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Optimized Virtual Screening Miklós Vargyas Zsuzsanna Szabó György Pirok Ferenc Csizmadia ChemAxon Ltd. Matthias Steger Modest von Korff AXOVAN AG Allschwil,

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Presentation on theme: "Optimized Virtual Screening Miklós Vargyas Zsuzsanna Szabó György Pirok Ferenc Csizmadia ChemAxon Ltd. Matthias Steger Modest von Korff AXOVAN AG Allschwil,"— Presentation transcript:

1 Optimized Virtual Screening Miklós Vargyas Zsuzsanna Szabó György Pirok Ferenc Csizmadia ChemAxon Ltd. Matthias Steger Modest von Korff AXOVAN AG Allschwil, Switzerland (Axovan is now Actelion.)Actelion Slide 1

2 Drug research structures found corporate database Is it searching for a needle in a haystack? Slide 2

3 structures found (virtual hits) query structures (known actives) corporate database (targets) Find something similar to a fistful of needles Drug research Slide 3

4 Molecular similarity Chemical, pharmacological or biological properties of two compounds match. The more the common features, the higher the similarity between two molecules. Chemical Pharmacophore What is it? Slide 4

5 Molecular similarity How to calculate it? Sequences/vectors of bits, or numeric values that can be compared by distance functions, similarity metrics. Quantitative assessment of similarity/dissimilarity of structures need a numerically tractable form molecular descriptors, fingerprints, structural keys Slide 5

6 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 Molecular descriptors Example 1: chemical fingerprint 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 Slide 6

7 Molecular descriptors Example 1: chemical fingerprint Example CH3 – CH2 – OH walks from the first carbon atom lengthwalkbit array 0C C – H C – C C – C – H C – C – O C – C – O – H merge bit arrays for the first carbon atom: Slide 7

8 Molecular descriptors Example 1: chemical fingerprint Slide 8

9 Molecular descriptors Example 2: pharmacophore fingerprint 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. map pharmacophore point type to atoms 2. calculate length of shortest path between each pair of atoms 3. assign a histogram to every pharmacophore point pairs and count the frequency of the pair with respect to its distance Slide 9

10 Molecular descriptors Example 2: pharmacophore fingerprint Pharmacophore point type based coloring of atoms: acceptor, donor, hydrophobic, none. Slide 10

11 query targets query fingerprint proximity target fingerprints hits Virtual screening using fingerprints Individual query structure Slide 11

12 queries targets hypothesis fingerprint proximity target fingerprints hits Virtual screening using fingerprints Multiple query structures Slide 12

13 Hypothesis fingerprints allows faster operation compiles features common to each individual actives Active Active Active Minimum Average Median Hypothesis types Advantages Slide 13

14 Hypothesis fingerprints AdvantagesDisadvantages Minimumstrict 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 Averagecaptures common features of more diverse active sets less selective if actives are very similar Mediancaptures 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 Slide 14

15 Does this work? Slide 15 Active setPharmacophore fingerprint Chemical fingerprint namesizeTanimotoEuclideanTanimotoEuclidean 5-HT ACE Angiotensin Beta D delta Ftp mGluR NPY Thrombin

16 Then why do we need optimization? Too many hits Slide 16

17 Then why do we need optimization? Inconsistent dissimilarity values Slide 17

18 What can be optimized? asymmetry factor scaling factor asymmetry factor weights Parameterized metrics Slide 18

19 Optimization of metrics 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 Slide 19

20 Optimization of metrics query set training set Step 1 optimize parameters for maximum enrichment Target hits Active hits query fingerprint Slide 20

21 Optimization of metrics v1v1 v2v2 v3v3 vivi vnvn One step of the algorithm potential variable value temporarily fixed value running variable value final value Slide 21

22 Optimization of metrics test set Step 2 validate metrics over an independent test set Target hits Active hits query set query fingerprint Slide 22

23 Results Similar structures get closer Slide 23

24 Results Hit set size reduction Active set: 18 mGlu-R1 antagonists Target set: randomly selected drug-like structures + 7 spikes Slide 24 MetricEnrichmentTest hits Random hits Tanimoto Basic Scaled Asymmetric Scaled Asymmetric Euclidean Basic Normalized Asymmetric Normalized Weighted Normalized Weighted Asymmetric Normalized

25 Results Improvement by optimization Slide 25 Active setsizeEuclideanOptimizedImprovement ratio 5-HT ACE Angiotensin Beta D delta Ftp mGluR NPY Thrombin

26 Results Active Hit Distribution 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 actives number of virtual hits Slide 26

27 Results ACE (pharmacophore similarity) Slide 27

28 Results NPY-5 (pharmacophore similarity) Slide 28

29 Results β2-adrenoceptor (pharmacophore similarity) Slide 29

30 Results Structural or pharmacophore fingerprint? Slide 30 * Average 1-Tanimoto coefficient between each pair of compounds in the active set, based on chemical fingerprint. Active setsizechemicalpharmacophorediversity* 5-HT ACE Angiotensin Beta D delta Ftp mGluR NPY Thrombin

31 Results Scaffold hopping Slide 31

32 Acknowledgements Nóra Máté Szilárd Dóránt Bernard Przybylski (Axovan) Contributors: The research was supported by Slide 32 (Axovan is now part of Actelion.)

33 Bibliography J. Xu: GMA: A Generic Match Algorithm for Structural Homomorphism, Isomorphism, and Maximal Common Substructure Match and its Applications, J. Chem. Inf. Comput. Sci., 1996, 36, 1, L. Xue, F. L. Stahura, J. W. Godden, J. Bajorath: Fingerprint Scaling Increases the Probability of Identifying Molecules with Similar Activity in Virtual Screening Calculations, J. Chem. Inf. Comput. Sci., 2001, 41, 3, G. Schneider, W. Neidhart, T. Giller, and G. Schmid: 'Scaffold-Hopping' by Topological Pharmacophore Search: A Contribution to Virtual Screening, Angew. Chem. Int. Ed., 1999, 38, 19, D. Horvath: High Throughput Conformational Sampling and Fuzzy Similarity Metrics: A Novel Approach to Similarity Searching and Focused Combinatorial Library Design and its Role in the Drug Discovery Laboratory; manuscript J. Bajorath: Virtual screening in drug discovery: Methods, expectations and reality Slide 33


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