solved and unsolved problems in chemoinformaticsapplications of chemoinformatics: drug design...

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Solved and Unsolved Problems in

Chemoinformatics

Johann Gasteiger Computer-Chemie-Centrum

University of Erlangen-Nürnberg

D-91052 Erlangen, Germany

[email protected]

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Overview

• objectives of lecture

• achievements

• challenges

• unsolved problems

• summary

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Objectives

• many problems have been solved

let us be proud!

•there is still a lot to be done

chemoinformatics is a field of its own,

is attractive for students

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• Structure activity relationships 1963 Hantsch & Fujita

• structure representation

1965, Morgan

• structure elucidation

1965, Sasaki, Munk, DENDRAL

• synthesis design

1970, Corey & Wipke, Ugi+Gasteiger, Hendrickson

• molecular modeling

1970, Langridge, Marshall

• data analysis / chemometrics

1970, Kowalski, Wold

Chemoinformatics – An Old Discipline

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• access to chemical information

• learning from chemical information

• applications

Achievements

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Databases

• Chemical Abstracts Service (1975)

• Cambridge Structure Database (1984)

• Beilstein (1990)

• Gmelin (1990)

• ChemInformRX (1991)

• SpecInfo (1991)

• PubChem (2004)

• etc.

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Number of Compounds in Chemistry

compounds published in CAS

0

10

20

30

40

50

1965 1970 1975 1980 1985 1990 1995 2000 2005

year

co

mp

ou

nd

s (

millio

ns

)

102 million

compounds,

66 million

sequences

(June 2015)

x

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Search for Cancerostatic Drugs

similar substrates protein/substrate complex

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Chemical Structures

• computers have learnt the language of a chemist

communicating by structure diagrams

•molecules are stored with atomic resolution providing

access to each atom and bond

enabling substructure search

•the complex structures of molecules can be visualized

new insights can be gained

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• databases have strongly contributed to the

progress in chemistry and related fields

• without databases modern research in

chemistry would be inconceivable

Summary

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• learning from data

• QSAR/QSPR

• representation of chemical structures

Learning from Chemical Information

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Problem: Not Enough Information

102,000,000 chemical compounds

1,000,000 3D structures in

Cambridge Crystallographic Data File

we only have data on the 3D structure for less than

1% of the known compounds

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Problem: Not Enough Information

102,000,000 chemical compounds

1,000,000 3D structures in

Cambridge Crystallographic Data File

we only have data on the 3D structure for less than

1% of the known compounds

can we learn the rules from the known 3D structures

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Rule-Based Learning: CORINA

*

connection table & stereo descriptors

initialization of

internal coordinates

rings:

ring perception

ring template search

ring assembly

3D coordinates

acyclic systems:

removal of steric crowding

generates 3D coordinates for >99.5 % of all organic compounds

J.Sadowski, J.Gasteiger, Chem.Reviews, 1993, 93, 2567-2581.

http://www.molecular-networks.com/products/corina/

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Data-Based Learning

Structure-Property Relationships (QSPR, QSAR)

molecular

structure property

structure

descriptors

//

representation model building

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Representation of Chemical Structures

• topological indices

• fragment codes

• fingerprints

• ….

Several thousands of different types of

chemical descriptors have been developed

R.Todeschini, V.Consonni, Molecular Descriptors in

Chemoinformatics, 2 volumes, Wiley-VCH, 2009

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Representation of Chemical Structures

3D model

molecular

surface

constitution

N H

N O

O

N H 3 +

Of Molecules and Humans

J. Gasteiger,

J. Med. Chem., 2006, 55, 6429 - 6434

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• physical, e.g.

– aqueous solubility

– 13C NMR shifts

• chemical, e.g.

– acidity

• biological, e.g.

– toxicity

Prediction of Properties

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Applications of Chemoinformatics: Drug Design

Chemoinformatics has become an integral part of the

drug design process

• lead discovery

• virtual screening

• pharmacophore searching

• lead optimization

• QSAR

• molecular docking

• prediction of ADME properties

• solubility, adsorption, distribution, metabolism, excretion….

