Faculté de Chimie, ULP, Strasbourg, FRANCE Criblage virtuel Alexandre Varnek Faculté de Chimie, ULP, Strasbourg, FRANCE

Small Library of selected hits experimental computational Virtual Screening Filtering, QSAR, Docking Small Library of selected hits High Throughout Screening Hit Target Protein Large libraries of molecules

Virtual screening must be fast and reliable Chemical universe: 10200 molecules 1060 druglike molecules Virtual screening must be fast and reliable Molecules are considered as vectors in multidimentional chemical space defined by the descriptors 3

Candidat au développement Criblage à haut débit Cible HTS Criblage à haut débit High-throughput screening Hits Lead Génomique Analyse de données Optimisation Candidat au développement

Drug Discovery and ADME/Tox studies should be performed in parallel idea target combichem/HTS hit lead candidate drug ADME/Tox studies

Methodologies of a virtual screening

Platform for Ligand Based Virtual Screening ~106 – 109 molecules Filters Similarity search ~103 - – 104 molecules QSAR models Candidates for docking or experimental tests 7

Virtual Screening Molecules available for screening (1) Real molecules 1 - 2 millions in in-house archives of large pharma and agrochemical companies 3 - 4 millions of samples available commercially (2) Hypothetical molecules Virtual combinatorial libraries (up to 1060 molecules)

Methods of virtual High-Throughput Screening Filters Similarity search Classification and regression structure – property models Docking

Filters: Lipinski rules for drug-like molecules (« Rules of 5 ») H-bond donors < 5 (the sum of OH and NH groups); MWT < 500; LogP < 5 H-bond acceptors < 10 (the sum of N and O atoms without H attached).

Example of different filters: Rules for Absorbable compounds It is quite interesting to compare our permeability model to the Lipinski’s and Veber’s rules. All three models are described by similar parameters. The following table shows the maximum cut-off values for absorbable compounds in our data set. In bold we show cut-off values of Lipinski’s and Veber’s rules. By comparing these three columns we can see that in most cases the cut-off values of Lipinski’s and Veber’s rules have been exceeded by 100 percent. This observation has a dual explanation. First, all three models dealt with quite different biological phenomena. Lipinski analyzed compounds that reached the second phase of clinical trials. Veber analyzed oral bioavailability in rats (which is affected by metabolism to a much greater extend than HIA). Whereas we analyzed HIA. The second explanation is that all models have been derived using quite different analytical tools. We used C-SAR analysis that automatically considers a large variety of possible causes that determine poor permeability. Lipinski and Veber used conventional data mining techniques.

unsupervised and supervised approaches Similarity Search: unsupervised and supervised approaches 12

2d (unsupervised) Similarity Search Tanimoto coef Recherche par similarité; comparaison des clés structurales; 1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 1 molecular fingerprints 13

Structural Spectrum of Thrombin Inhibitors structural similarity “fading away” … reference compounds 0.56 0.72 0.53 0.84 0.67 0.52 0.82 0.64 0.39

R. Guha et al. J.Chem.Inf.Mod., 2008, 48, 646 discontinuous SARs continuous SARs gradual changes in structure result in moderate changes in activity “rolling hills” (G. Maggiora) small changes in structure have dramatic effects on activity “cliffs” in activity landscapes Structure-Activity Landscape Index: SALIij = DAij / DSij DAij (DSij ) is the difference between activities (similarities) of molecules i and j R. Guha et al. J.Chem.Inf.Mod., 2008, 48, 646 Courtesy of Prof. J. Bajorath, University of Bonn

discontinuous SARs VEGFR-2 tyrosine kinase inhibitors MACCSTc: 1.00 Analog 6 nM 2390 nM small changes in structure have dramatic effects on activity “cliffs” in activity landscapes lead optimization, QSAR bad news for molecular similarity analysis... Courtesy of Prof. J. Bajorath, University of Bonn

Example of a “Classical” Discontinuous SAR Any similarity method must recognize these compounds as being “similar“ ... (MACCS Tanimoto similarity) Adenosine deaminase inhibitors

... when target structure is unknown Virtual Screening ... when target structure is unknown Virtual library Screening library Diverse Subset Parallel synthesis or synthesis of single compounds Design of focussed library Screening HTS Hits