1. TIDEA Target (and Lead) Independent Drug Enhancement Algorithm

2. 2 Receptor affinity versus Diversity: A classic problem Drug discovery methods too often require trading potency for diversity Our goal to develop a single metric for predicting potency and enhancing hit rates with the following properties: Independent of overall ligand shape and size Independent of macromolecular target site shape. Effective for a wide variety of ligand scaffolds, Effective for multiple targets and target classes Effective for multiple disease indications, and No knowledge of target structure or SAR required.

3. What is TIDEA? An algorithm for predicting small molecule potency Independent of target/ligand complementarity and ligand shape Identifies highly diverse, potent ligands 3

4. Learning Set 120 Ligands >40 ligand scaffold types >40 targets Targets distinct from Test Set Test Set 80 Ligands 20 ligand scaffold types 20 targets Targets distinct from Learning Set Combined Learning Set + Test Set 200 Small (FW<700) non peptide ligands Recently published*, drug-like IC50 or Ki between 10 pM and 10  M >60 targets, 60+ligand scaffolds 4 Development of TIDEA-Learning Set + Test Set

5. 5 Development of TIDEA-Continued Learning Set 120 Ligands Test Set 80 Ligands 1. Complex parameters each designed to increase % subnanomolar ligands for several different target classes/ligand shapes. Required years of work. 2. Combine >50 of these complex parameters to create and algorithm that calculates a single number with predictive value for affinity in >50 targets and target classes. TIDEA algorithm Test Set 80 TIDEA scores

6. 6 Affinity (PLogIC50) is significantly higher in a diverse Test Set of 80 ligands

7. 7 Test Set potency does not increase significantly with FW.

8. 8 Test Set potency does not increase significantly with ClogP

9. Test Set: More Potent Ligands (<1nM) are Enriched at Higher TIDEA Scores 9

10. 10 The 8 highest TIDEA values show more diversity (8÷5 =1.6 molecules per target) than the whole Test Set (80÷20=4 molecules per Target)

11. 11 Is TIDEA selective does it mostly find promiscuous inhibitors? The difference in average TIDEA score between promiscuous inhibitors and drugs, or between promiscuous and drug candidates, is statistically significant by the T test (P<0.0001)

12. 12 How does TIDEA compare to ligand-based approaches? They differ too much for direct comparison. TIDEA is not a replacement for ligand- based approaches, and vice-versa. Hit rates and average potency increase significantly with increasing TIDEA score even when every molecule binds to a different target: nM  M

13. 13 Independent, Prospective Trial of TIDEA Dr. Matthew Soellner, at the College of Pharmacy, Univ. Michigan, has carried out out a prospective study of the ability of TIDEA to identify active kinase inhibitors in collaboration with Focus Synthesis. He designed and screened a diverse, 181-molecule subset of a 3186-molecule kinase-targeted library using Src, Abl, and Hck kinases and identified 27 hits (>20% inhibition for 1 or more kinases). The TIDEA scores in the 181 molecule subset ranged from 0 to 19. The results show that high TIDEA values predict high activity.

14. 14 Prospective Trial by Matt Soellner: The hit rate (% of molecules that inhibit Src, Abl, or Hck by 20%+) increases with increasing TIDEA score for 181 nitrogen heterocycles

15. 15 Prospective Trial: The increase in hit rate at higher TIDEA values is statistically significant (Chi square) A high TIDEA score subset (score>6.5, 128 molecules) contained 26 hits out of 128 while a low TIDEA score subset (score<6.5) contained only 1 hit out of 53: a 10.7-fold increase in hit rate above 6.5. 96% of the hits (26/27) had TIDEA scores > 6.5. 39% of the entire 3186-molecule library had a TIDEA score below 6.5. In conclusion, TIDEA can cut purchase and screening costs ~39% while retaining ~96% of the hits for 3 kinases.

16. 16 Comparison of TIDEA and Ligand-Based methods TIDEA Ligand-Based Methods Designed to be independent of overall ligand shape and size Defines molecular shape and size as part of methodology Determines adhesiveness potential independent of ligand/target shape complementarity Determines degree of ligand/target shape complementarity A single model identifies potent, selective inhibitors independent of target and target class Mutliple, distinct models built for each target each identify potent, selective inhibitors of a single target type or a narrow range of targets. No need to for knowledge of SAR or even target identity. Requires SAR information for each target.

17. 17 Where does TIDEA fit in as a drug discovery tool? Earliest stage screening of large, diverse libraries prior to application of target-specific methods. For new targets. When the target is unknown or knowledge is limited (no 3D structure and little or no SAR data). For unkown targets. Prior to cell-based screening, phenotypic screening, chemical genetics. In combination with target-specific methods.

18. Benefits of TIDEA Maintains diversity Enhances discovery rates Does not require knowledge of target or SAR Saves money and time Identifies “drug-like” molecules 18

19. Additional slides 19

20. 20 TIDEA also identifies drug-like small molecules TIDEA is an excellent drug-like nature metric. For examples, Lipitor has a score of 13, much higher than the average (2.5) score for non- drug organic molecules.

21. Learning Set and Test Set targets 21