Structure- and Ligand-Based Design of Novel Antimicrobial Agents

The use of computer based techniques in the design of novel therapeutic agents is a rapidly emerging field. Although the drug-design techniques utilized by Computational Medicinal Chemists vary greatly, they can roughly be classified into structure-based and ligand-based approaches. Structure-based methods utilize a solved structure of the design target, protein or DNA, usually obtained by X-ray or NMR methods to design or improve compounds with activity against the target. Ligand-based methods use active compounds with known affinity for a target that may yet be unresolved. These methods include Pharmacophore-based searching for novel active compounds or Quantitative Structure-Activity Relationship (QSAR) studies. The research presented here utilized both structure and ligand-based methods against two bacterial targets: Bacillus anthracis and Mycobacterium tuberculosis. The first part of this thesis details our efforts to design novel inhibitors of the enzyme dihydropteroate synthase from B. anthracis using crystal structures with known inhibitors bound. The second part describes a QSAR study that was performed using a series of novel nitrofuranyl compounds with known, whole-cell, inhibitory activity against M. tuberculosis. Dihydropteroate synthase (DHPS) catalyzes the addition of p-amino benzoic acid (pABA) to dihydropterin pyrophosphate (DHPP) to form pteroic acid as a key step in bacterial folate biosynthesis. It is the traditional target of the sulfonamide class of antibiotics. Unfortunately, bacterial resistance and adverse effects have limited the clinical utility of the sulfonamide antibiotics. Although six bacterial crystal structures are available, the flexible loop regions that enclose pABA during binding and contain key sulfonamide resistance sites have yet to be visualized in their functional conformation. To gain a new understanding of the structural basis of sulfonamide resistance, the molecular mechanism of DHPS action, and to generate a screening structure for high-throughput virtual screening, molecular dynamics simulations were applied to model the conformations of the unresolved loops in the active site. Several series of molecular dynamics simulations were designed and performed utilizing enzyme substrates and inhibitors, a transition state analog, and a pterin-sulfamethoxazole adduct. The positions of key mutation sites conserved across several bacterial species were closely monitored during these analyses. These residues were shown to interact closely with the sulfonamide binding site. The simulations helped us gain new understanding of the positions of the flexible loops during inhibitor binding that has allowed the development of a DHPS structural model that could be used for high-through put virtual screening (HTVS). Additionally, insights gained on the location and possible function of key mutation sites on the flexible loops will facilitate the design of new, potent inhibitors of DHPS that can bypass resistance mutations that render sulfonamides inactive. Prior to performing high-throughput virtual screening, the docking and scoring functions to be used were validated using established techniques against the B. anthracis DHPS target. In this validation study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, as well as nine scoring functions, were evaluated for their utility in virtual screening against the novel pterin binding site. Their performance in ligand docking and virtual screening against this target was examined by their ability to reproduce a known inhibitor conformation and to correctly detect known active compounds seeded into three separate decoy sets. Enrichment was demonstrated by calculated enrichment factors at 1% and Receiver Operating Characteristic (ROC) curves. The effectiveness of post-docking relaxation prior to rescoring and consensus scoring were also evaluated. Of the docking and scoring functions evaluated, Surflex with SurflexScore and Glide with GlideScore performed best overall for virtual screening against the DHPS target. The next phase of the DHPS structure-based drug design project involved high-throughput virtual screening against the DHPS structural model previously developed and docking methodology validated against this target. Two general virtual screening methods were employed. First, large, virtual libraries were pre-filtered by 3D pharmacophore and modified Rule-of-Three fragment constraints. Nearly 5 million compounds from the ZINC databases were screened generating 3,104 unique, fragment-like hits that were subsequently docked and ranked by score. Second, fragment docking without pharmacophore filtering was performed on almost 285,000 fragment-like compounds obtained from databases of commercial vendors. Hits from both virtual screens with high predicted affinity for the pterin binding pocket, as determined by docking score, were selected for in vitro testing. Activity and structure-activity relationship of the active fragment compounds have been developed. Several compounds with micromolar activity were identified and taken to crystallographic trials. Finally, in our ligand-based research into M. tuberculosis active agents, a series of nitrofuranylamide and related aromatic compounds displaying potent activity was investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation and high internal validity (cross validated r2 \u3e .5) were developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents

Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae.

