Characterization of Pharmacophore Multiplet Fingerprints as Molecular Descriptors. Robert D. Clark 2004 Tripos, Inc.

Transcription

1 Characterization of Pharmacophore Multiplet Fingerprints as Molecular Descriptors Robert D. Clark Tripos, Inc Tripos, Inc.

2 Outline Background o history o mechanics Finding appropriate binning ranges o biased conformer generation Similarity measures o stochastic similarity Hypothesis generation o asymmetric similarity Conclusions

3 History of Pharmacophore Multiplets A.C. Good and I.D. Kuntz; J. Comput.-Aided Mol. Design 1995, 9, X. Chen, A. Rusinko, and S.S. Young; J. Chem. Inf. Comput. Sci. 1998, 38, J.S. Mason, I. Morize, P.R. Menard, D.L. Cheney, C. Hulme & R.F. Labaudiniere; J. Med. Chem. 1999, 42, M.J. McGregor & S.M. Muskal; J. Chem. Inf. Comput. Sci. 1999, 39, H. Matter and T. Pötter; J. Chem. Inf. Comput. Sci. 1999, 39, J.S. Mason and B.R. Beno; J. Mol. Graphics Mod. 2000, 18, E. Abrahamian, P.C. Fox, L. Nærum, I.T. Christensen, H. Thøgersen & R.D. Clark; J. Chem. Inf. Comput. Sci. 2003, 43,

4 Novo Nordisk / Tripos Tuplets Collaboration 2 year collaboration to develop and extend existing SYBYL triplet (PDT) technology Incorporate pair, triplet and quartet ( Tuplet) technology Augmented Tuplets and support for privileged substructures Conformers generated on-the-fly or retrieved Bitmaps created, stored and manipulated in compressed format o four 1.8 x 10 9 bit bitmaps stored as ~80kb file o seconds/molecule

5 Type III antiarrhythmic: UK donor atom acceptor atoms positive nitrogen hydrophobic center hydrophobic center donor/acceptor atoms

6 Multiplet Fingerprints

7 Indexing Triplets 2 D 3 Vertex joining longest and shortest edges A 5 H Bin: 5, 3, 2 Triplet: H-A-D

8 Indexing Tetrahedra Problems: Need a unique mapping Must deal with chirality Literally dozens of possible permutations Mapping must be based on bins and features A 4 4 C 2 3 D 2 C Plane of symmetry implies no chirality D 2 C 2 3 C 4 4 A 4 C 3 2 D 2 Chiral tetrahedra D C 4 A 4 B B 4 A

9 Mapping Quartet Bits Mapping for 7 bins and 3 features (D, A, H) * DDDD DDDA DDDH HHHH Bitmap Size = 7 6 * 3 4 = 9,529,569 bits * specifies the + enantiomer; specifies the - enantiomer +

10 Distribution of Distances Between Features frequency frequency frequency beta blockers K + channel openers Type I antiarrythmics edge length (Å)

11 1 Conformer By Class Cumulative Distributions across Classes frequency frequency Conformer By Class Estrogen Antagonists Type III Antiarrythmics Benzamides Phenothiazines Beta Blockers Type I Antiarrythmics K Channel Openers Estrogen Antagonists Type III Antiarrythmics Benzamides Phenothiazines Beta Blockers Type I Antiarrythmics K Channel Openers edge length (Å)

12 100 Confort Conformer By Class Effect of Biased Conformer Generation frequency frequency Estrogen Antagonists Type III Antiarrythmics Benzamides Phenothiazines Beta Blockers Type I Antiarrythmics K Channel Openers Systematic Search Conformers By Class Estrogen Antagonists Type III Antiarrythmics Benzamides Phenothiazines Beta Blockers Type I Antiarrythmics K Channel Openers edge length (Å)

13 Hypothesis Fingerprint Creation Binary Compound Fingerprints DDD 000 DDD 001 DDA 200 DAA 210 DDH 210 DAH 331 DHH 333 HHH

14 Hypothesis Fingerprint Creation Binary Compound Fingerprints Vector Sum Fingerprint DDD 000 DDD 001 DDA 200 DAA 210 DDH 210 DAH 331 DHH 333 HHH

15 Hypothesis Fingerprint Creation Binary Compound Fingerprints Vector Sum Fingerprint Feature Weights Bin Weights Bit Score DDD 111 DDD 211 DDA 311 DAA 321 DDH 321 DAH 442 DHH 444 HHH

16 Weighting Bits for Hypothesis Generation nf nd S b = f b i= 1 fw i j= 1 dw j f 1 S b is the score for the bit f b is the frequency of the bit fw i is the weight of the feature type dw j is the weight of the distance bin f 2 d 1 d 3 d 2 f 3 Construct an hypothesis from the highest scoring bits.

