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Chemoinformaticswith artificial intelligence
Péter Antal
Computational Biomedicine (Combine) workgroupDepartment of Measurement and Information Systems,
Budapest University of Technology and Economics
Overview
• Chemoinformatics
• Artificial intelligence/machine learning
• The data flood in life sciences
• The data and knowledge fusion challenge
• The semantic unification in chemoinformatics
• Artificial intelligence in drug discovery
• Examples:
– Drug repositioning
– Drug-target interaction prediction
2
Chemoinformatics
• Gasteiger, Johann, and Thomas Engel, eds.
Chemoinformatics: a textbook. John Wiley & Sons, 2006.
• Bajorath, Jürgen. Chemoinformatics for Drug Discovery.
John Wiley & Sons, 2013.
• Karthikeyan, Muthukumarasamy, and Renu Vyas.
Practical Chemoinformatics. Springer, 2014.
• Brown, Nathan. In Silico Medicinal Chemistry:
Computational Methods to Support Drug Design. No.
8. Royal Society of Chemistry, 2015.
3
MK&RV:Practical chemoinformatics
4
1. Open-Source Tools, Techniques, and Data in Chemoinformatics
Semantic(-web) technologies
2. Chemoinformatics Approach for the Design & Screening of Focused Virtual Libraries
Prioritization methods using data and knowledge fusion
3. Machine Learning Methods in Chemoinformatics for Drug Discovery
Prediction methods using data and knowledge fusion
4. Docking and Pharmacophore Modelling for Virtual Screening
Priors from docking
5. Active Site-Directed Pose Prediction Programs for Efficient Filtering of Molecules
Binding sites, pockets and latent dimensions
6. Representation, Fingerprinting, and Modelling of Chemical Reactions
7. Predictive Methods for Organic Spectral Data Simulation
8. Chemical Text Mining for Lead Discovery
9. Integration of Automated Workflow in Chemoinformatics for Drug Discovery
Visual data analytics and workflow systems
10.Cloud Computing Infrastructure Development for Chemoinformatics
Karthikeyan, Muthukumarasamy, and Renu Vyas. Practical Chemoinformatics.
Springer, 2014.
Automating
drug discovery
Schneider, Gisbert. "Automating drug discovery." Nature Reviews Drug Discovery 17.2 (2018): 97.
Design cycle
Automated drug discovery facility
Active learning with microfluidics
Artificial intelligence
• IBM Grand Challenge
– 1997: Deep Blue wins human champion G.
Kasparov.
– 1999-2006<: Blue Gene, protein prediction
– 2011: Watson
• Natural language processing
• inference
• Game theory
IBM Watson (2011): Jeopardy
Clinical decision support systems
Watson for Oncology – assessment and advice cycle
www.avanteoconsulting.com/machine-learning-accelerates-cancer-research-discovery-innovation/
• Google DeepMind
• Monte Carlo tree
search
• 2016: 9 dan
• 2017: wins against
human champion
Go:
• 2017: Carnegie Mellon University MI:
Libratius
• Pittsburgh Supercomputing Center:
– 1.35 petaflops computation
– 274 Terabytes memory
Poker: Libratus
• Teaching + Learning: learning from manual
and from practice
Machines playing Civilization
Proportion of wins
Playing computer games
• YOLO (you only look once)
Vision: YOLO
https://www.ted.com/talks/joseph_redmon_how_a_computer_learns_to_recognize_obj
ects_instantly#t-409586
Emotion detection, sentiment analysis
https://www.ted.com/talks/rana_el_kaliouby_this_app_knows_how_you_feel_fro
m_the_look_on_your_face
Walking, movements
Real-time translation
D.Adams: Hitchhiker's Guide to the Galaxy"Pilot Translating Earpiece
• ~„big data failed, AI correctly predicted
the upset victory” (correct prediction of
election in the US 3 times in a row)
Political analytics: MogIA
Automated essay scoring (AES)
• Juridical decisions:
– Human experts: 66% identical decision.
– Katz, D.M., Bommarito II, M.J. and Blackman,
J., 2017. A general approach for predicting
the behavior of the Supreme Court of the
United States. PloS one, 12(4), p.e0174698.
