Functional Group FingerprintsCNS ChemistryWilmington, USA

James R. ArnoldCharles L. LermanWilliam F. Michne

James R. Damewood

American Chemical Society National MeetingAugust, 2004

Philadelphia, PA

Functional Group Fingerprint Objectives

• Develop a 2D fingerprint searching method that uses medicinally relevant, custom defined, functional groups.

• Validate the method on a large dataset of biological targets andchemical classes.

• Examine approaches for enhancing accuracy, and reducing false positive rates, of 2D searches.

• Deploy method company-wide.

• Rapidly expand SAR in Hit/Lead Identification.• Subset the corporate collection for screening.

Factor of 2 improvement in every 2D similarity search.

Many Target Classes Approached by Ligand-Based Methods:The Impetus for Developing Functional Group Fingerprints

Receptors, 45%

Enzymes, 28%

Hormones & Factors, 11%

Unknown, 7%

Ion Channels, 5%

Nuclear Receptors, 2%

DNA, 2%

Drews, J. Science 2000, 287, 1960-1964

Biochemical Classes of Drug Targets of Current Therapies:(Perhaps 70% of Targets Approached by Ligand-Based Methods)

O NH2

O

OH

NH

Functional Groups are One Aspect of Medicinal Chemistry Reasoning in Ligand-Based Design

Express one aspect of the knowledge of our most experienced people for wide use.

Design is often partially driven by functional group features.

These can be warheads, linkers, substituents for interaction with receptors - and they influence many molecular properties.

Functional Group Fingerprints capitalize on proven Medicinal Chemistry approaches, and two-dimensional searches are widely used

Functional Group Fingerprint Based Classification and Similarity Searching

-Classification based on 400 medicinally relevant functional groups-Classification translated into bit strings

Imigran (1): GSK, 1.07 billion dollar treatment for migraine in 2000.

The exclusions make the functional group definitions specific and make the entire set as orthogonal as possible.

Functional groups are recognized algorithmically using SMARTS

Functional groups are Defined to Minimize Overlap Between Definitions

The above imide is defined as an imide….. not two carbonyls, two amides and an amine.

Orthogonal Functional group definitions allow specific functional groups to be related to activity.

O

NH

O

Most common functional group classes in 2003 MDDR

Functional Group Frequency Functional Group Frequency

aromatic nitrogen 9,609 3o amine, not a arom. 8,4932o alcohol 8,104 aryl halide 7,8962o amide 7,805 acyclic ether, a arom. 7,262carboxylic acid 6,464 alkene 6,044carboxylic ester 5,199 cyclic ether 4,778aromatic alcohol 3,691 aromatic sulfur, thio. 3,4852o amine, not a arom. 3,281 aromatic sulfur 3,1853 o amide 3,113 acyclic ether 3,0183 o amine, a arom 2,938 imidazole, fused, no H 2,7371 o alcohol 2,662 1 o amine, a arom. 2,4881 o amine, not a arom. 2,474 acetal ketal 2,177beta lactam, fused 1,931 3 o alcohol 1,9213 o lactam 1,814 aromatic N-N, no H 1,709a b unsaturated acid 1,666 2 o amine, a arom. 1,634lactone 1,586 cyclic ether a 1 arom 1,552cyclic thioether 1,552 imidazole, fused, w/ H 1,533ketone 1,513 ketone, a arom. 1,476aromatic ketone 1,385 aromatic N w/ H 1,326aromatic oxygen 1,283 acyclic thioether a arom 1,240a b unsaturated ester 1,188 acyclic thioether 1,1661 o amide 1,146 2 o lactam 1,136oxime ether 1,135 trihalide 1,135nitrile 1,122 sulfonamide, NH 1,075urethane, NH 1,071 urea 864

General categories are shown, actual functional group classifications are more specific.

Classification Quality: Coverage and Overlap of Functional Group Definitions

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CMC MDDR MedChem

% C

over

age

and

Ove

rlap

IdealCoverage

IdealOverlap

Testing in medicinally relevant databases.Roughly 90% coverage and 10% overlap.

Coverage: All heteroatoms in molecule are classified.Overlap: A heteroatom in molecule classified in > 1 functional group.

CMC = 8,545MDDR = 135,342MedCh = 145,158

Biological Validation: 538 Target Classes in MDDR

# Functional Groups MDDR (Cumulative) # Cpds and # Clust in Tgt. Classes

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0 1 2 3 4 5 6 7 8 9 10

# Functional Groups

% C

ompo

unds

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0 2000 4000 6000 8000 10000 12000 14000

# Compounds: 537 Target Classes

# cl

uste

rs a

t Tan

imot

o 0.

3

• Active compounds randomly divided into test and training• Each Target Class had > 10 actives, or not included• On average: 473 actives in 94 clusters* (Daylight) for each class• Compounds in MDDR: 4.5 functional groups is median

* Clusters generated with Daylight fingerprints at Tanimoto = 0.3

Tanimoto Scores From Functional Groups

NH

N

N

SNH

O

NH

N

N

SNH

NH

N

SNH

OO

NH

N

SNH

OO

O

Count Tanimoto (C. Lerman)

B1 = FG in mol 1B2 = FG in mol 2BC = FG common to mol 1 and mol2

Tdist = (B1 + B2 - 2 * BC) / (B1 + B2 - BC)

1

2

3

4

1 2 3 41 ---- .25 0.60 0.67

2 ---- 0.67 0.50

3 ---- 0.20

4 ----

Distance Matrix

One functional group difference = distance 0.2 - 0.25

Tanimoto based on presence of functional group (binary) or counts (count)

Average Percentage Actives Recovered538 Target Classes in MDDR 2003

• Actives in each target class randomly divided test & training.• Recovery of test set using training set is graphed.

