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The University of New Mexico Health Sciences Center

SCHOOL OF MEDICINE

Lead-like, Drug-like or Pub-like:Lead-like, Drug-like or Pub-like:How Different Are They?How Different Are They?

Tudor I. Oprea, Tharun K. Allu, Dan C. Fara, Ramona F. Rad, Lili Ostopovici, Cristian G. Bologa

UNM Division of Biocomputing UNM Division of Biocomputing

A Symposium to Honor Yvonne C. MartinACS Chicago, 25 March 2007Copyright © Tudor I. Oprea, 2007. All rights reserved

J. Comput. Aided Mol. Design 2007, 21:113–119http://screening.health.unm.edu/supplements/LeadDrugPub2.csvfor supplementary materials

The University of New MexicoSCHOOL OF MEDICINE

Brief History (how I met Yvonne)Brief History (how I met Yvonne)

• First contact: EuroQSAR 1992 (Strasbourg), where she expressed interest in one of my posters

• Continuing contact: EuroQSARs, Gordon Conferences (QSAR/CADD), MUGs, CUPs

• Yvonne loves to visit New Mexico

• I followed her in leading the QSAR Society

• (in style, too)

• We renamed it The Cheminformatics and QSAR Society

• Disclosure: I have yet to visit Abbott Labs


Molecular Libraries InitiativeMolecular Libraries Initiative

NIH Roadmap InitiativeNIH Roadmap Initiative

250-300 thousand small molecules

Hundreds of HTS Assays

SAR matrix

The NIH Roadmap: Some Numbers The NIH Roadmap: Some Numbers

4 Chemical SynthesisCenters

4 Chemical SynthesisCenters

MLSCN (9+1)9 external centers 1 NIH intramural

20 x 10 = 200 assays

MLSCN (9+1)9 external centers 1 NIH intramural

20 x 10 = 200 assays

PubChem(NLM)

PubChem(NLM)

ECCR (6)ExploratoryCenters

ECCR (6)ExploratoryCenters

CombiChemParallel synthesis

DOS4 centers + DPI

100k–500k compounds

CombiChemParallel synthesis

DOS4 centers + DPI

100k–500k compounds

Predictive ADMET (8)

Predictive ADMET (8)

Slide modified from Alex Tropsha (UNC)

OUTPUT:ChemicalProbes


So, what So, what IsIs A Chemical Probe? A Chemical Probe?

• Answer A: It has not been decided yet

• Answer B: A chemical probe is a somewhat selective, somewhat potent (sub-micromolar?) structure that works on the target / phenotypic assay of interest; it should be reasonably …soluble (I guess)

• Chemical Probes are not necessarily anticipated to work in vivo (though it will be preferred) and not expected to lead to drugs either

• Chemical Probes could be used for assay development & optimization, for imaging, radio-labeling, for flow cytometric analyses, etc. (not necessarily used to query biological space only)


Thus, Thus, Why Bother with Drug Properties?Why Bother with Drug Properties?

• Drug Discovery scientists sometimes think that the name of the game is to get high affinity to the target receptor, and then…

game over

• Not quite…

Neuraminidase Inhibitors for InfluenzaNeuraminidase Inhibitors for Influenza• X-ray structure guided rational design

• GRID-suggested replacing -OH with basic functionality• Physical properties not amenable for oral delivery

• GSK markets this as Relenza, • First drug for influenza

Nature 1993, 363, 418.

OCO2H

OH

OH

OH

NH

OH

O

IC50 = 8600nM IC50 = 5nM

Zanamivir

O

CO2H

OH

OH

OH

NH

NH

O

NH2

NH

Lead molecule

Slide modified from Andy Davis (AstraZeneca R&D Charnwood)

J. Am. Chem. Soc. 681, 119, 1997 J. Med. Chem. 2451, 41, 1998

IC50 = 1nM

CO2H

OH

NH

NH2

O

O

O

H

H

O

OH

IC50 = 150nM

Glu 276

Gilead Neuraminidase InhibitorsGilead Neuraminidase Inhibitors

• Zwitterion not amenable for oral delivery

• Ethyl ester (oseltamivir) good oral absorption, duration• Marketed as Tamiflu, first oral drug for influenza

O

CO2HNH

NH2

O


Relenza vs TamifluRelenza vs Tamiflu

• Both potent neuraminidase inhibitors

• Relenza: Zanamivir delivered by using Diskhaler

• Tamiflu simple tablet formulation• Deesterified in plasma long plasma T½

• Tamiflu (marketed by Roche)• took 65% U.S. market-share from Relenza in 7 weeks