• 1816-2015 esetek
• 70%< accuracy
– COMPAS CORE
Legal applications of AI
February 28, 2019 21
http://beauty.ai/• A beauty contest was judged by AI and the robots
didn't like dark skin, Guardian
• Another AI Robot Turned Racist, This Time At Beauty
Contest, Unilad
Beauty.AI
February 28, 2019 22
• Turing-test, Loebner-prize
• Tay was an artificial intelligence chatterbot released by
Microsoft Corporation on March 23, 2016. Tay caused
controversy on Twitter by releasing inflammatory tweets
and it was taken offline around 16 hours after its launch.[1]
Tay was accidentally reactivated on March 30, 2016, and
then quickly taken offline again.
Chatbot: Tay
• Gatys, L.A., Ecker,
A.S. and Bethge,
M., 2015. A neural
algorithm of artistic
style. arXiv
preprint
arXiv:1508.06576.
Reproduction of artistic style
Automated discovery systems Langley, P. (1978). Bacon: A general discovery system. Proceedings of the Second Biennial Conference of the Canadian Society for Computational Studies of Intelligence (pp. 173-180). Toronto, Ontario.
…
Chrisman, L., Langley, P., & Bay, S. (2003). Incorporating biological knowledge into evaluation of causal regulatory hypotheses. Proceedings of the Pacific Symposium on Biocomputing (pp. 128-139). Lihue, Hawaii.
(Gene prioritization…)
R.D.King et al.: The Automation of Science, Science, 2009
„Machine science”Swanson, Don R. "Fish oil, Raynaud's syndrome, and undiscovered public knowledge." Perspectives in biology and medicine 30.1 (1986): 7-18.
Smalheiser, Neil R., and Don R. Swanson. "Using ARROWSMITH: a computer-assisted approach to formulating and assessing scientific hypotheses." Computer methods and programs in biomedicine 57.3 (1998): 149-153.
D. R. Swanson et al.: An interactive system for finding complementary literatures: a stimulus to scientific discovery, Artificial Intelligence, 1997
James Evans and Andrey Rzhetsky: Machine science, Science, 2013
„Soon, computers could generate many useful hypotheses with little help from
humans.”
State of the art
26
• Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997
• Proved a mathematical conjecture (Robbins conjecture) unsolved for decades
• No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego)
• During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people
• NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft
• Proverb solves crossword puzzles better than most humans
• Google search
• Object recognition…
Hallmarks of a new AI era?
27
Factors behind the „A.I./learning
hype”• New theory?
– Unified theory of AI?
– A new machine learning approach?
• New hardware? (computing power..)
– Graphics cards (GPUs)?
– Quantum computers?
• New resources?
– Data?
– Knowledge?
– Money?
– Brains/Minds?
Milestones and phases in AI• ~1930: Zuse, Neumann, Turing..: „instruction is data”:
– Laws of nature can be represented, „executed”/simulated with modifications, learnt
– Knowledge analogously: representation, execution, adaptation and learning
• 1943 McCulloch & Pitts: Boolean circuit model of brain
• 1950 Turing's "Computing Machinery and Intelligence"
• 1956 Dartmouth meeting: the term "Artificial Intelligence”
• 1950s Early AI programs (e.g. Newell & Simon's Logic Theorist)
• The psysical symbol system hypothesis: search
• 1965 Robinson's complete algorithm for logical reasoning
• 1966—73 AI discovers computational complexityNeural network research almost disappears
• 1969—79 Early development of knowledge-based systems
• The knowledge system hypothesis: knowledge is power
• 1986-- Neural networks return to popularity
• 1988-- Probabilistic expert systems
• 1995-- Causality research
• The „big data” hypothesis: let data speak
• 1995-- Emergence of machine learning
• 2005/2015-- Emergence of autonomous adaptive decision systems („robots”, agents)
• The autonomy hypothesis??
Computational
complexity
Knowledge
representation
Exp
ert
syste
ms
Thresholds of
knowledge
Ma
ch
ine
lea
rnig
Statistical
complexity
Ad
ap
tive
de
cis
ion
syste
ms
Computer
Reminder:
Automating
drug discovery
Schneider, Gisbert. "Automating drug discovery." Nature Reviews Drug Discovery 17.2 (2018): 97.
