PREDICTION OF ACTIVITY SPECTRA FOR SUBSTANCES: TWENTY ... · PREDICTION OF ACTIVITY SPECTRA FOR...

PREDICTION OF ACTIVITY SPECTRA FOR SUBSTANCES: TWENTY YEARS

OF THE DEVELOPMENT

Vladimir Poroikov, Prof. Dr.

Institute of Biomedical Chemistry of Rus. Acad. Med. Sci., Pogodinskaya Str. 10/8, Moscow, 119121, Russia

http://www.ibmc.msk.ru

The Mission:

Better Medicines through Global Education

and Research

The International Union of Basic and Clinical Pharmacology

http://www.iuphar.org/

Target(s) Ligand(s)

Disease

> 2,000 targets (Thomson Reuters

Integrity)

~ 14,400 codes (ICD-10)

~60 M samples (ChemNavigator) ~166 B virtual (JCIM, 2012, p.56) ~10120 possible (JCICS, 2003, p.374)

Changing of paradigm: from «target-centric» approach to the analysis of signal transduction regulatory networks & pathways

Nature 2009, 462: 175-181.

XX century Disease → Target → Drug

XXI century Multitargeted drugs

Due to biological

activity, chemical

compound may be

used as a medicine

for treatment

of certain disease.

Due to biological

activity, chemical

compound may

cause adverse

or toxic effects

in human.

How to estimate biological activity spectra of pharmacological agents?

Clinical trials:

- Risks for a human health. Pre-clinical studies:

- Time-consuming and expensive. Computational prediction:

- Decreasing of risks, time and cost. - Can be done for virtual structures.

Outline 1. Computational approaches to biological activity

prediction.

2. NetFlowEx (targets identification)

3. PASS: Prediction of Activity Spectra for Substances.

4. PharmaExpert.

5. GUSAR: General Unrestricted Structure-Activity Relationships.

6. Some applications.

7. Web-services.

8. Summary.


prediction.



4. PharmaExpert.



7. Web-Services.

8. Summary.

Approaches to discover new pharmaceutical agents

3D structure of the target and structural formulae of ligands

are known. 3D structure of the target is

unknown but structural formulae of ligands are known.

3D structure of the target is

known but structural formulae of ligands are unknown.

3D structure of the target and

structural formulae of ligands are unknown.

Structure-based design Ligand-based design

De-novo design

Combinatorial chemistry and HTS

Ivanov A.S., Poroikov V.V., Archakov A.I. Bioinformatics: way from genomes to drug in silico. Herald of Russian State Medical University, 2003, issue 4 (30), 19-23.

Target-based approaches to prediction of biological activity

Prerequisites: Data about 3D structure of target macromolecule (X-

ray, NMR, Modeling). Data about 3D structure of active site (binding site).

Methods: Docking and estimation of binding energy (scoring

function). Active site mapping and de novo design.

Limitations of Target-Based Drug Design Methods

3D structure of the target is necessary.

3D structure in crystal vs. 3D structure in solution.

Approximation of energy binding estimates.

Approximation of 3D conformation of flexible

ligands.

The simplest Ligand-Based Drug Design methods are based on a similarity principle: “Me-too-drugs” design

Wermuth C. The Practice of Medicinal Chemistry, 3rd edition, 2008, p.126.

Quantitative estimation of similarity

Similarity search in ChemNavigator library

Acetylsalicylate

http://www.chemnavigator.com

The most similar compounds for Acetylsalicylate

TC=99%

TC=87%

TC=86%

TC=86%

TC=84%

TC=84%

…”there is only a 30% chance that a compound that is > 0.85 (Tanimoto) similar to an active is itself active”.

Martin Y. et al. J. Med. Chem. , 2002, 45, 4350-4358.

“The best material model for a cat is another [cat], or preferably the same cat".

Norbert Wiener, The Role of Models in Science.

Ligand-based approaches to prediction of biological activity

Prerequisites:

Set of ligands with known biological activity (training set).

Methods:

(Quantitative) Structure-Activity Relationships (Q)SAR, Pharmacophore models.

N

SN

O O

O

ON O

N

O

NNN

O O

O

FO

H

HN

NO

O

FF F

FF F

H

H

H

IC50 (µM): 0.1 12 87 0.03

. . .

. . .

Activity: Active Inactive Inactive Active . . .

18


prediction.



4. PharmaExpert.



7. Web-Services.

8. Summary.

Dichotomic modeling of regulatory networks by NetFlowEx program for identification of antitumor targets

Cell cycle arrest? or

Apoptosis?