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Handbook of

Chemoinformatics

J. Gasteiger (Editor)

65 authors

73 contributions

4 volumes

1900 pages

Wiley-VCH, Weinheim

(August 2003)

From Data to Knowledge

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Chemoinformatics

- A Textbook -

J. Gasteiger, T. Engel

(Editors)

650 pages

Wiley-VCH, Weinheim

(September 2003)

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• essense of chemistry

• environmental impact of chemicals

• understanding chemistry

• understanding biological systems

• human health

applications in all fields of chemistry and related

scientific disciplines

Challenges

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Essense of Chemistry

Chemical Industry is not selling

compounds

but is selling

properties

i.e., compounds with desired properties

(drugs, paints, fibers, plastics, pesticides, catalysts,

ceramics, etc.)

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What structure do I need for a certain property?

structure-activity relationships

How do I make this structure?

synthesis design

What is the product of my reaction?

reaction prediction

structure elucidation

In all those areas the use of chemoinformatics could help!

Fundamental Questions in Chemistry

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REACH – Registration, Evaluation, Authorization and

restriction of Chemicals

• for those chemicals used with more than 10 tons/year manufactured or

imported into the European Union a Chemical Safety Report is needed

• law since June 1, 2007; registration until Dec 1, 2013

• applies to about 35,000 chemicals

• testing on harmful effects on human health or environment, determination of

persistence, bioaccumulation and toxicity

• testing is time-consuming, expensive and might need many animals

Risk Assessment of Chemicals

Use chemoinformatics methods for ranking of chemicals

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• for chemicals used in cosmetics products

• no compounds tested on animals are allowed in cosmetics

in Europe since 2009.

• all animal tested cosmetics will eventually be banned on the

European market.

Cosmetics Directive

Use chemoinformatics methods for developing

alternatives to animal testing

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• access to chemical information

• learning from chemical information

• applications

Problems Still to be Solved

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• input of chemical structures (hand writing, voice)

• representation of neglected compounds (boranes, organo-

metallic structures (ferrocene, etc.), Markush structures (patents),

polymers)

• text mining (optical character recognition)

• publishing chemical information (3D structures, spectra)

• publishing and searching on the internet

Improved Access to Chemical Information

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• on compounds

• store all available information (all properties)

• store all spectra

• on reactions

• give the entire stoichiometry of a reaction

• store all reaction conditions (solvent, temperature,

reaction time)

• kinetic data

Neede for Better Databases

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From Experiment to Reaction Database (Presently)

experiment

lab journal report

manuscript

patent publication

secondary literature abstract

input into reaction database

reaction database

printed reaction scheme

information

producer

many breaks

in

information

processing

information

consumer

written on paper

electronically

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From Experiment to Reaction Database (Future: Electronic Lab Notebooks)

experiment

e lab notebook report

manuscript

patent publication

secondary literature abstract

input into reaction database

reaction database

printed reaction scheme

information

producer

a direct

information

flow

information

consumer

written on paper

electronically

X

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• Note: standard QSAR studies have difficulties to get

published if they do not also

- provide new experimental results or

- increase our knowledge and insight

• represent structures by descriptors that can be interpreted

• combine substructures with physicochemical effects

• use data analysis methods that are not a black box

• a forest of decision trees gives more exact predictions but cannot be

interpreted; a single decision tree can be interpreted!

• combine a QSAR model with e.g. docking studies

Learning from Chemical Information

From models to interpretation

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• all fields of chemistry

• drug design

• analytical chemistry

• organic chemistry

• biochemistry

• toxicology

• material science

Applications

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• conformational flexibility of drugs and proteins

• docking into proteins

• no consensus scoring (science cannot be predicted by voting)

• try to model the physicochemistry of the process

• protein-protein and protein-DNA interactions

• prediction of ADME-Tox properties

• model the various organs of a human

Drug Design

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Analytical Chemistry

• origin of olive oils

• simulation of infrared spectra

• 3D structure from infrared spectrum

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Italian Olive Oils

9 regions

572 olive oil samples

contents of 8 fatty acids:

palmitic acid linoleic acid

palmitoleic acid arachidic acid

stearic acid linoleic acid

oleic acid eicosenoic acid

1

2

3

4

5

6

7 8

9

M.Forina, et al. Ann.Chim.(Rome), 1982, 72, 127 and 144.

