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Screen Ligand based virtual screening presented by … maintained by Miklós Vargyas Last update: 13 April 2010.

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Presentation on theme: "Screen Ligand based virtual screening presented by … maintained by Miklós Vargyas Last update: 13 April 2010."— Presentation transcript:

1 Screen Ligand based virtual screening presented by … maintained by Miklós Vargyas Last update: 13 April 2010

2 Screen Virtual screening by topological descriptors

3 Screen performs high throughput virtual screening of compound libraries using similarity comparisons by various molecular descriptors. Description of the product Screen Availabilty JChemBase JChem Oracle cartridge Instant Jchem Server version standalone command line application programs KNIME PipelinePilot

4 Various 2D descriptors ChemAxon chemical fingerprint (CCFP) PipelinePilot ECFP/FCFP ChemAxon pharmacophore fingerprint (CPFP) BCUT Scalars (logP, logD, Szeged index …) custom descriptors, in-house fingerprints Optimized similarity measures Improves similarity prediction depends on set of known actives high enrichment ratios in virtual screening Multiple queries 3 types of hypotheses combined hit lists Key features

5 Versatile Use various descriptors in your well established model Access your trusted in-house fingerprint in IJC, JCB, JCART Easy integration in corporate discovery pipelines Search chemical files directly no need to import structures in database New descriptors are pluggable in deployed systems Optimal Consistent similarity scores Smaller hit set More focused library Benefits

6 More consistent similarity scores Benefits regular Tanimoto optimized Tanimoto

7 High enrichment ratio Fewer false hits Known actives are true positive hits (ACE inhibitors) Benefits

8 Results NPY-5 (pharmacophore similarity)

9 β2-adrenoceptor (pharmacophore similarity) Results

10 Case study at Axovan GPCR activity prediction distinguishing between GPCR subclasses GPCR-Tailored Pharmacophore Pattern Recognition of Small Molecular Ligands Modest von Korff and Matthias Steger, JCICS 2004, 44

11 Screen roadmap New molecular descriptors –ECFP/FCFP (in 5.4) –Shape descriptors (in 5.4) Hidden use of the optimiser –No-pain black-box approach –Simultaneous multi-descriptor search Enhanced IJC integration –Easy descriptor configuration and generation –Similarity search type instead of descriptors, metrics and other unfriendly concepts

12 Screen roadmap GUI –New web interface (HTML/AJAX) –Desktop application for descriptor generation 3D shape similarity –fast pre-filtering by 3D fingerprint –Alignment based volumetric Tanimoto calculation –scaffold hopping by maximizing topological dissimilarity and spatial similarity

13 Supplementary slides

14 query targets query fingerprint metric target fingerprints hits A typical approach

15 queries targets hypothesis fingerprint optimized metric target fingerprints hits optimization ChemAxons approach

16 Chemical fingerprint generation: 500/s Pharmacophore fingerprint generation calculated: 80/s rule-based: 200/s Screening: 12000/s Optimization: 10s/metric Hardware/software environment: P4 3GHz, 1GB RAM Red Hat Linux 9 Java Performance

17 Use of various fingerprints and metrics in JSP UGM presentation by Aureus Pharma Improved Virtual Screening Strategies and Enrichment of Focused Libraries in Active Compounds Using Target- Oriented Databases Implementations

18 Chemical, pharmacological or biological properties of two compounds match. The more the common features, the higher the similarity between two molecules. Chemical Pharmacophore Molecular similarity

19 Sequences/vectors of bits, or numeric values that can be compared by distance functions, similarity metrics. Quantitative assessment of similarity of structures need a numerically tractable form molecular descriptors, fingerprints, structural keys Similarity measures

20 (, ) = 0.68 (, ) = Standard metrics

21 hashed binary fingerprint encodes topological properties of the chemical graph: connectivity, edge label (bond type), node label (atom type) allows the comparison of two molecules with respect to their chemical structure Construction 1. find all 0, 1, …, n step walks in the chemical graph 2. generate a bit array for each walks with given number of bits set 3. merge the bit arrays with logical OR operation Topological chemical fingerprint