S(0)

S(n)

State of node i Si=1

bik=-1 bik=1

Edge property

Sk=1 Sk=0 k

i

k

Active node

Inactive node

Inactive edge of activation Active edge of inhibition Active edge of activation

Primary states

Fi (S1, S2, … ,Sn) = Θ(ai + ∑kSkbki)

Koborova O.N. et al. SAR and QSAR Environ. Res., 2009, 20 (7-8), 755-766.

X X X

HER2/neu-positive breast carcinomas.

Ductal carcinoma. Invasive ductal

carcinoma and/or a nodal metastasis.

Generalized breast cancer.

Input Data for Breast Cancer Modeling

Microarray data for breast cancer

Cyclonet database http://cyclonet.biouml.org

Regulatory network TRANSPATH® database

Fragment: 2336 edges and 1405 nodes


Simulation of tumor cell division

Generalized breast cancer

Apoptotic proteins

Cell cycle complexes

Cell cycle regulatory proteins

Steps of trajectory

Active Inactive

Prot

eins

/gen

es

Proteins regulating cell cycle and apoptosis


No Target 1 Target 2 Target 3 Number of compounds

1 Bcl2 antagonist Cyclin-dependent kinase 2 inhibitor

4

2 Bcl2 antagonist Myc inhibitor 10 3 Bcl2 antagonist Phosphatidylinositol 3-kinase

beta inhibitor 10

4 Cyclin-dependent kinase 2 inhibitor

Myc inhibitor 3

5 Hypoxia inducible factor 1 alpha inhibitor

Myc inhibitor 7

6 Hypoxia inducible factor 1 alpha inhibitor

Phosphatidylinositol 3-kinase beta inhibitor

10

7 Myc inhibitor Phosphatidylinositol 3-kinase inhibitor

10

8 Bcl2 antagonist Myc inhibitor Phosphatidylinositol 3-kinase beta inhibitor

10

Some Double and Triple Targets’ Combinations Identified For Breast Cancer



prediction.



4. PharmaExpert.



7. Web-Services.

8. Summary.

The key persons in PASS development

Dmitry Filimonov

Tatyana Gloriozova

Vladimir Poroikov

Alexey Lagunin

PASS 2012 Characteristics

1. Filimonov D.A. et al. J. Chem. Inform. Comput. Sci., 1999, 39, 666. 2. Filimonov D.A., Poroikov V.V. In: Chemoinformatics Approaches to Virtual

Screening. RSC Publ., 2008, 182-216. 3. Poroikov V.V. et al. J. Chem. Inform. Comput. Sci., 2000, 40, 1349.

Training Set

313,345 drugs, drug-candidates, pharmacological and toxic substances comprise the training set

Biological Activity

6400 biological activities can be predicted (Active vs. Inactive)

Chemical Structure Multilevel Neighborhoods of Atoms (MNA) descriptors [1, 2]

Mathematical Algorithm

Bayesian approach was selected by comparison of many different methods [2]

Validation

Average accuracy of prediction in LOO CV for the whole training set is ~95% [2]; robustness was shown using principal compounds from MDDR database [3]

Components of biological activity spectra predicted by PASS

Main pharmacological effects (antihypertensive, hepatoprotective, anti-inflammatory etc.);

Mechanisms of action (5-HT1A agonist, cyclooxygenase 1 inhibitor, adenosine uptake inhibitor, etc.);

Specific toxicities (mutagenicity, carcinogenicity, teratogenicity, etc.);

Interaction with Antitargets (HERG channel blocker, etc.);

Metabolic terms (CYP1A substrate, CYP3A4 inhibitor, CYP2C9 inducer, etc.);

Influence on gene expression (APOA1 expression enhancer, NOS2 expression inhibitor, etc.);

Action on transporters (Dopamine transporter antagonist, Sodium/bile acid cotransporter inhibitor, etc.).

MNA/0: C MNA/1: C(CN-H) MNA/2: C(C(CC-H)N(CC)-H(C))

CCH

CO

O

NC

H

C

CH

H H

Multilevel Neighborhoods of Atoms (MNA) Descriptors

CCH

CO

O

NC

H

C

CH

H H

CCH

CO

O

NC

H

C

CH

H H

Filimonov D.A. et al. J. Chem. Inform. Comp. Sci., 1999, 39: 666-670.

