Shape Signatures is a novel molecular shape-based method for virtual screening in drug discovery and computational toxicology. It employs a ray-tracing algorithm to explore the volume enclosed by a molecule's surface, constructing histograms that encode molecular shape and polarity as signatures. These signatures can be used to rapidly screen large libraries, classify compounds, and build predictive models such as for drug-target binding, toxicity, and blood-brain barrier permeation.

1. Dmitriy Chekmarev Department of Pharmacology & Environmental Bioinformatics and Computational Toxicology Center, UMDNJ - RWJMS 675 Hoes Lane, Piscataway, NJ 08854 [email_address] Shape Signatures: Exploring novel molecular shape based methods for in silico drug discovery and computational toxicology

3. Shape Signatures Method pics from Meek PJ, Liu Z, Tian L, Wang CY, Welsh WJ, Zauhar RJ Drug Discovery Today. 2006 Oct;11(19-20):895-904 Indinavir (IDV) - HIV protease inhibitor 1D/2D-to-3D conversion (e.g. with CORINA) Generation of Solvent Excluded Surface (SES) Triangulation of SES using SMART algorithm 100,000 reflections ray tracing Rays propagate by optical reflection from triangular surface elements 1D ShapeSigs (shape only) 2D ShapeSigs (shape + MEP) 1D Shape Signatures generate a histogram of ray segment lengths (prob. dist.) 2D Shape Signatures compute molecular electrostatic potential (Coulomb) at each reflection point of SES, then generate a 2D histogram of pairs of ray segments and associated MEP values (joint prob. dist.)

4. Shape Signatures employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule, then uses the output to construct compact histograms (signatures) that encode for molecular shape and polarity. The method lends itself to rapid screening of large chemical libraries , and Shape Signatures databases can be created for an almost limitless number and variety of chemical structures. Zauhar RJ, Moyna G, Tian L, Li Z, Welsh WJ J Med Chem. 2003;46(26):5674-90 Shape Signatures: a new approach to computer-aided ligand- and receptor-based drug design

5. Measuring similarity between molecules 17  -estradiol DES (Diethylstilbestrol)

6. Measuring similarity between molecules 17  -estradiol DES (Diethylstilbestrol) Compare histograms !

8. Measuring similarity between molecules 17  -estradiol DES (Diethylstilbestrol)  2 scores:  shape = 0.196  shape+MEP = 0.361

11. Shape Signatures: Ligand-based virtual screening for selected therapeutic targets DUD: A Directory of Useful Decoys: (http://dud.docking.org/clusters) Good AC, Andrew Good’s DUD clustering http://dud.docking.org/clusters ; Huang N, Shoichet BK, Irwin JJ, J.Med.Chem. 2006 , 49, 6789-6801 Target PDB # actives # clusters # decoys ACE (Angiotensin-converting enzyme) 1o86 46 19 1766 ACHE (Acetylcholinesterase) 1eve 97 18 3773 CDK2 (Cyclin-dependent kinase) 1ckp 47 32 2023 COX2 (Cyclooxygenase-2) 1cx2 209 43 12354 HIVRT (HIV reverse trascriptase) 1rt1 33 17 1463 INHA (Enoyl ACP reductase) 1p44 56 23 2651 P38 (P38 mitogen activated protein) 1kv2 134 20 6644 PDGFRB (Platelet derived growth factor receptor kinase) 1t46 120 22 5505 SRC (Tyrosine kinase) 2src 96 20 5568 VEGFR2 (Vascular endothelial growth factor receptor) 1fgi 48 31 2649

12. Shape Signatures: Ligand-based virtual screening for selected therapeutic targets Arithmetic weighted ROC enrichment (awROCE) at false positive rate of 5% Arithmetic weighted ROC AUC (awAUC) The performance of each method is assessed using a set of arithmetic weighted ROCE @X% false positive rates and arithmetic weighted area under ROC curve (awAUC), which account for differences in the chemotype among the retrieved actives Jahn A, Hinselmann G, Fechner N, Zell A, J.Cheminformatics 2009 , 1:14, 1-23

13. Shape Signatures: Predictive modeling Classification by Support Vector Machines (SVM) ACTIVE NON-ACTIVE INPUT SPACE ACTIVE NON-ACTIVE FEATURE SPACE MAPPING complex boundary separating hyperplane Chang CC, Lin CJ. LIBSVM: A library for support vector machines, 2001 Sensitivity: SE = TP/(TP+FN), expresses the prediction accuracy for actives Specificity: SP = TN/(TN+FP), reflects the prediction accuracy for non-actives Overall prediction accuracy: Q = (TP+TN)/(TP+FP+TN+FN) Matthews correlation coefficient (  ): C = [TP*TN-FP*FN]/[(TP+FN)(TP+FP)(TN+FP)(TN+FN)] 1/2 For a perfect classifier with FP=FN=0, one would have C = 1.0. For a random prediction, C = 0, and for a complete inversion (TP=TN=0) C = -1.0

