A unified database of structure/activity data is presented. This database was used to derive activity / classification models with Bayesian statistics and Linear Discriminant Analysis. This work has been published: http://www.nature.com/nbt/journal/v24/n7/abs/nbt1228.html

1. Predicting Pharmacology Willem van Hoorn Pfizer Global Research & Development Sandwich UK [email_address] Pipeline Pilot UGM, San Diego, Mar 2006

2. Willem van Hoorn Standing on the Shoulders of Giants Gaia Paolini Richard Shapland Andrew Hopkins Jonathan Mason

4. Why Giants Are Required

5. Unified DB Unified Database as Starting Point Bayesian Learn Molecular Categories Predicting activities Linear Discriminant Analysis (LDA) Predicting gene families Polypharmacology interaction network

6. Polypharmacology Network From Binding Data Node : target Edge : compound Metalloproteases Cysteine proteases Serine proteases Phosphodiesterases Aminergic GPCRs Peptide GPCRs GPCRs (others: classes A, B & C) Enzymes (hydrolases, transferases, oxidoreductases & others) Ion Channels Nuclear hormone receptors Aspartyl proteases Kinases Miscellaneous

7. Deriving Multi-Category Bayesian Model 238k actives (  10  M), human target, Mw < 1000, pass reactivity filter,  10 actives / target FCFP_6 90% / 214k 10% / 23,792 55,781 activities 698 models Unified DB

9. 50 Assessing the Predictions of the Random Test Set 58,428 predictions / 17,210 compounds 16,281 compounds  1 correct prediction 31,600 true positives (random: 292) Enrichment ~ 100 fold 26,828 false positives (random: 55,489) 24,181 false negatives

10. Nuclear hormone receptors Ion Channels Phosphodiesterases Aminergic GPCRs Peptide GPCRs GPCRs (others) Enzymes (others) True positive prediction False positive prediction Predicted Polypharmacology Network At Bayesian Cut-off 50

12. A More Challenging Test Set: Cerep Bioprint 238k actives (  10  M), human target, Mw < 1000, pass reactivity filter,  10 actives / target FCFP_6 237k Bioprint 997 compounds 316 targets 694 models Unified DB

13. A More Challenging Test Set: Cerep Bioprint 50 720 predictions / 291 compounds 210 compounds  1 correct prediction 433 true positives (random: 17) Enrichment ~ 25 fold 287 false positives (random: 55,489) 12,281 false negatives

14. Another Look At The Same Data 0 36,222 predictions 6,121 true positives 30,101 false positives 6,593 false negatives  48% of actives in 11% of data Plus 378 extra predicted targets

17. Predicting Forgeries with LDA and Bayesian LDA Bayesian NOTE Length Left Right Bottom Top Diagonal BankNotes LD1 BN1 215.1 130.0 129.8 9.100 10.20 141.5 true 2.501 BN2 214.7 130.7 130.8 11.20 11.20 139.4 false -4.561 BN3 214.3 129.9 129.9 10.20 11.50 139.6 false -3.390 BN4 214.7 130.0 129.4 7.800 10.00 141.2 true 4.060 NOTE Length Left Right Bottom Top Diagonal BankNotesBayes BN1 215.1 130.0 129.8 9.100 10.20 141.5 1.992 BN2 214.7 130.7 130.8 11.20 11.20 139.4 -6.611 BN3 214.3 129.9 129.9 10.20 11.50 139.6 -6.341 BN4 214.7 130.0 129.4 7.800 10.00 141.2 1.771

18. Predicting Gene Class by Physical Properties Compounds binding to different gene classes posses different physical property distributions: Can this be used to predict gene class from physical properties alone? How does LDA compare to Bayesian? Mw clogP

19. Predicting Gene Class by Physical Properties 148k actives (  10  M), human target, Mw < 1000, pass reactivity filter, binding to single target class only Aminergic GPCRs Aspartyl Proteases Cysteine Proteases Enzymes- others GPCRs Class A- others GPCRs Class B GPCRs Class C Hydrolases Ion Channels- Ligand_Gated Ion Channels- others Kinases- others Metalloproteases Nuclear hormone receptors Others Oxidoreductases PDEs Peptide GPCRs Protein Kinases Serine Proteases Transferases 20 Gene Classes: Unified DB

20. Molecular_Weight Num_H_Acceptors Num_H_Donors Num_RotatableBonds Molecular_PolarSurfaceArea No_IonCenters Molecular_Solubility Molecular_SurfaceArea ClogP * Andrews* Predicting Gene Class by Physical Properties 10 Descriptors: 147,534 118,118 29,416

21. Predicting Gene Class by Physical Properties 29416 (9025) 1 (0) 349 (137) 5309 (1423) 8123 (2811) 791 (248) 888 (241) 2638 (499) 482 (163) 279 (74) 0 (0) 152 (59) 47 (0) 0 (0) 0 (0) 1 (0) 1268 (366) 1969 (321) 75 (28) 1180 (613) 5864 (2042) LDA (correct) 29416 (5631) 1012 (125) 792 (133) 341 (147) 2809 (1135) 2176 (392) 1437 (329) 90 (47) 2083 (345) 1626 (293) 1545 (100) 964 (104) 2109 (280) 350 (42) 3346 (146) 2340 (115) 962 (309) 1 (0) 1464 (73) 1670 (614) 2299 (902) Bayes (correct) 29416 (1447) 1460 (36) 1526 (53) 1488 (148) 1461 (236) 1468 (56) 1492 (54) 1465 (167) 1459 (53) 1515 (47) 1430 (11) 1441 (29) 1448 (52) 1461 (15) 1438 (29) 1477 (14) 1524 (117) 1451 (135) 1470 (13) 1479 (29) 1463 (153) Random (correct) 29416 727 913 2927 5027 1178 1385 3336 1238 849 198 594 764 286 339 226 2647 2574 252 728 3228 Experiment Target class Total Transferases Serine Proteases Protein Kinases Peptide GPCRs PDEs Oxidoreductases Others Nuclear hormone receptors Metalloproteases Kinases- others Ion Channels- others Ion Channels- Ligand_Gated Hydrolases GPCRs Class C GPCRs Class B GPCRs Class A- others Enzymes- others Cysteine Proteases Aspartyl Proteases Aminergic GPCRs