• Q1/Q2 2002 sales Relenza vs Tamiflu• Relenza market share fallen to 10%

• GSK quoted reason “Slowness of the US to adopt inhalation therapies”

Inhalation to Overcome Low BioavailabilityInhalation to Overcome Low Bioavailability


Blood potencyFold over pA2

MEC=( Ki x ff x 3)VDss and Clearance

1 dose a day ? 2 doses a day ? Bioavailability

Holistic Drug DesignHolistic Drug Design

300mg/kg/day

20000 mg

< 3mg/kg/day

200 mg

Oral

kVDMEC el

%

1)exp(.24

)(mg/kg/day DoseHumanPredictedss


MedChem Space & CLogPMedChem Space & CLogPA. Hopkins et al: LE = A. Hopkins et al: LE = ΔΔG/N_at > 0.3G/N_at > 0.3

cLogP < 00 < cLogP < 4.5cLogP > 4.5

10 20 30 40 50

2

4

6

8

10

12

Fre

e E

nerg

y of

B

indi

ng (

≈1.4

2*-lo

g(A

ct)

14

16

18

20

99,119 out of 135,673 records have LE ≥ 0.3.

Of these, 78,393 have activity ≥ 1 μM, and 29,980 activity ≥ 10 nM.

1,310 17,38911,281

Nr. heavy atoms (N_at)


The Mis-Use of RO5 ScoresThe Mis-Use of RO5 Scores

0%

10%

20%

70%

80%

PASS FAIL SKIPPED

ACD

MDDRPDR

T.I. Oprea, J Comput-Aided Mol Des 2000 14, 251-264

• Pharmaceutical lead discovery world-wide apply Lipinski’s Rule of 5: MW ≤ 500, cLogP ≤ 5, HDO ≤ 5, HAC ≤ 10. Any two violations = poor %Oral

• Ro5 does not discriminate “druglikeness”. Its use is intended as filter in early HTS hit analysis/discovery. Problem is, it is applied literally (but was derived from drugs, not leads).


Common Sources for Leads(*) in Common Sources for Leads(*) in Drug DiscoveryDrug Discovery

(*) Chemical Probes in the NIH Roadmap(*) Chemical Probes in the NIH Roadmap

• One needs to distinguish “leadlike” leads from other sources of lead structures, e.g., natural products that are high-affinity compounds (NPY or taxol are leads!) or from “druglike” leads that are marketed structures (e.g., salbutamol or HTS actives from “normal” combichem)

Difficult to optimise

Serendipity

Easier to optimise

S. Teague et al., Angew. Chem., 1999, 38, 3743-3748

Druglike leadsaffinity > 0.1 MMW > 450CLogP > 4.5

Leadlike leadsaffinity > 0.1 MMW ≤ 300CLogP ≤ 3.0

DRUG

High-affinity leadsaffinity << 0.1 MMW >> 450CLogP < 4.5


4 Generations of Progesterone Derivatives4 Generations of Progesterone Derivatives

O

O

H

H H

Progesterone(1934)

O

O

O

H

H

H

O

Medroxyproges-terone acetate

(1958)

O

O

HH

H H

Norethindrone(1958)

O

O

H H

HH

Levonorgestrel(1978)

O

H H

H H

Desogestrel(1982)

O

O

H H

H

O

Cl

H

HO

Cyproteroneacetate(1974)

O

H H

H H

O

Etonogestrel


The First Synthetic Drugs…The First Synthetic Drugs…

N O

O

O

O

Cocaine(1884)

N

O

O O

Orthocaine(1896)

O

N

O

O

O

N

Nirvanin(1898)

N

O

O

Amylocaine(1902)

N

Coniine(Socrates)

O

O

N

N

Procaine(1910)

N

O

N

N

Procainamide(1953)

O

N

N

Cl

O

N

Metoclopramide(1964)

F

ON

N

O

N

Cl

O

O

Cisapride(1986)

N

N

OO

Br

O

Remoxipiride(1988?)


More Synthetic Drugs…More Synthetic Drugs…

N O

O

O

O

Cocaine(1884)

N

O

O O

Orthocaine(1896)

O

N

O

O

O

N

Nirvanin(1898)

O

ON

Gravitol(1929)

N

S

NPrometazine(1946)

N

S

N

Cl

Chlorpromazine(1952)

N+

N

O

N

Cl

Chlor-diazepoxide

(1960)

N

N

O

Cl

Oxazepam(1965?)