14. Shape Signatures: cardiotoxicity via blocking hERG potassium channels The human ether a-go-go-related gene, hERG , is believed to encode the K+ channel which regulates the repolarizing IKr current in the cardiac action potential (CAP). Blockage of hERG channel by some chemicals can cause potentially fatal cardiac arrhythmias by prolonging the QT interval of CAP. Drugs taken off the market include terfenadine, sertindole, cisapride Chekmarev D, Kholodovych V, Balakin KV, Ivanenkov Y, Ekins S, Welsh WJ. Chem. Res. Toxicol. 2008 , 21, 1304-1314 39 strong blockers: IC 50 < 1 µM and 44 weak blockers: IC 50 > 10 µM 2D Shape Sigs (shape + polarity) 1D Shape Sigs (shape only) Descriptors 0.488 74 74 73 78 SVM Classification Method 10-fold cross validation (%) Leave-20%-out testing SE (%) SP (%) Q (%) C SVM 77 70 68 69 0.390

15. Shape Signatures: cardiotoxicity via binding 5-HT 2B serotonin receptors Serotonin plays a major regulatory function in cardiovascular morphogenesis. 5-HT 2B (GPCR) is expressed in cardiovascular tissues and is implicated in the valvular heart diseases (VHD) caused by now banned ‘Fen-Phen’ anti-obesity medication. Norfenfluramine , a primary metabolite of fenfluramine , is a potent agonist of 5-HT 2B receptors Chekmarev D, Kholodovych V, Balakin KV, Ivanenkov Y, Ekins S, Welsh WJ. Chem. Res. Toxicol. 2008 , 21, 1304-1314 116 strong binders: K i  100 nM and 66 weak binders: K i  1 µM PDSP (NIMH Psychoactive Drug Screening Program) K i DB http://pdsp.med.unc.edu/ MOE 2D Shape Sigs (shape + polarity) 1D Shape Sigs (shape only) Descriptors 0.638 83 69 91 87 SVM Classification Method 10-fold cross validation (%) Leave-20%-out testing SE (%) SP (%) Q (%) C SVM 80 81 59 73 0.424 SVM 87 91 70 84 0.640

16. Shape Signatures: classification models with Blood-Brain Barrier permeation data SVM models using 2D Shape Signatures and MOE molecular descriptors Combined: 186 BBB+ and 165 BBB- Li et al: 250 BBB+ and 126 BBB- Kortagere S, Chekmarev D, Welsh WJ, Ekins S. Pharm. Res. 2008 , 25, 1836 - 1845 MOE 2D Shape Sigs (shape + polarity) MOE 2D Shape Sigs (shape + polarity) Molecular descriptors 0.635 82 79 84 83 Combined 0.595 80 79 80 80 Combined Dataset 10-fold cross validation (%) Leave-20%-out testing SE (%) SP (%) Q (%) C Li et al 80 89 62 80 0.533 Li et al 80 89 51 76 0.435

17. Shape Signatures: classification models for human PXR compounds SVM models using 2D (shape + polarity) Shape Signatures molecular descriptors Dataset 10-fold cross validation (%) Leave-20%-out testing SE (%) SP (%) Q (%) C CAT assay active: 39 non-actives: 54 90 81 88 85 0.703 Luciferase assay actives: 63 non-actives: 54 78 75 70 72 0.456

18. Shape Signatures: predicting inhibitors of AChE by regression methods QSAR: PLS and  -SVR regression models A set of 110 piperidine AChE inhibitors from # Sutherland et al. J. Med. Chem. 2004 , 47, 5541 Chekmarev D, Kholodovych V, Kortagere S, Welsh WJ, Ekins S. Pharm. Res. 2009 , 26, 2216-2224 1D Shape Sigs + MOE 2D CoMFA # 1D Shape Sigs + MOE 2D MOE 2D Molecular descriptors 1.09 0.34 0.83 0.55 PLS 1.03 0.43 0.75 0.64 PLS Method Training: 73 cmpds Test: 37 cmpds r 2 train s train r 2 test s test PLS 0.88 0.41 0.47 0.95  -SVR (Gaussian radial basis function kernel) 0.99 0.24 0.48 0.97

19. Shape Signatures User Interface Public access at http://shapesignatures.umdnj.edu

20. Shape Signatures User Interface Public access at http://shapesignatures.umdnj.edu

21. Shape Signatures User Interface Public access at http://shapesignatures.umdnj.edu

22. Shape Signatures User Interface Public access at http://shapesignatures.umdnj.edu