Data for AI models, the past, the present, the future

Data for AI
Models, The Past,
The Present, The
Future
John P. Overington
jpo@md.catapult.org.uk

© 2019 Medicines Discovery Catapult. All rights reserved.
“Public data is the
worst form of
training data for AI
except for all those
other forms that
have been tried
from time to time”
Winston Churchill, 2016

National facility connecting the UK
community to accelerate innovative
drug discovery
• Independent not-for-profit organisation
• Part of the U.K.’s Catapult network
• Helping to deliver the U.K.’s Industrial Strategy
• Funded by Innovate U.K., part of UK Research
and Innovation, reporting to the Department
for Business, Energy & Industrial Strategy
• Focus on SME and translational academic
sector support
MDC - Medicines Discovery Catapult

ChEMBL, SureChEMBL & UniChem

• Originally developed 2003 at
Inpharmatica
• Spun out to public domain
• The world’s largest primary public
database of medicinal chemistry data
• ~2.3 million compounds
• ~11,000 targets
• ~15 million bioactivities
• Truly Open Data - CC-BY-SA license
• API, MyChEMBL VM, RDF, full tables
download….
• Basis of vast majority of AI innovation
in compound design/optimisation
Gaulton et al (2012) Nucleic Acids Research Database Issue. 40 D1100-1107
ChEMBL – www.ebi.ac.uk/chembl

Compound
Assay
Ki=4.5 nM
>Thrombin
MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSY
EEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRS
RYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEG
SSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGD
EEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEAD
CGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVL
TAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLK
KPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVC
KDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFY
THVFRLKKWIQKVIDQFGE
ED2=230 nM
Inhibition of
human Thrombin
PTT (partial
thromboplastin
time)
ChEMBL

• Public chemistry patent resource
• Donated by Digital Science –
SureChem commercial product
• Automatically extracted chemical
structures from full-text patents
• >18 million chemical structures
• Updated daily
• Full chemistry data download
SureChEMBL– www.surechembl.org
Papadatos et al (2016) Nucl. Acids Res Database Issue D1220-1228

UniChem – www.ebi.ac.uk/unichem
• Simple chemical
integration service
• >144 million structures
from ~30 sources
• URI/resource ID/Standard
InChI based lookups
• Available chemicals,
PubChem, ZINC, real
time, private
• Chemical structure ‘Time
Machine’
Chambers et al (2013) J. Cheminf. DOI:10.1186/1758-2946-5-3

Personal Perspectives on ChEMBL
• Things that worked well
• Single, major visionary funder – Wellcome Trust
• Focus on data content/backend not GUI
• Clear License – CC-BY-SA - same license as Wikipedia content
• Private/secure services
• Opportunism – SureChEMBL
• Open Data in ChEMBL re-invigorated cheminformatics research
• Things that didn’t work so well
• Community curation attempts – armchair critics
• Publisher interactions – except Royal Society of Chemistry
• I would do things very differently now

The Reproducibility Reproducibility Crisis!
Begley & Lee (2012) Nature DOI:10.1038/483531 & Prinz et al (2011) NRDD DOI:10.1038/nrd3439-c1

Enhanced data
model for ChEMBL
can appear as
‘errors’: e.g.
complexes,
receptor sets,
model organisms
“The more complex
the parameter, the
more frequent the
errors”
Errors in ChEMBL
Tiikkainen et al (2013) JCIM DOI:10.1021/ci400099q

Errors in SureChEMBL
Senger et al (2015) J Cheminf DOI:10.1186/s13321-015-0097-z

0.2
0.4
0.6
−4 −2 0 2 4
diff
density
Inter-species Assay Variability
Distribution of potency
differences
Scatter plot of
measured potencies
n = 2.781
Krüger & Overington (2012) PLoS Comp. Biol. DOI:10.1371/journal.pcbi.1002333
Same compound, same end-point for rat and human orthologs
pKi human
pKirat
diff(human, rat)
norm.dens.
2
4
6
8
10
12
2 4 6 8 10 12
orthoFrame$afnty1
orthoFrame$afnty2

2
4
6
8
10
12
2 4 6 8 10 12
sampleFrame$afnty1
sampleFrame$afnty2
0.2
0.4
0.6
−4 −2 0 2 4
diffdensity
pKi Assay1
pKiAssay2
diff(assay1, assay2)
n = 3.000
norm.dens.
Scatter plot of measured
potencies
Same compound, same species, different publication
Distribution of potency
differences
Inter-lab Assay Variability

density
Inter-species vs Inter-lab Variability
pKii - pKij
density Inter-laboratory
Inter-species