NO

O

Prosympal(1933)

N

N

O

Cl

O

Diazepam(1963)

N

S

O

O

N

O

O

Diltiazem(1981?)


Leads 4: Leads 4: HH22 Blockers Blockers

NN

N N

N

NO

S N

N SN

OS N

N

N+

O

O

NN

N N

S

NN

S N N

N

N

N-alpha-guanyl histamine

Ranitidine

Cimetidine

AH1866

Burimamide

toxic

Tautomerismbelieved to beessential pKa < 3

pKa < 3Isostericreplacement

Interferes with drug(P450) metabolism

Consequences…

1. Tagamet® is replaced by Zantac® as nr.1 best-selling drug

2. Income from Zantac® boosts Glaxo to nr.1 in top 10 pharma

3. Glaxo acquires SmithKline Beecham

… all because of an imidazole-excessive patent coverage

T. I. Oprea et al., J. Chem. Inf. Comput. Sci., 2001, 41, 1308-1315


People always think that “newer drugs” People always think that “newer drugs” have higher MW than “older drugs” have higher MW than “older drugs”

• MW increase: ~50dalton/40 years• The Property Space applies to most of us

Slide from Andy Davis, AstraZeneca Charnwood

Mean molecular weight against year of introduction for oral marketed drugs

0

100

200

300

400

1930

1940

1950

1960

1970

1980

1990

DECADE

ME

AN

MO

LE

CU

LA

R

WE

IGH

T


0

100000

200000

300000

400000

500000

0 200 400 600 800

MW (a.m.u.)

Compounds withgiven MW

Cumulative(observed)

Cumulative(exponentialestimate)

Current Chemical Space SamplingCurrent Chemical Space Sampling

Well-sampledchemical space

Under-sampledchemical space

M.M. Hann & T.I. Oprea, Curr. Opin. Chem. Biol., 2004, 8, 255-263


Is There a Preferred Property Space Is There a Preferred Property Space for Leads & Chemical Probes?for Leads & Chemical Probes?

• There is a tendency in the academic sector to ignore past mistakes from the pharmaceutical industry, e.g., the “tyranny of Lipinski” and “we don’t care about in vivo, we just want chemical probes”, which is unfortunate… since the output of academic research ought to result in tools to better understand biology (pharmacology, chemical biology, etc)

• So the reason for this talk is to learn from drug discovery, and from the failures in the pharma sector?

T. I. Oprea et al., J. Chem. Inf. Comput. Sci., 2001, 41, 1308-1315; updated


Leads and Actives DatasetsLeads and Actives Datasets• 385 leads and the 541 drugs that emerged from these leads,

which resulted by combining previously described datasets (Hann et al., Proudfoot, Oprea et al)

• Compounds of current interest extracted from PubChem, categorized according to their source and PubChem activity label, as follows:

• 152 ‘‘actives’’ from MLSMR and MLSCN, referred to as MLSMR Act;

• 46 ‘‘actives’’ from Nature Chemical Biology, tested in MLSCN, referred to as NCB Act;

• 1,488 ‘‘inactives’’ from MLSMR and MLSCN, referred to as MLSMR Inact;

• 72 ‘‘inactives’’ from Nature Chemical Biology, tested in MLSCN, referred to as NCB Inact;

T. I. Oprea et al. J. Comput. Aided Mol. Design 21:113-119, 2007


Drugs & Bio-Activity DatasetsDrugs & Bio-Activity Datasets• Compounds in pharmaceutical development, extracted from

the MDDR (MDL Drug Data Report) 2005.2 database, categorized according to their clinical testing phase, in the following manner:

• 1,147 launched drugs;• 301 compounds in phase III clinical trials, referred to as Phase III;• 1,047 compounds in phase II clinical trials referred to as Phase II;• 801 compounds in phase I clinical trials, referred to as Phase I;

• Compounds extracted from WOMBAT 2006.1, which indexes papers published in mainstream medicinal chemistry journals, split in 2 categories:

• 30,690 compounds for which the biological activity is above 1 lM, or below 6 units on the –log10 (activity) scale, on all of the documented literature assays (WB6);

• 5,784 compounds for which the biological activity is below 1 nM, or above 9 units on the –log10 (activity) scale, in one of the documented literature assays (WB9). Of these, only 127 were launched drugs.