Design cycle
Automated drug discovery facility
Active learning with microfluidics
The data flood in life sciences
Heterogeneous data in biomedicine
Genome(s)
Phenome (disease, side effect)
Transcriptome
Proteome
Metabolome
Environment&life style
Drugs
Moore’s Law for Data Explosion (Carlson’s law)
Sequencing
costs per mill.
base
Publicly
available
genetic data
NATURE, Vol 464, April 2010
• x10 every 2-3 years
• Data volumes and
complexity that IT has
never faced before…
Bioactivity databases I.
34
•Targets: 10,774
•Compound records: 1,715,667
•Distinct compounds: 1,463,270
•Activities: 13,520,737
•Publications: 59,610
ChEMBL is a database of bioactive drug-like small molecules, it contains 2-D
structures, calculated properties (e.g. logP, Molecular Weight, Lipinski
Parameters, etc.) and abstracted bioactivities (e.g. binding constants,
pharmacology and ADMET data).
https://www.ebi.ac.uk/chembl
Bioactivity databases II.
Compounds: 97,127,348
Substances: 252,300,917
BioAssays: 1,067,565
Tested Compounds: 3,417,415
Tested Substances: 5,591,261
RNAi BioAssays: 173
BioActivities: 239,680,570
Protein Targets: 12,159
Gene Targets: 58,18635
Bioactivity databases III:ExCAPE-DB
36
Sun, J., Jeliazkova, N., Chupakhin, V., Golib-Dzib, J.F., Engkvist, O.,
Carlsson, L., Wegner, J., Ceulemans, H., Georgiev, I., Jeliazkov, V. and
Kochev, N., 2017. ExCAPE-DB: an integrated large scale dataset
facilitating Big Data analysis in chemogenomics. Journal of
cheminformatics, 9(1), p.17.
Data: chemogenomics screening
• Justin Lamb: The Connectivity Map: a new tool for
biomedical research, Nature, 7,pp 54-60, 2007
Compounds Cell lines
Each cell is
transcriptional
proifle
Repositories for gene expression
• Gene Expression Omnibus (NCBI)
• http://www.ncbi.nlm.nih.gov/geo/
STRING - Protein-Protein Interactions
• http://string-db.org/
Number of genome-wide association studiesTota
l N
um
ber
of
Public
ations
Calendar Quarter
0
200
400
600
800
1000
1200
1400
2005 2006 2007 2008 2009 2010 2011 2012
1350
NHGRI GWA Catalog
www.genome.gov/GWAStudie
s
www.ebi.ac.uk/fgpt/gwas/
Published Genome-Wide Associations through 12/2012
Published GWA at p≤5X10-8 for 17 trait categories
Genetic overlap based disease maps
L.A.Barabási:PNAS, 2007, The human disease network
Epidemiologocal disease maps
Marx, P., Antal, P., Bolgar, B., Bagdy, G., Deakin, B. and Juhasz, G., 2017. Comorbidities in the diseasome are
more apparent than real: What Bayesian filtering reveals about the comorbidities of depression. PLoS
computational biology, 13(6), p.e1005487.
Number of biomedical publications
44
Little Science, Big Science, by
Derek J. de Solla Price, 1963
0
200000
400000
600000
800000
1000000
1200000
1950 1960 1970 1980 1990 2000 2010
Number of annual papers
Unification of biology: Gene Ontology
• Ontologies:
– Gene Ontology (GO): http://www.geneontology.org/
– Enzyme Classification (EC)
– Unified Medical Language Systems (UMLS)
– OBO
The Human Phenotype Ontology
http://human-phenotype-ontology.github.io/
Semantic publishing:
papers vs DBs/KBs
M. Gerstein, "E-publishing on the Web: Promises, pitfalls, and payoffs for bioinformatics," Bioinformatics, 1999
M. Gerstein: Blurring the boundaries between scientific 'papers' and biological databases, Nature, 2001
P. Bourne, "Will a biological database be different from a biological journal?," Plos Computational Biology, 2005
M. Gerstein et al: "Structured digital abstract makes text mining easy," Nature, 2007.
M. Seringhaus et al: "Publishing perishing? Towards tomorrow's information architecture," Bmc Bioinformatics,
2007.