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Self-Organizing Map of Olive Oils

6 6 6 6 6 5 5 5 5 5 5 8 8 8 8 8 8 8

6 6 6 6 5 5 5 5 5 7 8 8 8 8

6 6 6 6 5 5 5 5 5 5 7 8 8 8 8 8 6 6 6 6 5 5 5 5 5 5 7 7 7 7 8 8 8 8

6 6 5 5 5 5 7 7 7 7 7 7 8 8

3 3 5 5 5 7 7 7 9 9 9 9 8

3 5 5 5 7 7 7 9 9 9 9

3 3 5 5 7 7 7 7 9 9 9 9 9 3 3 3 7 7 7 7 9 9 9 1

3 3 3 3 3 3 3 7 7 7 9 1 4 1

3 3 3 3 3 3 3 2 2 4 2 1 1 1 1

3 3 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1

3 3 3 3 3 3 3 3 3 2 2 1 1 1 3 3 3 3 3 3 2 2 2 2 2 4 4

3 3 3 3 3 3 3 3 3 3 3 2 2 2 2

3 3 3 3 3 3 3 3 3 3 2 2 2 4 4 4

3 3 3 3 3 3 3 3 3 3 3 3 4 2 2 4 4 4 4

3 3 3 3 3 3 3 3 3 4 2 2 2 2 3 3 3 3 3 3 3 3 4 4 3 4 4 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 2 2 2 4

572 samples

9 regions

8 fatty acids

250 training

322 test

10 prediction errors

J.Zupan, M.Novic, X.Li, J. Gasteiger, Anal. Chim. Acta, 1994, 292, 219-234

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Self-Organizing Map of Olive Oils

1

2

3

4

5

6

7 8

9

6 6 6 6 6 5 5 5 5 5 5 8 8 8 8 8 8 8

6 6 6 6 5 5 5 5 5 7 8 8 8 8

6 6 6 6 5 5 5 5 5 5 7 8 8 8 8 8 6 6 6 6 5 5 5 5 5 5 7 7 7 7 8 8 8 8

6 6 5 5 5 5 7 7 7 7 7 7 8 8

3 3 5 5 5 7 7 7 9 9 9 9 8

3 5 5 5 7 7 7 9 9 9 9

3 3 5 5 7 7 7 7 9 9 9 9 9 3 3 3 7 7 7 7 9 9 9 1

3 3 3 3 3 3 3 7 7 7 9 1 4 1

3 3 3 3 3 3 3 2 2 4 2 1 1 1 1

3 3 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1

3 3 3 3 3 3 3 3 3 2 2 1 1 1 3 3 3 3 3 3 2 2 2 2 2 4 4

3 3 3 3 3 3 3 3 3 3 3 2 2 2 2

3 3 3 3 3 3 3 3 3 3 2 2 2 4 4 4

3 3 3 3 3 3 3 3 3 3 3 3 4 2 2 4 4 4 4

3 3 3 3 3 3 3 3 3 4 2 2 2 2 3 3 3 3 3 3 3 3 4 4 3 4 4 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 2 2 2 4

J.Zupan, M.Novic, X.Li, J. Gasteiger, Anal. Chim. Acta, 1994, 292, 219-234

The merits of

unsupervised

learning

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• CASE was one of the roots of chemoinformatics

• various groups worked on it (Munk, Sasaki, Funatsu,

Steinrück)

• not much done recently

• no useful general purpose system available

• much time spent by chemists on structure elucidation

structure elucidation should be done more efficiently,

using all available information and using software

Computer-Assisted Structure Elucidation (CASE)