WOMBAT 2006.1WOMBAT 2006.1

• WOMBAT 2006.1 contains 154,236 entries (136,091 unique SMILES), totaling 307,700 biological activities on over 1,320 unique targets.

• WOMBAT 2006.1 contains 6,801 different series from 6,791 papers published in medicinal chemistry journals between 1975 and 2005

• Systematic coverage for: J. Med. Chem. (77.6%) 1991-2004 [complete], 2005 [partial], Bioorg. Med. Chem. Lett. (15.4%) 2002-2003 [complete], 2004 [partial], Bioorg. Med. Chem. (5.6%) 2002-2003 [complete], Eur. J. Med. Chem. (1%) 2002-2003 [complete] 2004 [partial];

• SwissProt IDs for ~88% of the Entries

• DOI (digital object identifier) links & PubMed IDs for all references (direct access to PDF files for institutions with appropriate subscriptions)

• ClogP and XMR from Biobyte Corporation (Al Leo), AlogP and LogSw from ALOGPS (Igor Tetko), Ligand Efficiency, Rule-of-Five, and Molecular Complexity can be queried.

• Over 10,000 unique entries are added every six months

M. Olah et al. Chemical Biology, Wiley-VCH 2007, in press


Databases Scanned for Molecular PropertiesDatabases Scanned for Molecular PropertiesType Count Source; Comments

Leads 385 From Hann, Proudfoot, Oprea datasetsSMR MLSCN Act 152 MLSMR; Actives only in MLSCN assaysNCB Act 46 Nature Chem Biol; Actives in any assayWB9 5,784 WOMBAT 2006.1; nM compoundsDrugs 541 Marketed drugs from the above Leads setMDDR_Launched 1,147 MDDR 2005.2, all launched drugsMDDR_Phase I 808 MDDR 2005.2, all phase I candidate drugsMDDR_Phase II 1,061 MDDR 2005.2, all phase II candidate drugsMDDR_Phase III 303 MDDR 2005.2, all phase III candidate drugsSMR MLSCN Inact 1,488 MLSMR; Inactives only in MLSCN assaysNCB Inact 72 Nature Chem Biol; Inactives in any assayWB6 30,690 WOMBAT 2006.1; M compounds


Note: PubChem queries were performed in August 2006; some of the structures & “active” definitions may have changed – this is an evolving database, and users sometimes revise published data


Leads, Drugs, MDDR, WOMBAT - Leads, Drugs, MDDR, WOMBAT - midpointsmidpoints

All values are the median (50% distribution moment)T. I. Oprea et al. J. Comput. Aided Mol. Design 21:113-119, 2007

Type Count MWSMCM RNG HAC RTB CLP TLogP TLogSw Ro5Leads 385 287.4 37.6 3 4 4 2.3 2.3 -3.3 0SMR MLSCN Act 152 273.9 30.0 3 4 4 2.5 2.5 -3.3 0NCB Act 46 273.9 37.2 3 3 2 3.1 3.2 -3.8 0WB9 5,784 463.6 56.7 4 6 10 3.8 3.6 -4.7 1Drugs 541 332.7 43.3 3 4 6 2.6 2.6 -3.8 0MDDR_Launched 1,147 345.4 42.7 3 5 6 2.3 2.4 -3.7 0MDDR_Phase I 808 420.6 51.3 3 6 8 3.0 2.9 -4.3 0MDDR_Phase II 1,061 399.5 49.2 3 6 8 3.2 2.9 -4.3 0MDDR_Phase III 303 378.9 46.4 3 5 7 2.7 2.6 -4.0 0SMR MLSCN Inact 1,488 260.3 28.5 2 4 4 2.0 2.0 -3.0 0NCB Inact 72 254.8 31.2 2 4 3 1.6 1.7 -2.6 0WB 6Only 30,690 364.4 42.6 3 5 6 3.0 2.9 -4.2 0

7 11 3 15 27 11 3 15 21 19 21 41 19 21 4

6 8 56 8 5

InterpretationInterpretation

Complexity(R2=0.55against MW)source codeavailablefrom UNM

Nr of RINGS

Nr of H-bondacceptors; nosignificant changein H-bond donors

Nr of flex.bonds

2 methodsfor LogPoct

LogSwat:“intrinsic”aqueoussolubility


Actives, Drugs, WB9 & Inactives – Actives, Drugs, WB9 & Inactives – Median & Tail ValuesMedian & Tail Values