M. Seringhaus: "Manually structured digital abstracts: A scaffold for automatic text mining," Febs Letters, 2008.
D. Shotton: "Semantic publishing: the coming revolution in scientific journal publishing," Learned Publishing, 2009
47
The fusion challenge
in drug discovery
Combination of
elements
geneg
en
e
target
co
mp
ou
nd
ge
ne
disease
binding site
co
mp
ou
nd
target protein
bin
din
g s
ite
product
ge
ne
gene
TF
BS
pathway
ge
ne
disease
pa
thw
ay
transcription factor
binding site
pro
du
ct
AT
C
GO
EC
HPO
E D. Green et al. Nature 470, 204-213 (2011) doi:10.1038/nature09764
Accomplishments of genomics research
Pharma productivity (~gap)
Mullard, A., 2017. 2016 FDA drug approvals. Nature Reviews Drug Discovery,
16(2), pp.73-76.
The fusion bottleneck
(~limits of personal cognition)
Watson?
The Science Behind an Answer
• http://www-03.ibm.com/innovation/us/watson/what-is-watson/science-behind-an-answer.html
Network of databases in 2000
54
• 10k< relevant biological
databases and knowledge-bases
• Petabytes of sequence and
high-throughput gene/protein
data
• ~10.000.000 concepts and
relations explicitly in
knowledge bases
Linked Open Data in 2017
55
Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard
Cyganiak. http://lod-cloud.net/
Approaches to fusion
• Encyclopedists:
– Wikipedia, Wikidata,
– Linked Open Data (LOD),
– Semantic unification
• Automated cross-domain querying
– Forms
– Workflow systems
– Natural language understanding, Machine reading
• Automated reasoning
– Watson
• Automated discovery systems („Automation of science”)
– Adam, Eve
• Large-scale similarity-based fusion applied in repositioning
56
Semantic unification in
chemoinformatics
Semantic Web
• Tim Berners-Lee, 1999, „I have a dream...”, W3C
• Web of data, Web 3.0
• Share, reuse, querying, integration of data, automatic processing, reasoning
• Publishing data in human readable HTML documents to machine readable documents
• Linked Data
The Internet network: nodes are computers or post-pc devices and links are wired or
wireless connections between them.
https://users.dimi.uniud.it/~massimo.franceschet/netart/talk/netart.html
The Resource Description Framework
(RDF)
• The data model of the Semantic Web
• RDF statement
– subject: resource identified by an IRI
– predicate (property): resource identified by an
IRI
– object: resource or literal (constant value)
• Graph databases of RDF triples
60
Relational databases vs.
Triplestores (graph databases)Relational databases• Relations are separated from data (cases)
• Tables&keys define the formal model (syntax)
for the data (cases)
• Model-based (~predefined)
• Meaning (semantics) is informal (out of scope
of the DB)
• Singular databases (~they are separated)
Triplestores• Unified representation of relations and data
• Triples („graph database”) stores the dynamic
model for the data, together with the factual
data
• Model-free (~relations as data)
• Meaning is defined by the (explicit) relations
(~ontology)
• Linked open data space (using universal
identifiers & ontologies)
61
Semantic technologies for drug
discovery
• Whitaker, B.J. and Rzepa, H.S., 1995. Chemical publishing via the
Internet. In International chemical information conference (pp. 62-71).
• Murray-Rust, P., Rzepa, H.S., Wright, M. and Zara, S., 2000. A
universal approach to web-based chemistry using XML and CML.
Chemical Communications, (16), pp.1471-1472.
• Murray-Rust, P. and Rzepa, H.S., 2002. Scientific publications in
XML-towards a global knowledge base. Data Science Journal, 1,
pp.84-98.
• Murray-Rust, P., 2008. Chemistry for everyone. Nature, 451(7179),
pp.648-651.
62
A problem with public data: parallel works on cleaning...integration
63
• Discovery Platform for cross-domain fusion.
• Public, curated, linked data.
– The data sources you already use, integrated and
linked together: compounds, targets, pathways,
diseases and tissues.
• Everything in triples: Subject-predicate-object
64
Open Pharmacological Space
Precursor: Gene Ontology: tool for the unification of biology, Nature, 2000
@gray_alasdair Big Data Integration 65
• Discovery Platform to cross barriers.
• The data sources you already use, integrated
and linked together: compounds, targets,
pathways, diseases and tissues.
• ChEBI, ChEMBL, ChemSpider, ConceptWiki,
DisGeNET, DrugBank, Gene Ontology,
neXtProt, UniProt and WikiPathways.