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps

Crystal Structure of the 6-Hydroxymethyl-7,8-Dihydropterin Pyrophosphokinase•Dihydropteroate Synthase Bifunctional Enzyme from Francisella tularensis

The 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (HPPK) and dihydropteroate synthase (DHPS) enzymes catalyze sequential metabolic reactions in the folate biosynthetic pathway of bacteria and lower eukaryotes. Both enzymes represent validated targets for the development of novel anti-microbial therapies. We report herein that the genes which encode FtHPPK and FtDHPS from the biowarfare agent Francisella tularensis are fused into a single polypeptide. The potential of simultaneously targeting both modules with pterin binding inhibitors prompted us to characterize the molecular details of the multifunctional complex. Our high resolution crystallographic analyses reveal the structural organization between FtHPPK and FtDHPS which are tethered together by a short linker. Additional structural analyses of substrate complexes reveal that the active sites of each module are virtually indistinguishable from those of the monofunctional enzymes. The fused bifunctional enzyme therefore represents an excellent vehicle for finding inhibitors that engage the pterin binding pockets of both modules that have entirely different architectures. To demonstrate that this approach has the potential of producing novel two-hit inhibitors of the folate pathway, we identify and structurally characterize a fragment-like molecule that simultaneously engages both active sites. Our study provides a molecular framework to study the enzyme mechanisms of HPPK and DHPS, and to design novel and much needed therapeutic compounds to treat infectious diseases

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

IN SILICO STUDY OF CEPHALOSPORIN DERIVATIVES TO INHIBIT THE ACTIONS OF Pseudomonas aeruginosa

Studi In Silico Senyawa Turunan Sefalosporin dalam Menghambat Aktivitas Bakteri Pseudomonas aeruginosa Infeksi yang diakibatkan oleh bakteri gram-negatif, seperti Pseudomonas aeruginosa telah menyebar luas di seluruh dunia. Hal ini menjadi ancaman terhadap kesehatan masyarakat karena merupakan bakteri yang multi-drug resistance dan sulit diobati. Oleh karena itu, pentingnya pengembangan agen antimikroba untuk mengobati infeksi semakin meningkat dan salah satu yang saat ini banyak dikembangkan adalah senyawa turunan sefalosporin. Penelitian ini melakukan studi mengenai interaksi tiga dimensi (3D) antara antibiotik dari senyawa turunan Sefalosporin dengan penicillin-binding proteins (PBPs) pada P. aeruginosa. Tujuan dari penelitian ini adalah untuk mengklarifikasi bahwa agen antimikroba yang berasal dari senyawa turunan sefalosporin efektif untuk menghambat aktivitas bakteri P. aeruginosa. Struktur PBPs didapatkan dari Protein Data Bank (PDB ID: 5DF9). Sketsa struktur turunan sefalosporin digambar menggunakan Marvins Sketch. Kemudian, studi mengenai interaksi antara antibiotik dan PBPs dilakukan menggunakan program Mollegro Virtual Docker 6.0. Hasil yang didapatkan yaitu nilai rerank score terendah dari kelima generasi sefalosporin, di antaranya sefalotin (-116.306), sefotetan (-133.605), sefoperazon (-160.805), sefpirom (-144.045), dan seftarolin fosamil (-146.398). Infections caused by gram-negative bacteria, such as Pseudomonas aeruginosa, have been spreading worldwide. It is a threat to public health because of its multi-drug resistance and difficulty to treat. Therefore, the demand for developing antimicrobial agents to treat infections is increasing. One of them that is currently under development is cephalosporin derivative compounds. This research studied the three-dimensional (3D) interaction between antibiotics from cephalosporin derivatives and penicillin-binding proteins (PBPs) in P. aeruginosa. This study aimed to clarify whether the cephalosporin derivatives were effective in inhibiting the activity of P. aeruginosa. The PBPs structure was obtained from the Protein Data Bank (PDB ID: 5DF9). The structural sketch of the cephalosporin derivative was drawn using the Marvins Sketch, whereas the study on the interaction between antibiotics and PBPs was carried out using the Mollegro Virtual Docker 6.0 program. The results showed the lowest rerank score from five cephalosporin derivatives, namely cephalotin (-116,306), cephotetan (-133.605), cephoperazone (-160.805), cephpirome (-144.045), and cephtaroline fosamil (-146.398)