Algorithm for Prediction of Biological Activity Spectra

According to the Bayes' theorem, the probability P(A|S) that the compound S has activity (or inactivity) A, equals to: P(A|S) = P(S|A)•P(A)/P(S) If the descriptors of organic compound D1, ..., Dm are independent, then: P(S|A) = P(D1, ..., Dm|A) = ПiP(Di|A) P(A) and P(A|Di) are calculated as sums through all compounds of the training set:

∑∑=

k ik

k kiki )(Dg

(A))w(Dg)|DP(A

∑ ∑∑ ∑=

i k ik

i k kik

)(Dg(A))w(Dg

P(A)

Anxiolytic Sedative

5HT1A Inhibitor Carcinogen

. . .

Structural formula of new compound

Estimating of probability for each particular biological activity

Predicted biological activity spectrum

How PASS predicts biological activity spectrum?

Pa – probability to be active, Pi – probability to be inactive

Example of prediction for Clopidogrel

Some publications, where PASS algorithm is described Filimonov D.A., Poroikov V.V. (2008). Probabilistic Approach in Virtual Screening. In: Chemoinformatics Approaches to Virtual Screening. Alexander Varnek and Alexander Tropsha, Eds. RSC Publishing, 182-216.

Filimonov D.A., Poroikov V.V. (2006). Prediction of biological activity spectra for organic compounds. Russian Journal of General Chemistry, 50 (2), 66-75.

Poroikov V., Filimonov D. (2005). PASS: Prediction of Biological Activity Spectra for Substances. In: Predictive Toxicology. Ed. by Christoph Helma. N.Y.: Taylor & Francis, 459-478.

Descriptors (1999) Robustness (2000) Drug-likeness (2001) PASS Online (2002) NCI Browser (2003)

18977 compounds with 124 activities were selected from MDDR.

The set of compounds was 50 times divided at random into two equal subsets.

The first subset was used as the training set, the second one as the evaluation subset and vice versa (100 experiments).

20, 40, 60, 80% of information (activity/structure data) were excluded from the training set.

Average accuracy of prediction (IAP) was calculated for each type of activity.

Evaluation of PASS Robustness

PASS Provides Robust Predictions Despite the Incompleteness of the Training Set

60

65

70

75

80

85

90

95

100

0 10 20 30 40 50 60 70 80 90 100 110 120

NN of 124 activity for compounds selected from MDDR

IAP

for t

he E

valu

atio

n Se

t, %

10080604020

PASS Professional Training Procedure: New Base & Add

PASS Professional Training Procedure: Training

PASS Professional Training Procedure: Selection

PASS Professional Training Procedure: Ready for Prediction


prediction.



4. PharmaExpert.



7. Web-Services.

8. Summary.

PharmaExpert: Tool for analysis of PASS prediction results

Search for “HIV reverse transcriptase inhibitors AND HIV integrase (strand transfer) inhibitors”

Search for “HIV reverse transcriptase inhibitors AND HIV integrase (strand transfer) inhibitors AND non-mutagenic”

Search for multitargeted compounds using PharmaExpert Antihypertensive agents, ACE and NEP inhibitors

Antiinflammatory agents, COX-1, COX-2, LOX inhibitors

J. Med. Chem., 2004, 47(11), 2870-2876

Bioorg. Med. Chem., 2004, 12(24), 6559-6568

Pharmaceut. Chem. J., 2011, 45 (10), 605-611

J. Med. Chem., 2003, 46(15),

3326-3332

J. Med. Chem. 2008, 51(6), 1601-1609

Curr. Pharm. Des. 2010, 16(15), 1703-1717 Med. Chem. Res. 2011, 20(9), 1509-1514

Cardiovascul. Therap. Prof., 2008, 7(5), 100-104

PASS & PharmaExpert

The search for new compounds

with specific therapeutic

effect(s) or/and interaction with specific target(s)

Drug repositioning

The search for new compounds

with multiple mechanisms of

action

Assessment of drug-drug

interactions and between natural

compounds - components of

medicinal plants


prediction.



4. PharmaExpert.



7. Web-Services.

8. Summary.

GUSAR: General Unrestricted Structure-Activity Relationships

Filimonov D.A. et al. SAR and QSAR Environ. Res., 2009, 20: 679.