22 lengthwalkbit array 0C C – H C – C C – C – H C – C – O C – C – O – H ALL CCOHH H H HH Construction of chemical fingerprint

23 Chemical similarity

24 encodes pharmacophore properties of molecules as frequency counts of pharmacophore point pairs at given topological distance allows the comparison of two molecules with respect to their pharmacophore Construction 1. perceive pharmacophoric features 2. map pharmacophore point type to atoms 3. calculate length of shortest path between each pair of atoms 4. assign a histogram to every pharmacophore point pairs and count the frequency of the pair with respect to its distance Topological pharmacophore fingreprint

25 Rule based approach donor Rule 1: The pharmacophore type of an atom is an acceptor, if it is a nitrogen, oxygen or sulfur, and it is not an amide nitrogen or sulfur, and it is not an aniline nitrogen, and it is not a sulfonyl sulfur, and it is not a nitro group nitrogen. acceptor Pharmacophore perception

26 sp2 atom n-cyano-methil piperidine donor exception extra rules large number of rules maintenance, performance Exceptions to simple rules

27 pH = 7 pH = 1 acceptor donor pH pH specific rules large number of rules maintenance, performance Effect of pH

28 Step 1: estimation of pK a allows the determination of the protonation state for ionizable groups at the given pH Step 2: partial charge calculation Pharmacophore perception Calculation based approach

29 Step 3: hydrogen bond donor/acceptor recognition Step 4: aromatic perception Step 5: pharmacophore property assignment acceptor negatively charged acceptor acceptor and donor hydrophobic none Pharmacophore perception Calculation based approach

30 Pharmacophore type coloring: acceptor, donor, hydrophobic, none. Pharmacophore fingerprint

31 0 1 2 AA1AA2AA3AA4AA5AA6 0 1 AA1AA2AA3AA4AA5AA6 D E = AA1AA2AA3AA4AA5AA AA1AA2AA3AA4AA5AA6 D E =0.45 Fuzzy smoothing

32 query targets query fingerprint metric target fingerprints hits Virtual screening using fingerprints

33 queries targets hypothesis fingerprint metric target fingerprints hits Multiple query structures

34 allows faster operation compiles features common to each individual actives reduces noise Active Active Active Minimum Average Median Hypothesis types Advantages Hypothesis fingerprints

35 AdvantagesDisadvantages Minimum strict conditions for hits if actives are fairly similar false results with asymmetric metrics misses common features of highly diverse sets very sensitive to one missing feature Average captures common features of more diverse active sets less selective if actives are very similar Median captures common features of more diverse active sets specific treatment of the absence of a feature less sensitive to outliers less selective if actives are very similar Hypothesis fingerprints

36 Too many hits The need for optimization

37 Inconsistent dissimilarity values The need for optimization

38 asymmetry factor scaling factor asymmetry factor weights Parametrized metrics

39 selected targets training set test setknown actives query set training set test set Step 1 optimize parameters for maximum enrichment Step 2 validate metrics over an independent test set Optimization of metrics

40 query set training set Step 1 optimize parameters for maximum enrichment query fingerprint parametrized metric Optimization of metrics

41 v1v1 v2v2 v3v3 vivi vnvn potential variable value temporarily fixed value running variable value final value Optimization of metrics

42 test set Step 2 validate metrics over an independent test set query set query fingerprint optimized metric Optimization of metrics

43 Similar structures get closer Results of Optimization

44 2. Hit set size reduced Active set: 18 mGlu-R1 antagonists Target set: randomly selected drug-like structures Results of Optimization

45 3. Higher enrichment Results of Optimization

46 4. Top ranked structures are spikes offers a more intuitive way to evaluate the efficiency of screening based on sorting random set hits and known actives on dissimilarity values and counting the number of random set hits preceding each active in the sorted list number of spikes retrieved number of virtual hits Results of Optimization

47 ACE (pharmacophore similarity) Results

48 NPY-5 (pharmacophore similarity)

49 β2-adrenoceptor (pharmacophore similarity) Results

50 3D flexible search Expected top performance 200 structures/s


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