Computational Studies and Design of PPARγ and GLUT1 Inhibitors

The peroxisome proliferator-activated receptor gamma (PPARγ) is a ligand-dependent transcription factor of the nuclear receptor superfamily that controls the expression of a variety of genes involved in fatty acid metabolism, adipogenesis, and insulin sensitivity. PPARγ is a target for insulin-sensitizing drugs, and it plays a significant function in prostate cancer. PPARγ antagonists have anti-proliferative effects in a broad range of hematopoietic and epithelial cell lines. The ligand binding domain (LBD) of PPARγ is large and has orthosteric and allosteric binding sites. Several PPARγ-ligand co-crystal structures show two bound molecules, one to the orthosteric pocket and a second to the allosteric site. We ran docking studies against the orthosteric and allosteric binding sites to determine the most favorable binding site for PPARγ antagonists. We found that Glide docking performed well in predicting PPARγ antagonist binding affinities, and that the allosteric site of PPARγ was the most favorable binding site for antagonists. We also investigated PPARγ ligand-protein interactions to better define a structural basis for the binding selectivity of PPARγ antagonists. We found that Phe282, Arg288, and Lys367 interact with antagonists more than with agonists and partial agonists. We then identified several potential PPARγ antagonists by virtual screening of the PPARγ allosteric pocket. The glucose transporter 1 (GLUT1) is a uniporter protein that facilitates the transport of glucose across the plasma membranes of mammalian cells. As GLUT1 is overexpressed in numerous tumors, this transporter is a potential target for cancer treatment. GLUT1 works through conformational switching from an outward-open (OOP) to an inward-open (IOP) conformation passing through an occluded conformation. We sought to determine which conformation is favored for ligand binding by molecular docking studies of known GLUT1 inhibitors with the different GLUT1 conformers. Our data revealed that the IOP is the preferred conformation and that residues Phe291, Phe379, Glu380, Trp388, and Trp412 may play important roles in ligand binding to GLUT1. To identify new chemotypes targeting GLUT1, we built a pharmacophore model and searched against an NCI compound database. Sixteen hit molecules with good docking scores were screened for GLUT1 inhibition and anti-proliferative activities. From these, we identified four compounds that inhibited cell viability in an HCT116 colon cancer cell line. We also determined that one of these, NSC295720, inhibited GLUT1 in a biochemical assay

Optimization of Clustering and Database Screening Procedures for Cavbase and Virtual Screening for Novel Antimalarial and Antibacterial Molecules

Im Zyklus der rationellen Arzneimittelentwicklung werden Affinität und Selektivität von potentiellen Wirkstoffen intensiv erforscht. Da diese beiden Eigenschaften keine lineare Abhängigkeit zueinander aufweisen, führt höhere Affinität nicht gezwungenermaßen auch zu einer höheren Selektivität. Computer-basierte Verfahren spielen eine immer größere Rolle für die Analyse und Vorhersage von Selektivitätsprofilen. Da die meisten erfolgreich eingesetzten niedermolekularen Arzneistoffe in Vertiefungen auf Proteinoberflächen binden, spielen physiko-chemische Eigenschaften von Bindetaschen eine zentrale Rolle in der Erkennung und damit auch der Bindung von Liganden. Cavbase ist eine Methode, die es ermöglicht Bindetaschen anhand der physiko-chemischen Eigenschaften dort exponierter Aminosäuren zu beschreiben und unabhängig von ihrer Proteinsequenz und Faltungsgeometrie zu vergleichen. Die Bindetaschen-basierte Klassifizierung von Proteinen ist ein effektiver Ansatz, um relevante Informationen für Selektivitätsanalysen zu extrahieren, die durch Anwendung von Clustermethoden erreicht werden kann. In der vorliegenden Arbeit wurde ein neuartiger Arbeitsablauf zur Untersuchung von wichtigen Parametern einer Clusterung entwickelt. Für einen Datensatz von Proteinen wird eine Ähnlichkeitsmatrix berechnet und anschließend dem entwickelten Arbeitsablauf übergeben. Dieser Ansatz wurde erfolgreich an zwei unterschiedlichen Datensätzen getestet. Die vorhergesagte Anzahl der Cluster, die am besten geeignete Clustermethode und die anschließende Clusterstruktur waren in Übereinstimmung mit den Referenzklassifikation der Proteine. Im Falle der Protease-Proteinfamilie führte die Bindetaschen-basierte Klassifizierung zur einer signifikanten Gruppierung von Proteineinträgen, die unabhängig von Sequenzinformation entstanden. Damit konnte auf struktureller Ebene die Kreuzreaktivität zwischen dem Protein Calpain-1 und Cysteincathepsinen detektiert werden, die bis jetzt nur auf Basis von Liganddaten beschrieben wurde. Im weiteren Verlauf wurden elf unterschiedliche Serinproteasen untersucht, indem die Topologie der Liganden, Bindetaschen- und Sequenzinformationen miteinander verglichen wurden. Die entstandenen Cluster zeigen einen Korrelationstrend zwischen der Ähnlichkeit im Liganden- und Bindetaschenraum. Eine steigende Anzahl von Resistenzen auf derzeitig angewandte antiparasitäre und antibakterielle Arzneistoffe erfordert die Entwicklung neuartiger Antiinfektiva. Für den Parasiten Plasmodium falciparum, den Erreger der Malaria, wurde das Schlüsselenzym der Fettsäuresynthese Typ-2, Enoyl ACP Reduktase (ENR), als potentielle Zielstruktur vorgeschlagen. In einem virtuellen Screening einer virtuellen Datenbank von fragmentartigen Kleinmolekülen konnten acht vielversprechende Strukturen ausfindig gemacht werden. Ein Salicylsäureamidderivat zeigte in einem zellulären Assay inhibitorische Wirkung im erythrozytären Stadium. Diese Verbindung wurde in weiteren Schritten optimiert, in dem Struktur-Aktivitäts-Beziehungen und kombinatorisches Docking für Salicylamide analysiert wurden. Aus dieser Studie konnten zwei potente Verbindungen hervorgehen, die eine niedrige Zytotoxizität aufweisen und in einstellig mikromolarer Konzentration sowohl im erythrozytären als auch im prä-erythrozytären Stadium ihre hemmende Wirkung entfalten. Die Wirkung im prä-erythrozytären Stadium zeigte sich der Wirkung von Primaquin überlegen. Die Biosynthese der Tetrahydrofolsäure ist ein essenzieller Stoffwechselweg für fast alle Organismen. Das Enzym Pyruvoyltetrahydropterin Synthase im Plasmodium falciparum (PfPTPS) übernimmt in diesem Stoffwechselweg die Katalyse einer Reaktion, die gewöhnlich von Dihydroneopterin Aldolase katalysiert wird, das jedoch im Plasmodium Genom fehlt. Die Einbettung des Enzyms PfPTPS in den Folatstoffwechsel qualifiziert es als eine potentielle Zielstruktur zur Entwicklung neuartiger Antifolate. Eine spezielle auf dieses Zielprotein hin aufgearbeitete Bibliothek weist Kleinmoleküle mit zink-bindenden funktionellen Gruppen auf. Die Durchführung eines virtuellen Screenings führte zur Auswahl von neun Molekülen für die Synthese, die anschließend auf ihre biologische Wirkung evaluiert werden sollen. Eine Vielzahl pathogener Mikroorganismen sind auf die Synthese der Isoprenoide aus dem Methylerithritolphosphatweg (MEP-Weg) angewiesen, daher eignet sich die Inhibition dieses Stoffwechselweges als eine sinnvolle Strategie für die Wirkstoffentwicklung. IspD ist eines der Enzyme des MEP-Weges und wurde als Modellprotein zur Untersuchung der bestimmenden Faktoren für eine strukturbasierte Wirkstoffentwickung ausgewählt. Ein Datensatz von leitstrukturartigen Kleinmolekülen aus der ZINC Datenbank wurde für ein virtuelles Screening benutzt, das zur Auswahl von sieben Kandidaten führte. Sechs Verbindungen konnten kommerziell erworben und getestet werden. Für drei Verbindungen konnte eine Proteinbindung gemessen werden

Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: The Main Protease (Mpro) Case Study

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.Fil: Llanos, Manuel. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Gantner, Melisa Edith. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rodríguez, Santiago. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Bioquímicas de La Plata "Prof. Dr. Rodolfo R. Brenner". Universidad Nacional de la Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata "Prof. Dr. Rodolfo R. Brenner"; ArgentinaFil: Alberca, Lucas Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Bellera, Carolina Leticia. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Talevi, Alan. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Gavernet, Luciana. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin

WISDOM: A Grid-Enabled Drug Discovery Initiative Against Malaria

The goal of this chapter is to present the WISDOM initiative, which is one of the main accomplishments in the use of grids for biomedical sciences achieved on grid infrastructures in Europe. Researchers in life sciences are among the most active scientifi c communities on the EGEE infrastructure. As a consequence, the biomedical virtual organization stands fourth in terms of resources consumed in 2007, with an average of 7000 jobs submitted every day to the grid and more than 4 million hours of CPU consumed in the last 12 months. Only three experiments on the CERN Large Hadron Collider have used more resources. Compared to particle physics, the use of resources is much less centralized as about 40 different scientifi c applications are now currently deployed on EGEE. Each of them requires an amount of CPU which ranges from a few to a few hundred CPU years. Thanks to the 20,000 processors available to the users of the biomedical virtual organization, crunching factors in the hundreds are witnessed routinely. Such performances were already achieved on supercomputers but at the cost of reservation and long delays in the access to resources. On the contrary, grid infrastructures are constantly open to the user communities. Such changes in the scale of the computing resources made continuously available to the researchers in biomedical sciences open opportunities for exploring new fi elds or changing the approach to existing challenges. In this chapter, we would like to show the potential impact of grids in the fi eld of drug discovery through the example of the WISDOM initiative