-0.10 -0.05 0.00 0.05 0.10 0.15 0.20

2D Cerius2

3D Cerius2

CoMSIA

EVA

CoMFA

HQSAR

GFA

MLR

PLS

delta R2 test

delta Q2

delta R2

C1 O

2

O3

H4

H 5

0 1 1 1 0 1 0 0 0 1 C = 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0

1.40 -0.59 -0.57 -0.57 0.14 -0.59 1.27 0.14 0.14 -0.54

( )C21Exp − = -0.57 0.14 1.13 0.13 -0.02

-0.57 0.14 0.13 1.13 -0.02 0.14 -0.54 -0.02 -0.02 1.13

a) b) c)

EA IP A B P Q C 1.263 11.26 6.262 0.316 -0.00218 -0.1820 O 1.461 13.62 7.541 0.287 0.02944 0.3019 O 1.461 13.62 7.541 0.287 0.06199 0.5297 H 0.754 13.60 7.177 0.279 0.05812 0.4706 H 0.754 13.60 7.177 0.279 0.05304 0.3533

d)

Kokurkina G.V. et al. Eur. J. Med. Chem., 2012, 46 (9), 4374-4382.

QSAR Modelling of antifungal activities

Comparison of computational predictions with the experiment


prediction.



4. PharmaExpert.



7. Web-Services.

8. Summary.

Drug repositioning: new indications for known drugs

Prediction of nootropic effect for some antihypertensive drugs

0102030405060708090

Pa (n

ootr

opic

effe

ct)

PASS predictions Mice: behavioral reactions

in cross-maze Results

Perindopril in dose of 1 mg/kg, and quinapril and monopril in doses of 10 mg/kg improved the patrolling behavior in the maze. This effect is similar to the effects of standard nootropic drugs piracetam and meclofenoxate (in doses of 300 and 120 mg/kg, respectively). The observed nootropic effect of these ACE inhibitors is likely to be unrelated to their

antihypertensive effect, since the nootropic action took place only at relatively low doses of perindopril, quinapril and monopril and was not observed with further increase in dose.

Kryzhanovskii S.A. et al. Pharmaceutical Chemistry Journal, 2012, 45: 605-611.

Pharmacological targets for breast cancer therapy

Thomson Reuters Integrity

Participants: 9 teams from 8 countries

European project «From analysis of gene regulatory networks to drug» (Net2Drug)

ChemNavigator database (~24,000,000 structures of organic

compounds)

Virtual screening of potential multitarget anticancer substances (PASS, GUSAR)

11 compounds tested in cellular assays

2 active compounds (BC, melanoma)

Synergism with RITA.

Activity confirmed in experiments on mouse xenograft models

ALab – resident of «Skolkovo» (2012)

Grant of«Skolkovo» (2013)

Further progress:

In Silico Fragment-Based Drug Design Using PASS approach

In silico Fragment-Based Drug Design using PASS approach (COX-1/2 & LOX inhibitors)

Test Results:

8 – Active

7 – Inhibitors of one Enzyme

6 – Inhibitors of both Enzymes

Filz O.A. (Tarasova O.A.), Lagunin A.A., Filimonov D.A. (2012). In Silico Fragment-Based Drug Design Using PASS approach. SAR and QSAR in Environmental Research, 23 (3-4), 279-296. Eleftheriou P., Geronikaki A., Hadjipavlou-Litina D., Vicini P., Filz O., Filimonov D., Poroikov V., Chaudhaery S.S., Roy K.K., Saxena A. (2012). Fragment-based design, docking, synthesis, biological evaluation and structure–activity relationships of 2-benzo/benzisothiazolimino–5–aryliden–4-hiazolidinones as cycloxygenase/lipoxygenase inhibitors. Eur. J. Med. Chem., 47, 111-124.

COX-1 77

COX-2 95

LOX 93

COX-1 89

COX-2 100

LOX 69

COX-1 100

COX-2 96

LOX 100

COX-1 100

COX-2 100

LOX 84

COX-1 73

COX-2 100

LOX 13

COX-1 85

COX-2 87

LOX 97

COX-1 85

COX-2 94

LOX 79

ON

S S

N

O

N

F Cl

O

COX-1 65

COX-2 80

LOX 22

NIH/RFBR project “Virtual screening and biological testing of anti-HIV microbicides”

The purpose: Using virtual screening techniques for multiple targets simultaneously that are of relevance in the early stages of exposure to the virus after sexual intercourse, with the goal to identify commercially available compounds that show cell-protective/ microbicide activity in a subsequent cell-based assay.