• For questions in drug discovery, answers from
publications in peer reviewed scientific journals.
66
OPS: scientific pharma questions
67
Top questions in the pharma
industry I. (Open PHACTS)
68
Top questions II.
69
Open PHACTS: databases
70
Dataset Downloaded Version Licence Triples
Bio Assay Ontology CC-By 10,360
CALOHA 8 Apr 2015 2014-01-22 CC-By-ND 14,552
ChEBI 4 Mar 2015 125 CC-By-SA 1,012,056
ChEMBL 18 Feb 2015 20.0 CC-By-SA 445,732,880
ConceptWiki 12 Dec 2013 CC-By-SA 4,331,760
DisGeNET 31 Mar 2015 2.1.0 ODbL 15,011,136
Disease Ontology 2015-05-21 CC-By 188,062
DrugBank 19 Feb 2015 4.1 Non-commercial 4,028,767
ENZYME 2015_11 CC-By-ND 61,467
FDA Adverse Events 9 Jul 2012 CC0 13,557,070
Total: ~3 Billion triples
Dataset Downloaded Version Licence Triples
Gene Ontology 4 Mar 2015 CC-By 1,366,494
Gene Ontology Annotations 17 Feb 2015 CC-By 879,448,347
NCATS OPDDR Nov 2015 Oct 2015 2,643
neXTProt (NP) 1 Feb 2014 1.0 CC-By-ND 215,006,108
OPS Chemical Registry 4 Nov 2014 CC-By-SA 241,986,722
HMDB 3.6 HMDB
MeSH 2015 MeSH
PDB Ligands 2 PDB
OPS Metadata CC-By-SA 2,053
UniProt 2015_11 CC-By-ND 1,131,186,434
WikiPathways 20151118 CC-By 11,781,627
Total: ~3 Billion triples
OPS: open tools for free
academic use
73
Open Targets I.
74
https://www.opentargets.org/
Khaladkar, M., Koscielny, G., Hasan, S., Agarwal, P., Dunham, I., Rajpal, D. and Sanseau, P., 2017.
Uncovering novel repositioning opportunities using the Open Targets platform. Drug discovery today.
Koscielny, G., An, P., Carvalho-Silva, D., Cham, J.A., Fumis, L., Gasparyan, R., Hasan, S., Karamanis, N.,
Maguire, M., Papa, E. and Pierleoni, A., 2016. Open Targets: a platform for therapeutic target identification
and validation. Nucleic acids research, 45(D1), pp.D985-D994.
Open Targets II.
75
Linked Data
for the Life Sciences
76
http://bio2rdf.org/
Databases:..
1. Belleau, F., Nolin, M.A., Tourigny,
N., Rigault, P. and Morissette, J.,
2008. Bio2RDF: towards a
mashup to build bioinformatics
knowledge systems. Journal of
biomedical informatics, 41(5),
pp.706-716.
2. Dumontier, M., Callahan, A., Cruz-
Toledo, J., Ansell, P., Emonet, V.,
Belleau, F. and Droit, A., 2014,
October. Bio2RDF release 3: a
larger connected network of
linked data for the life sciences.
In Proceedings of the 2014
International Conference on
Posters & Demonstrations Track-
Volume 1272 (pp. 401-404). CEUR-
WS. org.
Chem2Bio2RDF I.
77
Chem2Bio2RDF II.
78
Artificial intelligence
and
machine learning
in
drug discovery
Drug-target interaction prediction
80
Kövesdi, I., Dominguez‐Rodriguez, M.F., Ôrfi, L., Náray‐Szabó, G., Varró, A.,
Papp, J.G. and Mátyus, P., 1999. Application of neural networks in structure–
activity relationships. Medicinal research reviews, 19(3), pp.249-269.
Colwell, L.J., 2018. Statistical and machine learning approaches to predicting
protein–ligand interactions. Current opinion in structural biology, 49, pp.123-128.
Machine learning
in
chemoinformatics
81
Lo, Y.C., Rensi, S.E., Torng,
W. and Altman, R.B., 2018.
Machine learning in
chemoinformatics and drug
discovery. Drug discovery
today.
Deep learning
in
chemoinformatics
82
Chen, H., Engkvist, O.,
Wang, Y., Olivecrona, M. and
Blaschke, T., 2018. The rise
of deep learning in drug
discovery. Drug discovery
today, 23(6), pp.1241-1250.