Type Count MW SMCM RNG HAC RTB CLogP TLogPTLogSw Ro5Actives 50 569 284.3 35.2 3 4 4 2.5 2.4 -3.3 0Actives 90 569 432.5 59.8 4 8 9 5.1 4.6 -5.1 1Drugs 50 1,651 339.5 42.6 3 5 6 2.3 2.3 -3.7 0Drugs 90 1,651 558.9 73.2 5 12 14 5.3 4.8 -5.5 2WB 9 Mul 50 5,784 463.6 56.7 4 6 10 3.8 3.6 -4.7 1WB 9 Mul 90 5,784 761.1 94.7 6 14 25 6.5 5.6 -6.0 2Inactives 50 32,114 358.4 41.8 3 5 6 2.9 2.8 -4.1 0Inactives 90 32,114 581.7 70.0 5 11 17 6.0 5.2 -5.8 2

• All “90” values are the 90% distribution moment, except TLogSw (@ 10%)

• Actives are less complex, less flexible, slightly more soluble than drugs

• WB9 (literature) actives are more complex, more flexible, more hydrophobic & less soluble when compared to actives & drugs



Actives, Drugs, WB9 & Inactives – Actives, Drugs, WB9 & Inactives – ChangeChange from Median & Tail Values from Median & Tail Values

Type Count MW SMCM RNG HAC RTB CLogP TLogPTLogSw Ro5Actives 50 569 284.3 35.2 3 4 4 2.5 2.4 -3.3 0Actives 90 569 432.5 59.8 4 8 9 5.1 4.6 -5.1 1Drugs 50 1,651 55.2 7.4 0 1 2 -0.2 -0.1 -0.4 0Drugs 90 1,615 126.4 13.4 1 4 5 0.3 0.2 -0.3 1WB 9 Mul 50 5,784 179.3 21.5 1 2 6 1.4 1.2 -1.4 1WB 9 Mul 90 5,784 328.6 34.9 2 6 16 1.4 1.0 -0.9 1Inactives 50 32,114 74.1 6.6 0 1 2 0.4 0.4 -0.8 0Inactives 90 32,114 149.2 10.2 1 3 8 1.0 0.6 -0.7 1

• This analysis can give us trends & boundaries for this property space, e.g., we could use tail-end values from the above to filter for lead- or “pub”-like-ness (is there a preferred space for chemical probes?)

• Learn from the WOMBAT “nM” ligands: these are failed medchem projects from big pharma (i.e., not too good as leads). Data very useful for ideas, but I would not advise anyone to use these as leads

• Note: if working with natural products, these filters will fail



Understanding the Property Space for ActivesUnderstanding the Property Space for Actives• 100 MW 432.5, -4 ClogP 5.1, LogSw ≥ -5.1, *Hdon 4,

*Hacc 9, *SMCM 60, *RTB 9, *RNG 4 (*low value 0):

• This filter covers 68.5% of the actives, 50.5% of the drugs, 17.7% of the WB nM ligands, and 49.8% of the WB μM ligands.

• 100 MW 432.5, -4 ClogP 5.1, LogSw ≥ -5.1, *Hdon 4, *Hacc 9, *SMCM 73.2, *RTB 14, *RNG 5 (low value 0):

• This filter covers 75% of the actives, 59.7% of the drugs, 26.4% of the WB nM ligands, and 54.5% of the WB μM ligands.

• 100 MW 559, -4 ClogP 5.3, LogSw ≥ -5.5, *Hdon 5, *Hacc 12, *SMCM 73.2, *RTB 14, *RNG 5 (low value 0):

• This filter covers 81.4% of the actives, 69.3% of the drugs, 46.6% of the WB nM ligands, and 64.8% of the WB μM ligands.

• At ClogP > 4.1 & LogSw < -4.1, we find 15.1% of the actives, 19.4% of the drugs, 39.5% of the WB nM ligands, and 27.8% of the WB μM ligands

• Obs: none of the above combinations yields Ro5 ≥ 2 violations!!!


