Optimized Virtual Screening

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

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

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

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

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

6. Slide 6 Molecular descriptors Example 1: chemical fingerprint 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 find all 0, 1, …, n step walks in the chemical graph generate a bit array for each walks with given number of bits set merge the bit arrays with logical OR operation

7. Slide 7 Molecular descriptors Example 1: chemical fingerprint Example CH3 – CH2 – OH walks from the first carbon atom merge bit arrays for the first carbon atom: 1111011110

8. Slide 8 Molecular descriptors Example 1: chemical fingerprint 0100010100010100010000000001101010011010100000010100000000100000 0100010100010100010000000001101010011010100000000100000000100000

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

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

11. Slide 11 Virtual screening using fingerprints Individual query structure 0101010100010100010100100000000000010010000010010100100100010000 query fingerprint query proximity 0000000100001101000000101010000000000110000010000100001000001000 0100010110010010010110011010011100111101000000110000000110001000 0100010100011101010000110000101000010011000010100000000100100000 0001101110011101111110100000100010000110110110000000100110100000 0100010100110100010000000010000000010010000000100100001000101000 0100011100011101000100001011101100110110010010001101001100001000 0101110100110101010111111000010000011111100010000100001000101000 0100010100111101010000100010000000010010000010100100001000101000 0001000100010100010100100000000000001010000010000100000100000000 0100010100010011000000000000000000010100000010000000000000000000 0100010100010100000000000000101000010010000000000100000000000000 0101010101111100111110100000000000011010100011100100001100101000 0100010100011000010000011000000000010001000000110000000001100000 0000000100000000010000100000000000001010100000000100000100100000 0100010100010100000000100000000000010000000000000100001000011000 0001000100001100010010100000010100101011100010000100001000101000 0100011100010100010000100001001110010010000010001100000000101000 0101010100010100010100100000000000010010000010010100100100010000 hits targets target fingerprints

12. Slide 12 Virtual screening using fingerprints Multiple query structures 0100010100011101010000110000101000010011000010100000000100100000 0001101110011101111110100000100010000110110110000000100110100000 0100010100110100010000000010000000010010000000100100001000101000 0101110100110101010111111000010000011111100010000100001000101000 0001000100010100010100100000000000001010000010000100000100000000 0100010100010100000000000000101000010010000000000100000000000000 0101010101111100111110100000000000011010100011100100001100101000 0100010100011000010000011000000000010001000000110000000001100000 0000000100000000010000100000000000001010100000000100000100100000 0101110100110101010111111000010000011111100010000100001000101000 queries hypothesis fingerprint proximity 0000000100001101000000101010000000000110000010000100001000001000 0100010110010010010110011010011100111101000000110000000110001000 0100010100011101010000110000101000010011000010100000000100100000 0001101110011101111110100000100010000110110110000000100110100000 0100010100110100010000000010000000010010000000100100001000101000 0100011100011101000100001011101100110110010010001101001100001000 0101110100110101010111111000010000011111100010000100001000101000 0100010100111101010000100010000000010010000010100100001000101000 0001000100010100010100100000000000001010000010000100000100000000 0100010100010011000000000000000000010100000010000000000000000000 0100010100010100000000000000101000010010000000000100000000000000 0101010101111100111110100000000000011010100011100100001100101000 0100010100011000010000011000000000010001000000110000000001100000 0000000100000000010000100000000000001010100000000100000100100000 0100010100010100000000100000000000010000000000000100001000011000 0001000100001100010010100000010100101011100010000100001000101000 0100011100010100010000100001001110010010000010001100000000101000 0101010100010100010100100000000000010010000010010100100100010000 hits targets target fingerprints

13. Slide 13 Hypothesis fingerprints Advantages • allows faster operation • compiles features common to each individual actives Hypothesis types

14. Slide 14 Hypothesis fingerprints

15. Slide 15 Does this work?

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

17. Slide 17 0.57 0.55 0.47 Then why do we need optimization? Inconsistent dissimilarity values

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

19. Slide 19 training set training set query set known actives selected targets test set test set Optimization of metrics Step 1 optimize parameters for maximum enrichment Step 2 validate metrics over an independent test set

20. Slide 20 Target hits training set Active hits Optimization of metrics Step 1 optimize parameters for maximum enrichment query set 1111100010000100001000101000 query fingerprint

21. Slide 21 potential variable value temporarily fixed value final value running variable value Optimization of metrics One step of the algorithm v1 v2 v3 vi vn

22. Slide 22 Target hits test set Active hits Optimization of metrics Step 2 validate metrics over an independent test set query set 1111100010000100001000101000 query fingerprint

23. Slide 23 0.57 0.55 0.47 Results Similar structures get closer 0.20 0.06 0.28

24. Slide 24 Results Hit set size reduction Active set: 18 mGlu-R1 antagonists Target set: 10000 randomly selected drug-like structures + 7 spikes

25. Slide 25 Results Improvement by optimization

26. Slide 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 0.014 0.015 0.017 0.020 0.022 0.023 0.027 0.041 0.043 number of virtual hits number of actives

27. Slide 27 Results ACE (pharmacophore similarity)

28. Slide 28 Results NPY-5 (pharmacophore similarity)

29. Slide 29 Results β2-adrenoceptor (pharmacophore similarity)

30. Slide 30 Results Structural or pharmacophore fingerprint? * Average 1-Tanimoto coefficient between each pair of compounds in the active set, based on chemical fingerprint.

31. Slide 31 Results Scaffold hopping

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

33. Slide 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, 25-34. • 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, 746-753. • 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, 2894-2896 • 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 http://www.currentdrugdiscovery.com/pdf/2002/3/BAJORATH.pdf