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Simulation of Infrared Spectra

N number of atoms

A atomic properties

B temperature or smoothing factor

rij interatomic distance

g r A A ei j

j i

N

i

NB r rij( )

( )

1

12

3D structure representation by radial distribution function

Predict the Infrared Spectrum from the structure of a compound

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Radialcode

(128 values)

IR spectrum

(128 absorbance values)

3D structure transformation

Training of a Counterpropagation (CPG) Network

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Simulation of IR Spectra

r = 0.965

0

0.4

0.8

500100015002000250030003500

cm-1

absorb

ance

simulation

experiment

Br

O O

J.Schuur, J.Gasteiger, Anal.Chim. 1997, 69, 2398-2405.

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Radialcode

(128 values)

IR spectrum

(128 absorbance values)

3D structure decoding

3D Structure Prediction from IR Spectrum

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IR Spectrum of Unknown Compound

0

0.1

0.2

0.3

0.4

500100015002000250030003500 cm-1

Abs.

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3D Structure Prediction from IR Spectra

NO

O

F

O

Correct prediction!

M.Hemmer, J.Gasteiger, Anal.Chim.Acta, 2000, 420, 145-154.

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Organic Chemistry

• chemical reactivity

• organic synthesis design

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• needs better data in reaction databases

• reach out to theoretical chemists

• put the results of quantum mechanical calculations

into databases

Chemical Reactivity

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Calculation of Chemical Effects

charge distribution

J. Gasteiger, M. Marsili, Tetrahedron 36, 3219 (1980)

inductive effect

J. Gasteiger, M. G. Hutchings, Tetrah. Lett. 24, 2541 (1983)

resonance effect

J. Gasteiger, H. Saller, Angew. Chem. Int. Ed. Engl. 24, 687 (1985)

polarizability effect

J. Gasteiger, M. G. Hutchings, J. Chem. Soc. Perkin 2, 559 (1984)

bond dissociation energy

J. Gasteiger, Comp. Chem. 2, 85 (1978)

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Calculation of Proton Affinities (PA)

• alkyl amines only (49 cpds)

PA(kJ/mol) = 205.6 + 2.82 ad,N

J. Gasteiger, M.G. Hutchings, J. Chem. Soc. Perkin 2, 1984, 559

• alkyl amines + heteroatom substituted alkyl amines (80 cpds)

PA(kJ/mol) = 1435.5 + 12.5 ad,N - 116.3 cr,N

J. Gasteiger, M.G. Hutchings, Tetrahedron Lett. 1983, 2541

N N H+ H

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A pKa Model of Aliphatic Carboxylic Acids

10.1989.111.002.127.1254.37 ,,2, aminoCOODOa IAQpK a ca

n = 1122, r2 = 0.81, s = 0.42, F = 809

A2D Q a

H 2 O + H 3 O +

H c

C

O

O

C C

O

O R1

R2

R3

C

R1

R2

R3

J.Zhang, T.Kleinoeder, J.Gasteiger, J.Chem.Inf.Model., 2006, 46, 2256-2266.

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• CASD was one of the roots of chemoinformatics

• many products can be traced back to CASD work

•MACCS, REACCS, Beilstein DB, ChemInform RX DB

• however CASD systems are not yet widely accepted

by organic chemists

• but it is still true:

“The amount of information to be processed and the decisions between

many alternatives suggests the use of computers in synthesis design.”

(H.Gelernter, 1973)

The design of organic syntheses should be done more

efficiently, using all available information, by using software

Computer-Assisted Organic Synthesis Design (CASD)

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• search for enzyme inhibitors M.Reitz, A.von Homeyer, J.Gasteiger,

J.Chem.Inf.Model., 2006, 46, 2324-2332

• search for similar enzymes O.Sacher, M.Reitz, J.Gasteiger,

J.Chem.Inf.Model., 2009, 49, 1525-1534

X.Hu, A,Yan, T.Tan, O.Sacher, J.Gasteiger

J.Chem.Inf.Model., 2010, 50, 1089-1100

• discover essential pathways of diseases G.Kastenmüller, J.Gasteiger, H.W.Mewes,