Garnett et al (2012) Nature DOI:10.1371/journal.pcbi.1002333 & Barretina et al (2012) Nature DOI:10.1038/nature11003
Large-Scale Cell-line Screening Data

Inconsistent Cell-line Screening Data
Haibe-Kains et al (2013) Nature DOI:10.1038/nature12831 (see also Stransky et al (2015) Nature DOI:10.1038/nature15736)

Primary Data – Batches and Replicates
http://www.wexlerwallace.com/wp-content/uploads/2012/04/Southeast-Laborers-Health-v-Pfizer.pdf

Incorrect Chemical Structures
Bosutinib Voxtalisib
http://cen.acs.org/articles/90/web/2012/05/Bosutinib-Buyer-Beware.html, & Overington & Wennerberg unpublished

Biochemical
assay
Cell-
based
screen
Functional
assay
Animal
disease
model
Human
clinical
trial
Variance – From Simple to Complex
Inter study variance
Number of assay variables
Steady state Time dependent

The Present

MDC Collaborating With The Sector

DeepADMET
• DeepADMET – InnovateUK grant
• Optibrium Ltd.
• Intellegens Ltd.
• Medicines Discovery Catapult
• MDC engineering software pipeline to
supply ‘SAR data on demand’
• Flexible wrt document source
• Fast and responsive
• Significantly boost public/internal data
• Deliver provenanced activity ‘vectors’
• Develop broader range of robust
ADMET models using deep learning
Document
gathering
NLP /
NER
Data
Extraction
&
Heuristics
SAR
vectors

Secondary
(compiled from literature review, databases)
Primary (preferred)
(measured in the same assay)
Assay conditions Assay conditions
Compound
Compound
*
DeepADMET – Data Structure

The Future

https://stevenmiller888.github.io/mind-how-to-build-a-neural-network/
Neural Networks

Assays in Drug Discovery
Biochemical
assays
Cell-based
assays
Functional
assays
In vivo
assays
Human
studies
Proteins Cell lines Tissues &
organs
Animal models Humans
ancient
“Human clinical trial”
• Error prone, serendipitous discoveries
• Traditional medicines: aspirin, quinine, …

Biochemical
assays
Cell-based
assays
Functional
assays
In vivo
assays
Human
studies
organs
1910s ancient
Animal in vivo assays
• Faster, safer, cheaper
• … but less predictive

Biochemical
assays
Cell-based
assays
Functional
assays
In vivo
assays
Human
studies
organs
1920s 1910s ancient
Ex vivo assays
• Higher throughput, cheaper
• Mechanistic insights

Biochemical
assays
Cell-based
assays
Functional
assays
In vivo
assays
Human
studies
organs
1950s 1920s 1910s ancient
Cell-based assays
• Higher throughput, cheaper

Biochemical
assays
Cell-based
assays
Functional
assays
In vivo
assays
Human
studies
organs
1970s 1950s 1920s 1910s ancient
Biochemical assays
• Higher throughput
• Recombinant DNA technology

Example Assay Path: Anti-inflammatory Drugs
Prostaglandin
G/H synthase 2
LPS-stimulated
THP-1 cells
LPS-stimulated
human whole blood
carrageenan-
injected rat
acute gout
patient

• Finding Assays
• Text-mining across papers, patents, vendor catalogues
• Indexing of Assays
• specialist dictionaries - techniques, equipment, genes, end-points, ….
• Classification of assays
• Efficacy/ADMET & biochemical, cell-based, organoid, tissue, ….
• Similarity of Assays
• how ‘similar’ are two assays?
• Chaining of Assays
• constructing the directed graph
• Learning thresholds
• Identification of ‘triggers’ from chained, directed assay pairs
AssayNet – Building the Network

Assay 1 Assay 2
• Decision Thresholds
• What activity threshold in Assay 1 makes it worth measuring in Assay 2?
• Learn from statistical distributions
• Probably artefactually thresholded at integral pIC50 thresholds – e.g. 1mM (cf P-value distributions)
Learning Decision Thresholds
pIC50
pIC50
#
#
Compounds
selected for
screening in
assay 2
Distribution of activity values of
compounds in Assay 1
Sharp cutoff
Sampled cutoff