Collaborators: Marc Nicklaus, Computer-Aided Drug Design (CADD) Group, Chemical Biology Laboratory, Center for Cancer Research, NCI/NIH. Stephen Hughes, Ph.D., Director, HIV Drug Resistance Program; Chief, Retroviral Replication Laboratory; and Head, Vector Design and Replication Section, Center for Cancer Research, NCI/NIH. Vladimir Poroikov, Department for Bioinformatics, Laboratory for Structure-Function Based Drug Design, Inst. Biomed. Chem. RAMS.

Virtual Screening Approaches Used in the Project Ligand-Based:

SAR (PASS)

QSAR (GUSAR)

Target-Based:

Docking (Glide) ChemNavigator iResearch Library

Market Select ( 5,755,574 str.)

50-100 compounds for testing in cell-based assays

Compound No.

EC50

-WT,

µM

From 45 compounds tested in cellular HIV infectivity assay,

16 (~35%) were active with EC50-WT = 0.3 – 16.2 µM

For Tenofovir, which is studied in phase III clinical trials, as microbicide, EC50-WT = 10 - 50 µM (in different cellular HIV infectivity assays).

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


prediction.



4. PharmaExpert.



7. Web-Services.

8. Summary.

I trust in PASS prediction!

I do not trust in PASS prediction!

Trust but verify! - I will check how good PASS prediction is for my compounds at: www.way2drug.com/passonline

Thanks to the courtesy of Prof. Sergey Vasilevsky

Total users: 8890 Total prediction: 305730 Total countries: 91

http://www.way2drug.com/passonline

France - 2741 Mexico - 5465 Australia - 7043

Other countries 24585

Italy - 8718

Ukrane - 10948

Kazakhstan - 11733

Poland - 13950

Chine - 14495

India - 59564 USA - 9552

Russia - 136935

Russia 136935 India 59564 China 14495 Poland 13950 Kazakhstan 11733 Ukraine 10948 USA 9552 Italy 8718 … …

Country Predictions

Comparison of web-services for biological activity prediction

Evaluation set (23 diverse compounds, 46 activities):

Web-services: ChemSpider – http://chemspider.com

CPI-DRAR – http://cpi.bio-x.cn/drar/ PASS Online – http://www.way2drug.com/passonline SuperPred – http://bioinformatics.charite.de/superpred/

SEA – http://sea.bkslab.org/

Criteria: Sensitivity = TP/Na; Computational time

Poroikov V. et al. 6th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, Maribor, Slovenia, September 3 – 7, 2011.

Sens

itivi

ty

Com

puta

tiona

l tim

e. h

our

Sensitivity and computational time of different web-services for biological activity prediction