(Revised) Guidelines for Probe Discovery(Revised) Guidelines for Probe Discovery• The following (restrictive!) properties could be considered when evaluating

chemical probes, in particular when many HTS primary hits come out:

• 100 MW 432.5, -4 ClogP 5.1, LogSw ≥ -5.1, 0 Hdon 4, 0 Hacc 9 (inspired from the tail-end of Actives)

• Obs.: MW cut-off in Ro5 is 427, according to Michal Vieth• SMCM 73.2, RTB 14, RNG 5 (from the tail-end of Drugs) • Good probes require that subtle interplay between solubility and

permeability, in order to work in cells and in vivo.• For further progression (lead opt.), additional properties are required:

• %F ≥ 30, CL 30 mL/min, %PPB 99 (in rat PK models)• KD ≥ 100 M for drug-metabolizing P450s (no DDIs)• Preferably, no acute toxicity, no carcinogenicity, etc.• These cut-off values will change with each target, its location (e.g., brain vs.

stomach vs. bone vs. kidney), with the intended admin. mode… These values are not the pharma equivalent of the Planck constant


ConclusionsConclusions• The MLSMR & NCB actives (N=198) has similar property

distribution with the historical Leads (N=385).

• It’s interesting to compare the differences between the property distribution values of the 569 Actives and the 5,784 high-activity molecules (WB9).

• The WB9 subset contains molecules that are, on average, larger, more hydrophobic and less soluble than any of the other datasets examined here.

• Pub-like Actives are, on average, smaller, less complex, less hydrophobic and more soluble than the other datasets.


Conclusions 2Conclusions 2• As we discover new chemical probes, the issue of what

constitutes high-quality probes is under scrutiny.

• Arguments such as ‘‘historical bias’’ are used when it comes to defining property boundaries (the ‘tyranny of Lipinski’).

• Yet, the number of new approved drugs /year continued to decline in the past decade, despite significantly larger numbers of molecules & targets explored.

• Whether the boundary limits will be extend beyond the Ro5 ‘‘cube’’, only time will tell.

• Over 55% of the top 200 oral drug products in the United States, Great Britain, Japan and Spain are ‘‘high-solubility drugs’’ [*], and that only 18 of the 133 active principles from these drugs have ClogP values greater than 4.0.

[*] Takagi T et al., Mol. Pharmaceutics 2007, 3:631


In Fairness:In Fairness: Computer Aided Drug Design Computer Aided Drug DesignMarketed drugs whose discovery was aided by computers

Generic name

Brand Name

US Approval

CADD Method

Therapeutic Category

Norfloxacin Noroxin 1983 QSAR Antibacterial

Losartan Cozaar 1994 CADD Antihypertensive

Dorzolamide Trusopt 1995 CADD Antiglaucoma

Ritonavir Norvir 1996 CADD Antiviral

Indinavir Crixivan 1997 QSAR Antiviral

Donepezil Aricept 1997 QSAR Anti-Alzheimer's

Zolmitriptan Zomig 1997 CADD Antimigraine

Nelfinavir Viracept 1997 SBDD Antiviral

Amprenavir Agenerase 1999 SBDD Antiviral

Zanamivir Relenza 1999 SBDD Antiviral

Oseltamivir Tamiflu 1999 SBDD Antiviral

Lopinavir Aluviran 2000 SBDD Antiviral

Imatinib Gleevec 2001 SBDD Antineoplastic

Erlotinib Tarceva 2004 SBDD Antineoplastic

Ximelagatran Exanta 2004 (EU) SBDD Anticoagulant


AcknowledgmentsAcknowledgments

• Simon Teague, Andy Davis and Paul Leeson (AstraZeneca R&D Charnwood), Mike Hann, Andrew Leach and Gavin Harper (GSK Medicines Research Centre, Stevenage), and John Proudfoot (Boehringer Ingelheim, Ridgefield CT) worked on the leadlike concept

• WOMBAT Team: Maria Mracec, Marius Olah, Lili Ostopovici, Ramona Rad, Alina Bora, Nicoleta Hadaruga, Ramona Moldovan, Dan Hadaruga (Romanian Academy Institute of Chemistry, Timisoara, Romania)

• Funding: • NIH U54 MH074425-01


Yvonne Likes To Keep Her Eyes OpenYvonne Likes To Keep Her Eyes Open

• Here, giving her “grandma” talk


QSAR RebornQSAR RebornA Symposium in Honor of Philip MageeA Symposium in Honor of Philip Magee

• Dr. Phil S. Magee (1926-2005) was one of the pioneers in utilizing QSAR, mostly in agrochemistry and transdermal property modeling. Served as first Chair of the QSAR and Modeling Society

• Symposium organized by Bob Clark, John Block, Lowell Hall & Lermont Kier

• At ACS Boston, August 2007, sponsored by the COMP, CINF and AGRO Divisions

• Deadline for Abstracts: April 2, 2007• Topics: QSAR Descriptors, QSAR Techniques, and

QSAR Applications

the university of new mexico health sciences center school of medicine lead-like, drug-like or...

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