a well-established knowledge. In many cases different proteins forms, associated to a slight variation at genomic level, can be targeted by a drug in a more or less efficient way. On the other site a drug can induce differential gene expressions. The evaluation of genomic modification, triggered by drugs, is the aim of pharmacogenomics. It is quite obvious that, for virtual screening, pharmacogenetic knowledge has a critical influence. The identification of new drug targets requires the integration of bioinformatics and chemoinformatics with experimental results. These data are the knowledge background for the QSAR (Quantitative Structure-activity Relationship) modelling. At higher level the final step of the discovery process is related to the capability to evaluate the efficacy by the ADME (Adsorbtion Diffusion Metabolism and Excretion) simulation. In order to improve the performance of target identification and design of new drugs, bioinformatics and chemoinformatics tools have to be integrated. The use of data mining techniques related to gene expression level during FP6 Cardioworkbench project (www.cardioworkbench.eu) has highlighted the role of Rac1 protein in cardiovascular diseases [2]. In particular Rac1 protein is implicated in several events of atherosclerotic plaque development and represents a new potential pharmacological target for cardiovascular diseases. Rac1 is member of the Rho family. This protein family serve as conformational switches in a wide variety of signal transduction pathways. It is a key regulators of angiogenesis, modulating a diversity of cellular processes, including vascular permeability, extracellular matrix remodeling, migration, proliferation, morphogenesis, and survival [3]. Rac1 plays a central role in endothelial cell migration, tubulogenesis, adhesion, and permeability in response to vascular endothelial growth factor (VEGF) and sphingosine- 1-phosphate (S1P), which is likely due to the inability of Rac1-deficient endothelial cells to form lamellipodial structures and focal adhesions, and to remodel their cell-cell contacts [4]. It is been demonstrate that the activation of Rac1, but not RhoA, in human aortic smooth muscle cells (SMCs) through the engagement of Į2ȕ1 integrin by type I collagen induces the expression of matrix metalloproteinase 1 (MMP1) and MMP2, an event that may contribute to atherosclerotic plaque rupture [2]. Drug Design For Cardiovascular Disease: The Effect Of Solvation Energy On Rac1-Ligand Interactions Norbert Maggi, Student Member, IEEE, Patrizio Arrigo, and Carmelina Ruggiero, Member, IEEE T 978-1-4244-4122-8/11/$26.00 ©2011 IEEE 3237 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, August 30 - September 3, 2011