Machine learning in chemoinformatics
83
Lo, Y.C., Rensi, S.E., Torng, W. and Altman, R.B., 2018. Machine learning in
chemoinformatics and drug discovery. Drug discovery today.
Results from machine learning
84
Zhang, L., Tan, J., Han, D. and Zhu, H., 2017. From machine learning to deep
learning: progress in machine intelligence for rational drug discovery. Drug
discovery today, 22(11), pp.1680-1685.
De novo molecular design I.
85
Olivecrona, M., Blaschke, T., Engkvist, O. and Chen, H., 2017. Molecular de-novo
design through deep reinforcement learning. Journal of cheminformatics, 9(1),
p.48.
De novo molecular design II.
86
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J. and Chen, H., 2018.
Application of generative autoencoder in de novo molecular design. Molecular
informatics, 37(1-2), p.1700123.
Autoencoder
Variational autoencoder
De novo molecular design III.
87
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J. and Chen, H., 2018.
Application of generative autoencoder in de novo molecular design. Molecular
informatics, 37(1-2), p.1700123.
Generative adversarial autoencoder neural network
Privacy-preserving data analysis
88
Hie, B., Cho, H. and Berger, B., 2018. Realizing private and practical
pharmacological collaboration. Science, 362(6412), pp.347-350.
Chemical syntheses
by deep artificial intelligence I.
89
Segler, M.H., Preuss, M. and Waller, M.P., 2018. Planning chemical syntheses
with deep neural networks and symbolic AI. Nature, 555(7698), p.604.
Chemical syntheses
by deep artificial intelligence II.
90Segler, M.H., Preuss, M. and Waller, M.P., 2018. Planning chemical syntheses
with deep neural networks and symbolic AI. Nature, 555(7698), p.604.
Data and knowledge fusion
in repositioning
Attrition in drug discovery
De novo drug discovery and development
10-17 years process and around 1B USD
~10% probability of success from Phase 1 to Market
Drug repositioning
3-12 years process and up to 80% cost reduction
Significantly higher probability of success from Phase 1 to
Market due to reduced safety and pharmacokinetic uncertainty
De novo discovery vs. repositioning
Scientific motivations for repositioning/rescue
L.A.Barabási:PNAS, 2007,
M.Campillos:Science, 2008Ingenuity Pathway Analysis
A disease-disease similarity network
A drug-drug network
A gene regulatory network
1, Multiple targets
2, Multifactorial diseases
4, Complex pathways (accumulating knowledge)
3, Personalized aspects:
3a, pharmaceutical/phenotypic: efficacy, side effects
3b, genetic/epigenetic
5, New measurements (accumulating omic data)
ENCODE:
tissue specific
regulation
6, Drugome (2000-7000, 1941) + failed drugs (~2000, +100 new yearly)
Scientific motivations for repositioning II.
• Magic bullet vs. Promiscuous/dirty drugs
• Monogenic vs multifactorial disease
• Selective optimisation of side activities (SOSA)
• Network pharmacy
• Personalized („precision”) drugs (for sub-
populations)
– Special external applicability conditions
– „Pathway” drugs
95
Repositioning publications
96
Ashburn TT, Thor KB: Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov
2004, 3(8):673-683.
Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P: Drug target identification using side-effect similarity. Science 2008,
321(5886):263-266.
Joachim von Eichborn Manuela S. Murgueitio, Mathias Dunkel, Soeren Koerner,Philip E. Bourne, Robert Preissner:
PROMISCUOUS: a database for network-based drug-repositioning, Nucleic Acids Research, 2010, 1–7
Michael Kuhn, Monica Campillos, Ivica Letunic, Lars Juhl Jensen, Peer Bork,*SIDER: A side effect resource to capture
phenotypic effects of drugs, Molecular Systems Biology 6:343, 2010
……..
0
20
40
60
80
2004200520062007200820092010201120122013
Repositioning: examples
97Li and Jones: Drug repositioning for personalized medicine, Genome Medicine 2012, 4:27
Information sources in repositioning and lead discovery.