17 Hypothesis Fingerprint Creation Binary Compound Fingerprints Vector Sum Fingerprint Feature Weights Bin Weights Bit Score DDD 111 DDD 211 DDA 311 DAA 321 DDH 321 DAH 442 DHH 444 HHH

18 S = tn N N t Sanity Checker

19 Similarity Measures Tanimoto coefficient t( A, B) = Cosine coefficient pdt( A) pdt( A) pdt( B) pdt( B) Cc( a, b) = pdt( a) pdt( b) pdt( a) pdt( b) Stochastic cosine coefficient s( A, B) = E * E[ pdt( A) pdt ( B) ] * * [ pdt( A) pdt ( A) ] E[ pdt( B) pdt ( B) ]

20 Effect of Conformer Count on Stochastic Cosine Similarity 0.6 similarity Estrogen_Antagonist Class Similarity Estrogen_Antagonist Non-Class Similarity K_openers Class Similarity K_openers Non-Class Similarity benzamides Class Similarity 0.1 benzamides Non-Class Similarity conformer count (max)

21 Effect of Conformer Count on Stochastic Cosine Discrimination discrimination ratio I_Antiarrythmics III_Antiarrythmics Phenothiazines beta Blocker Benzamides K_openers Estrogen_Antagonist conformer count (max)

22 Discrimination and Similarity Measure discrimination ratio discrimination ratio simple cosine Tanimoto I_Antiarrythmics III_Antiarrythmics Phenothiazines beta Blocker Benzamides K_openers Estrogen_Antagonist I_Antiarrythmics III_Antiarrythmics Phenothiazines beta Blocker Benzamides K_openers Estrogen_Antagonist conformer count (max)

23 Discrimiantion and Conformer Bias discrimination ratio discrimination ratio CONFORT systematic search I_Antiarrythmics III_Antiarrythmics Phenothiazines beta Blocker Benzamides K_openers Estrogen_Antagonist I_Antiarrythmics III_Antiarrythmics Phenothiazines beta Blocker Benzamides K_openers Estrogen_Antagonist conformer count (max)

24 Symmetric Similarity Measures Symmetric stochastic cosine s( A, B) = E [ ] * E pdt( A) pdt ( B) [ ( ) ( ) ] [ ( ) ( ) ] * * pdt A pdt A E pdt B pdt B Asymmetric stochastic cosine s*( h, t) = [ ( ) pdt( t) ] [ ( ) *( )] E pdt h E pdt h pdt h

25 Effect of Hypoothesis Size (Type III antiarrhythmics) average similarity average similarity symmetric cosine asymmetric stochastic cosine CONFORT within class 100 Conformers without class systematic search within class 1000 Conformers without class bits in hypothesis

26 Conclusions Compression is cool Natural binning does make sense o >15Å o at least for triplets Systematic bias increases discrimination o rule-based conformational bias can be useful o caveat: it may limit lead-hopping More is not necessarily better o true in terms of conformation count o true in terms of multiplet hypothesis size A little asymmetry can be a good thing Compression is still cool

27 Acknowledgements Novo Nordisk A/S (Denmark) Lars Nærum * Henning Thøgersen* Tripos, Inc. Edmond Abrahamian Peter Fox Trevor Heritage

28 May the multiplets be with you...

29

30 What a Protein Sees (electrostatic field at 0.5 Å resolution, 80 and 30% contours)

31 What the Chemist Sees H 3 C O S O Cl O N O F H 3 C N N O O H 3 C N H 3 C N H O CF 3 tetrahydrophthalimide (American Cyanamide) trifluorotoluidide pyrazole ether (Monsanto)

32 Pharmacophoric Features hydrogen bond acceptors H 3 C O S O Cl O N O F H 3 C N N O H 3 C N hydrophobic centers H 3 C O N H O hydrogen bond donor CF 3

33 Conformational Sampling* *diverse conformers obtained using CONFORT

34 Mapping Multiplets Mapping for 7 bins and 3 features (D, A, H)* bit... DDD DDA DDH HHH Bitmap Size = 7 3 * 3 3 = 9261 bits * Features are handled in the order supplied by the application.

35 Hypothesis Generation Multiple methods implemented for hypothesis generation o o o From a collection of known actives From a user defined UNITY query From a single molecule pharmacophore map a) Single or multiple generated conformers o From user specified residues in receptor cavity

36 Privileged Substructures: Augmented Triplets HY DS HY # name mnemonic xref weight min_dist max_dist DONOR_SITE DS AA =NULL.

37 Effect of Conformer Count on Cosine Coefficient Similarity 0.6 discrimination similarity ratio conformer count (max) Estrogen_Antagonist Class Similarity Estrogen_Antagonist Non-Class Similarity I_Antiarrythmics K_openers Class Similarity III_Antiarrythmics Phenothiazines K_openers Non-Class Similarity beta Blocker benzamides Class Similarity Benzamides K_openers benzamides Non-Class Similarity Estrogen_Antagonist