Bioinformatics, 2008, 24, i56-i62

G.Kastenmüller, M.E.Schenk, J.Gasteiger, H.W.Mewes,

Genome Biology, 2009,10, R28

Application of BioPath.Database

http://www.molecular-networks.com/databases/biopath

C3 © Gasteiger 56

Uncovering Metabolic Pathways to Phenotypic Traits

Attribute selection

+ Pathway ranking

PEDANT

Annotated

genomes

BioPath

database

Phenotypic

traits

Metabolic

reconstruction

Pathway profile

Relevant pathways

C3 © Gasteiger 57

Periodontal Disease

Human oral flora:

> 700 species

PEDANT:

15 fully sequenced oral genomes

(incl. 4 of 6 periodontal pathogens)

BioPath

68 global pathways

306 smaller pathways

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Relevant Pathways for Phenotype

Periodontal Disease Causing Glutamate fermentation

Biosynthesis of L-proline

Biosynthesis of 5-formimino-THF

Conversion of l-glutamate to L-proline

Conversion of l-glutamate to l-ornithine

Degradation of l-histidine to l-glutamate

G. Kastenmüller, M.E. Schenk, J. Gasteiger, H.W.Mewes,


C3 © Gasteiger 59

Relevant Pathways for Phenotype

Periodontal Disease Causing Glutamate fermentation

Biosynthesis of L-proline

Biosynthesis of 5-formimino-THF

Conversion of l-glutamate to L-proline

Conversion of l-glutamate to l-ornithine

Degradation of l-histidine to l-glutamate

Several of these pathways produce NH3

Cytotoxic NH3 plays a major role in periodontal disease

G. Kastenmüller, M.E. Schenk, J. Gasteiger, H.W.Mewes,


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• meeting the challenges posed by REACH, drug design

and Cosmetics Directive

• projects funded by the European Union, Innovative

Medicine Initiative and Cosmetics Europe

• eTOX (11 academic groups, 6 SMEs, 13 pharma companies)

• COSMOS (5 academic groups, 3 public institutions, 5 SMEs,

2 cosmetics companies)

Toxicity and Risk Assessment

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• Chemotypes

> structure fragments with physico-

chemical properties

• ToxPrint

> alerting chemotypes for genotoxic

carcinogens

• Publicly available

• www.chemotyper.org

• www.toxprint.org

Search Structures for Toxic Alerts

CSRML: C.Yang et al., J. Chem. Inf. Model., 2015, 55, 510-528

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QSPR Modeling of Diverse Material Properties

• Nanomaterials

• Catalysts

• Biomaterials

• Polymers

• Ionic liquids

• Supercritical carbon dioxide

• Ceramics

T.Le, V.C.Epa, F.R.Burden, D.A.Winkler, Chem.Reviews, 2012, 112, 2889-2919.

David A. Winkler, CSIRO, Melbourne, Australia

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• Chemoinformatics has become indispensable in

drug design

• has many potential applications in all areas of chemistry

and other scientific fields

• talk to colleagues about their problems

• build models with their data – and knowledge

• Society expects a lot from chemoinformatics

(Nobel Prize in Chemistry 2013: Karplus, Levitt, Warshel)

Conclusions

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Teaching

• several textbooks have been published

• Gillet+Leach, Gasteiger+Engel, Bajorath, Wild

• define curriculum in chemoinformatics

• various universities already teach chemoinformatics

• the number is growing

• integrate chemoinformatics into regular

chemistry curricula

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• Many excellent and dedicated coworkers at

Computer-Chemie-Centrum, University of Erlangen-Nuremberg,

Germany

http://www2.chemie.uni-erlangen.de

• Excellent coworkers at

http://www.molecular-networks.com

• Dr.Ulrike Gasteiger

Acknowledgements

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solved and unsolved problems in chemoinformaticsapplications of chemoinformatics: drug design...

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