Profile Repositioning HTS-based Dimension
Chemical X X 100-10000
Target protein X X n x 10000
Taxonomy X 3 (depth)
Side effect X 10000
Literature X 100000
Gene Expression X X k x 1000
Off-label use X 10000
Chemical fingerprints
• MACCS 2D, Molcon-Z, Dragon, 3D,..
• Schrödinger Canvas using Tanimoto distance
•Structurefingerprint
810 drugs
011001001011010101..
.
001010000001110100..
.
The drug landscape
100
Campillos, M., Kuhn, M., Gavin, A.C.,
Jensen, L.J. and Bork, P., 2008. Drug
target identification using side-effect
similarity. Science, 321(5886),
pp.263-266.
A drug network based on
likelihoods to share targets. All
drug pairs
predicted to share a target with at
least 25% probability were combined
to construct a
drug network. In contrast to Fig. 2 of
the main text, no side-effect similarity
P value
cut-off is used here. The large
network contains 628 nodes (drugs)
with at least 7 side
effects connected by 2881 edges
(drug pairs). Edge width is
proportional to the
probability of the drug pair to share a
target.
The target landscape
101http://www.cytoscape.org/what_is_cytoscape.html
Drug-target landscape
102Keiser, M.J., Setola, V., Irwin, J.J., Laggner, C., Abbas, A., Hufeisen, S.J., Jensen, N.H., Kuijer, M.B.,
Matos, R.C., Tran, T.B. and Whaley, R., 2009. Predicting new molecular targets for known drugs.
Nature, 462(7270), p.175.
Side-effect profiles• DailyMed textmining
– qualitative:SIDER adatbázis (http://sideeffects.embl.de)
– quantitative: exact prevalences
• E.g. Olanzapine
514 drugs
Taxonomies
• Anatomical Therapeutic Chemical
Classification System (ATC)
– 5 levels:
• Main anatomic,
• Main therapeutic
• therapeutic/pharmacological subgroup
• chemical/therapeutic/pharmacological subgroup
• Drugs.com
– http://www.drugs.com/
• RxNorm, Aetionomy 104
Chemical
Target
Pathway
“Disease”
Side effect
drugi
drugj
Combination of chemical and side effect
information for better target prediction
M.Campillos: Drug target identification using side-effect similarity, Science, 2008
Potential avenues of drug repositioning
106Li and Jones: Drug repositioning for personalized medicine, Genome Medicine 2012, 4:27
In silico/virtual screening using LOD
Chemical Side-effects Target prot. MMoA Pathways
Ta
nim
oto
Linked Open Data (LOD), e.g. Open PHACTS
Representation
Surrogate
Compound representations
Compound-compound similaritiesDavis,Shrobe,Szolovits, 1993
Similarity-based virtual screening
1, The “One-One-One” phaseHenrickson J, Johnson M, Maggiori G: Concepts and applications of molecular similarity. 1991, New York: John
Willey & Sons.
Willett P, Barnard J, Downs G: Chemical similarity searching. Journal of Chemical Information and Computer
Sciences 1998, 38(6):983-996.
2, The „data fusion” phase “One-Many-One”Ginn C, Willett P, Bradshaw J: Combination of molecular similarity measures using data fusion. Perspectives in
Drug Discovery and Design 2000, 20(1):1-16.
3, The „group fusion” phase “Many-Many-One”Whittle M, Gillet V, Willett P, Loesel J: Analysis of data fusion methods in virtual screening: Similarity and group
fusion. Journal of Chemical Information and Modeling 2006, 46(6):2206-2219.
Keiser M, Roth B, Armbruster B, Ernsberger P, Irwin J, Shoichet B: Relating protein pharmacology by ligand
chemistry. Nature Biotechnology 2007, 25(2):197-206.
Chen B, Mueller C, Willett P: Combination rules for group fusion in similarity-based virtual screening. Molecular
Informatics 2010, 29(6-7):533-541.
Gardiner E, Holliday J, O'Dowd C, Willett P: Effectiveness of 2D fingerprints for scaffold hopping. Future Medicinal
Chemistry 2011, 3(4):405-414.
Svensson F, Karlén A, Sköld C: Virtual screening data fusion using both structure- and ligand-based methods.
Journal of Chemical Information and Modeling 2011, 52(1):225-232.