Recovery RatesTop Top Top Top100 500 1,000 5,000

Bin 25.7 49.6 59.4 75.8Ct 31.4 54.3 63.1 78.1Day 38.2 56.4 68.3 82.2Cons 37.7 65.0 74.5 87.9

> 60% Actives in top 1% DBase

MDDR 2003 > 135,000 cpds.0

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Ranked MDDR

% A

ctiv

es R

etre

ived

BinaryCountsDaylightConsensusRandom

Tanimoto Enrichment Rate Analysis 538 Target Classes in MDDR 2003

A = # actives at TanimotoB = # cpds total at TanimotoADB = total actives in DBaseNDB = total cpds in Dbase

E = (A / B) / (ADB / NDB)

Enrichment Rate Equation

Enrichments normalized for the number of actives in target class.

• Actives in each target class randomly divided test & training• Recovery of test set using training set is graphed

Example Biological Categories: MDDR 2003

Biology

Number Cpds With

Biology In Test

Set

Number Cpds With

Biology Retrieved Daylight

0.3

Number Cpds Total

Retrieved Daylight

0.3

Enhance Ratio 0.3 Daylight

Number Cpds With

Biology Retrieved

FGroup 0.3

Number Cpds Total

Retrieved FGroup

0.3

Enhance Ratio 0.3 FGroup

Number Cpds With

Biology Retrieved Consens

0.3

Number Cpds Total

Retrieved Consens

0.3

Enhance Ratio 0.3 Consens

Neurokinin NK2 Antagonist 147 132 3112 38.3 110 1488 66.7 106 469 203.9Neurokinin NK3 Antagonist 25 23 220 566.0 17 517 178.0 16 81 1069.4Protein Kinase C Inhibitor 225 199 1619 73.9 151 1056 86.0 145 503 173.4HIV-1 Protease Inhibitor 457 411 2547 47.8 336 1575 63.2 327 899 107.75HT1B Agonist 24 18 322 315.2 15 202 418.8 12 120 563.9mGluR1 Antagonist 20 13 95 926.0 8 298 181.7 7 47 1007.9Thrombin Inhibitor 555 493 3571 33.7 417 1698 59.9 399 1004 96.9Factor Xa Inhibitor 379 293 2307 45.4 238 1326 64.1 215 546 140.6GABA-B Receptor Antagonis 21 15 33 2929.5 8 33 1562.4 7 16 2819.6Adrenergic_beta_Blocker 89 70 494 215.5 76 618 187.0 67 228 446.9Potassium_Channel_Blocke 132 110 644 175.1 88 1512 59.7 86 345 255.6Sodium_Channel_Blocker 97 64 419 213.1 52 1775 40.9 49 220 310.8ACE_Inhibitor 266 232 3185 37.1 198 1169 86.2 182 642 144.2Estrogen_Receptor_Modulat 59 46 466 226.4 41 953 98.7 37 227 373.9Dopamine_D2_Agonist 80 71 434 276.8 53 1323 67.8 52 187 470.4Dopamine_D2_Antagonist 244 187 1283 80.8 158 3147 27.9 146 546 148.3Thymidylate_Synthetase_Inh 128 120 493 257.4 106 365 307.1 103 274 397.5Dihydrofolate_Reductase_Inh 72 61 322 356.1 61 340 337.2 58 255 427.6Renin_Inhibitor 599 575 3164 41.1 527 1766 67.4 516 1337 87.2Trypsin_Inhibitor 51 40 485 218.9 32 133 638.5 29 93 827.5Antiviral 1635 1377 11080 10.3 1167 7345 13.2 1116 4121 22.4Antiinflammatory 2363 1915 13221 8.3 1495 14285 6.0 1376 5603 14.1

The circles are drawn to scale.

Consensus Approach: Overlap of True Positives from FG Count and Daylight

Number of true and false positives for the Functional Group Fingerprint counts, Daylight fingerprint and consensus (logical “AND”) approaches for the five hundred and thirty eight biological target classes at Tanimoto distances of 0.0, 0.1, 0.2, and 0.3. The three methods are binned at the various Tanimoto distances and are reported in the order of counts, Daylight, consensus, and are listed as C, D and A, respectively.

Performance of the FG Count, Daylight and Consensus Approaches in Terms of True and False Positives

FG = FG CountD = DaylightC = Consensus

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FG, 0

.0

D, 0

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C, 0

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FG, 0

.1

D, 0

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C, 0

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FG, 0

.2

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.3

Tanimoto Distance for Methods

Num

ber o

f Com

poun

ds

Functional Group Fingerprint Conclusions

• Developed a 2D fingerprint searching method that uses medicinally relevant, custom defined, functional groups.

• Validated the method on a large dataset of biological targets and chemical classes (538 target classes, 473 cpds 90 clust per class).

• Factor of 2 gain in accuracy through reduction of false positives.• Deploy method company-wide.

• Rapidly expand SAR in Hit/Lead Identification.• Subset the corporate collection for screening.• Factor of 2 improvement in 2D searches.

Acknowledgement: Dave Cosgrove AstraZeneca