ChemSpider SuperPred CPI-DRAR SEA PASS

Over fifty publications with independent confirmation of PASS online predictions

Ajay Kumar, Poonam Lohan, Deepak K. Aneja, Girish Kumar Gupta, Dhirender Kaushik, Om Prakash. (2012). Design, synthesis, computational and biological evaluation of some new hydrazino derivatives of DHA and pyranopyrazoles. European Journal of Medicinal Chemistry, 50: 81-89. Azza A. Kamel, Athina Geronikaki, Wafaa M. Abdou. (2012). Inhibitory effect of novel S,N-bisphosphonates on some carcinoma cell lines, osteoarthritis, and chronic inflammation. European Journal of Medicinal Chemistry, 51: 239-249. Chandrika B-Rao et al. (2012). Identification of novel isocytosine derivatives as xanthine oxidase inhibitors from a set of virtual screening hits. Bioorganic & Medicinal Chemistry, 20: 2930–2939. Donatella Verbanac, Subhash C. Jain, Nidhi Jain, Mahesh Chand, Hana Cipcic Paljetak, Mario Matijasic, Mihaela Peric, Visnja Stepanic, Luciano Saso. (2012). An efficient and convenient microwave-assisted chemical synthesis of (thio)xanthones with additional in vitro and in silico characterization. Bioorganic & Medicinal Chemistry, 20: 3180–3185. Wafaa M. Abdou, Azza A. Kamel, Rizk E. Khidre, Athina Geronikaki, and Maria T. Ekonomopoulou. (2012). Synthesis of 5- and 6-N-heterocyclic Methylenebisphosphonate Derivatives and Evaluation of their Cytogenetic Activity in Normal Human Lymphocyte Cultures. Chem. Biol. Drug. Des., 79: 719–730. Navarrete-Vazquez G., Hidalgo-Figueroa S., Torres-Piedra M., et al. (2010). Synthesis, vasorelaxant activity and antihypertensive effect of benzo[d]imidazole derivatives. Bioorganic & Medicinal Chemistry, 18 (11): 3985–3991. Mustafayeva K., Di Giorgio C., Elias R., et al. (2010). DNA-Damaging, mutagenic, and clastogenic activities of gentiopicroside isolated from Cephalaria kotschyi roots. Journal of Natural Products, 73 (2): 99–103. Raja A.K., Vimalanathan A.B., Raj S.V., et al. (2010). Indispensable chemical genomic approaches in novel systemic targeted drug discovery. Biology and Medicine, 2 (3): 26-37. Torres-Piedra M., Ortiz-Andrade R., Villalobos-Molina R., et al. (2010). A comparative study of flavonoid analogues on streptozotocinenicotinamide induced diabetic rats: Quercetin as a potential antidiabetic agent acting via 11b–Hydroxysteroid dehydrogenase type 1 inhibition. European Journal of Medicinal Chemistry, 45: 2606-2612. Benchabane Y., Di Giorgio C., Boyer G., et al. (2009). Photo-inducible cytotoxic and clastogenic activities of 3,6-di-substituted acridines obtained by acylation of proflavine. European Journal of Medicinal Chemistry, 44: 2459–2467. Hernandez-Nunez E., Tlahuext H., Moo-Puc R., et al. (2009). Synthesis and in vitro trichomonicidal, giardicidal and amebicidal activity of N-acetamide(sulfonamide)-2-methyl-4-nitro-1H-imidazoles. European Journal of Medicinal Chemistry, 44 (7), 2975-2984. Babaev E.V. (2009). Combinatorial chemistry at the university: ten years experience of research, educational and organizational projects. Russsian Chemical Journal, 53 (5), 140-152. Ghadimi S., Ebrahimi-Valmoozi A.A. (2009). Lipophilicity, electronic, steric and topological effects of some phosphoramidates on acethylcholinesterase inhibitory property. J. Iran. Chem. Soc., 6 (4): 838-848.

. . .

GUSAR-Based Web-Service: www.way2drug.com/gusar

Zakharov A., Lagunin A.A., Filimonov D.A., Poroikov V.V. Quantitative prediction of antitarget interaction profiles for chemical compounds. Chemical Research in Toxicology, 2012, 25 (11), 2378-2385. Lagunin A., Zakharov A., Filimonov D., Poroikov V. QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Molecular Informatics, 2011, 30(2-3), 241–250 .

www.way2drug.com

Computer-aided approaches is useful for finding of hits and their optimization to lead compounds.

PASS predictions allow to identify the most relevant biological screens for testing of particular chemical compounds.

PharmaExpert provides the means for selection of chemical compounds with desirable biological activity spectra (incl. multitargeted actions).

GUSAR can be used as an universal tool for solving various QSAR/QSPR problems.

Predictive web-services are freely available at web-site: http://www.way2drug.com

Contacts: [email protected] (Prof. Vladimir Poroikov)

Summary

Acknowledgements IBMC of RAMS, Russia: Dmitry Filimonov, Ph.D. Alexey Lagunin, Ph.D. Tatyana Gloriozova Anastasia Rudik, Ph.D. Olga Tarasova (Filz), Ph.D. Sergey Ivanov, Ph.D. Student Pavel Pogodin, Student

NCI/NIH, US: Marc Nicklaus, Ph.D. Alexey Zakharov, Ph.D. Laura Guasch, Ph.D. Stephen Hughes, Ph.D. Steven Smith, Ph.D.

Funding: RFBR (03-07-90282, 05-07-90123, 06-03-08077), CRDF (RC1-2064), INTAS (00-0711, 03-55-5218), ISTC (3197, 3777), FP6 (LSHB-CT-2007-037590), FP7 (200787), RFBR/CRDF grant # 12-04-91445-NIH_А/RUB1-31081-MO-12.

Institute of Pharmacology of RAMS, Russia: Tatyana Voronina, Dr. Sci. Ramiz Salimob, Dr. Sci. Sergey Kryzhanovsky, Dr. Sci.

Aristotelian University of Thessaloniki, Greece: Athina Geronikaki, PhD Karolinska Institute, Sweden: Galina Selivanova. Ph.D. GeneXplain GMbH, Germany: Alexander Kel, Ph.D.

20th EuroQSAR Understanding Chemical-Biological Interactions

PREDICTION OF ACTIVITY SPECTRA FOR SUBSTANCES: TWENTY ... · PREDICTION OF ACTIVITY SPECTRA FOR...

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