B
A
S1S2S3S4 S5
B
A
S*
B
Q2
S S S
B
S*
Q3
Q1
Q2
Q3
Q1
B
S*
Q2
Q3
Q1
B
Q2
S1 S2S3S4 S5
Q1
S2S3S4 S5
Q3
S2S3S4 S5
Q2
Q1
Q3
S*
B
A
S
1, Similarity-based
approach
2, Data fusion
3, Group fusion
4, Query Driven Fusion Framework
Similarity-based fusion in drug repositioning
Chemical Side-effects Target prot. MMoA Pathways
Query-based optimal fusion
Ta
nim
oto
Query: set of corresponding drugs
QDF2
On the use of query analysis
• The information content of
– the query,
– the information resources,
– and the unknown observations(!)
• allow a one-class analysis of the query
(data description)
• and this induction is used in prioritization.
JOINTLY OPTIMIZED:1. weighting the members in the query (e.g. detection of outliers in the question),
GETTING THE RIGHT/IMPROVED QUESTION
2. weighting the similarity measures (e.g.information resources),
GETTING THE SCORING (SIMILARITY) FOR THE RIGHT/IMPROVED QUESTION
3. scoring/ranking the aggregate similarity of the unknown data points to the.
QDF2
The repositome (2013)
The „repositome” of FDA approved drugs (row) for the ATC level 4 classes (columns).
Arany, A., Bolgár, B., Balogh, B., Antal, P., & Mátyus, P. (2013). Multi-aspect
candidates for repositioning: data fusion methods using heterogeneous information
sources. Current medicinal chemistry, 20(1), 95-107.
Drug-target interaction
prediction
Drug-target interaction prediction I.
• Drug/compound information
– Fingerprints, pharmacophore properties, etc.
– Similarities
• Target information
– Protein vs. binding site/pocket
– Sequence/../complete structure
– Similarities
• Interaction data
– Indirect/direct
– Binary/rank/scalar
– IC50, Ki,..
– Complete/incomplete114
Drug-target interaction prediction II.
• Goal
– New drugs for a given target
– New targets for a given compound
– Multitask learning
• Targets for a novel drug
• Drugs for a novel target
• Interaction between novel drugs and targets.
• (Sequentiality)
115
A benchmark DTI task
116
Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug-
target interaction networks from the integration of chemical and genomic spaces.
Bioinformatics. 2008; 24(13):232–40. doi:10.1093/bioinformatics/btn162.
Multitask DTI prediction
• Approaches
– Network methods
– ….
– Pairwise conditional approaches or
pairwise kernel methods
– Matrix factorization methods
117
Fusion of drugs, targets and
interactions
118
Bolgár, Bence, and Péter Antal. "VB-MK-LMF: fusion of drugs, targets and
interactions using variational Bayesian multiple kernel logistic matrix
factorization." BMC Bioinformatics 18.1 (2017): 440.
VB-MK-LMF: performance
119
Bolgár, Bence, and Péter Antal. "VB-MK-LMF: fusion of drugs, targets and
interactions using variational Bayesian multiple kernel logistic matrix
factorization." BMC Bioinformatics 18.1 (2017): 440.
Latent dimensions
120
Bolgár, Bence, and Péter Antal. "VB-MK-LMF: fusion of drugs, targets and
interactions using variational Bayesian multiple kernel logistic matrix
factorization." BMC Bioinformatics 18.1 (2017): 440.
Effect of side information
121
Bolgár, Bence, and Péter Antal. "VB-MK-LMF: fusion of drugs, targets and
interactions using variational Bayesian multiple kernel logistic matrix
factorization." BMC Bioinformatics 18.1 (2017): 440.
Effect of prior
122
Bolgár, Bence, and Péter Antal. "VB-MK-LMF: fusion of drugs, targets and
interactions using variational Bayesian multiple kernel logistic matrix
factorization." BMC Bioinformatics 18.1 (2017): 440.
Unified pharmacogenomic space
123
Bolgár, Bence, and Péter Antal. "VB-MK-LMF: fusion of drugs, targets and
interactions using variational Bayesian multiple kernel logistic matrix
factorization." BMC Bioinformatics 18.1 (2017): 440.
Probabilistic prediction of interactions
124
Bolgár, Bence, and Péter Antal. "VB-MK-LMF: fusion of drugs, targets and
interactions using variational Bayesian multiple kernel logistic matrix
factorization." BMC Bioinformatics 18.1 (2017): 440.
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