Bayesian Networks

Bioassay data - ChEMBL Database
IC50 4.5 nM
>Thrombin
MAHVRGLQLPGCLALAALCSLVHSQHVFLA
PQQARSLLQRVRRANTFLEEVRKGNLEREC
VEETCSYEEAFEALESSTATDVFWAKYTAC
ETARTPRDKLAACLEGNCAEGLGTNYRGHV
APTT
11 min
Target
Compoun
d
Bioassay data
Compound
Assay
• Data manually extracted by a team of
curators from published pharmacology
and drug discovery literature (e.g.
Journal of Medicinal Chemistry)
• ChEMBL has transformed many aspects
of cheminformatics research
− Target prediction
− Large-scale QSAR
− Matched Molecular Pairs
− …
• ChEMBL is foundation data source of
almost all published AI compound
design research

1
a
b
d
2
3c
5e
4 g
f
h
6
ChEMBL as a Graph
assay-assay network
compound-compound network
b
f
c
h
ge
a d
1
a
1 a
compound assay
has activity in
Zwierzyna & Overington (in preparation)
1
2
4
6
5
3

Assay Network: Binding Assay Data (Subset)
A subset of the assay network (~6,000 nodes)
constructed using protein-binding assay data
from ChEMBL

Assay Network: Preclinical Assay Data
PPAR binding assay
DPP-4 binding assay
in vivo assay
cell-based assay
• Fragment of the assay network with a
subset of bioassays testing antidiabetic
compounds
• Assays involving closely related
biological targets are clustered
together, e.g. assays involving various
peroxisome proliferator-activated
receptors in the green cluster
• Antidiabetic compounds with different
mechanism of action (e.g. DPP-4
inhibitors and PPAR agonists) are often
tested in the same animal model (such
as Zucker diabetic rat) → in vivo
assays link distinct clusters

Animal Models: Assay Descriptions
CHEMBL893931:
“Inhibition of carrageenan-induced paw oedema
in Sprague-Dawley rat at 5.16 mg/kg, sc after 3 hrs.”

Animal Models: Assay Descriptions
Induced
Model Phenotype
Genetic
Strain
Dosage Administratio
n Route
Timing
CHEMBL893931:
“Inhibition of carrageenan-induced paw oedema
in Sprague-Dawley rat at 5.16 mg/kg, sc after 3 hrs.”

Information Extraction From Assay Descriptions
Antiallodynicactivity in Wistar albino rat chronicconstrictioninjury-induced neuropathic pain model assessed as attenuation of mechanicalallodynia
JJ NN IN NNP NN NN JJ NN JJ JJ NN NN VBN IN NN IN JJ NN
NP PP NP VP PP NP PP NP
S
CHEMBL1799193:
Antiallodynicactivity in Wistar albino rat chronic constriction injury-induced neuropathic pain model assessed as attenuation of mechanical allodynia.
Antiallodynicactivity Wistar albino rat chronicconstrictioninjury-induced neuropathic pain model assessed attenuation mechanicalallodynia
Experiment Phenotype PhenotypeStrain
A
B
C
D
Antiallodynicactivity in Wistar albino rat chronicconstriction injury-induced neuropathic pain model assessed as attenuation of mechanical allodynia
S
CHEMBL1799193:
Antiallodynicactivity Wistar albino rat chronicconstriction injury-induced neuropathic pain model assessed attenuation mechanical allodynia
Antiallodynicactivity Wistar albino rat chronicconstrictioninjury-induced neuropathic pain model assessed attenuation mechanical allodynia
A
B
C
D
Sentence
Noun Phrase
Verb Phrase
AdjectiveNoun Verb
Prepositional Phrase
Antiallodynicactivity in Wistar albino rat chronicconstrictioninjury-induced neuropathic pain model assessed as attenuation of mechanical allodynia
S
CHEMBL1799193:
[9.11,8.73,9.19,...] [-0.17,-0.57,0.01,...] [8.95,3.39,-5.22,...] [9.08,8.02,8.09,...][9.11,8.73,9.19,...][9.56,9.14,2.10,...][9.10,8.72,9.18,...]
Antiallodynicactivity Wistar albino rat chronicconstrictioninjury-induced neuropathic pain model assessed attenuation mechanical allodynia
A
B
C
D
E

PCA of Word2Vec Assay Descriptions
Each assay description: average over its word vectors. Data points projected from a 200-dimensional
space to 2D using PCA
Zwierzyna & Overington, unpublished

Word2vec Embedding of Assays
L01 (antineoplastic)M01 (anti-inflammatory)
ChEMBL assays of known drugs annotated with different ATC codes (~15k of ~94k)
N03 (antiepileptic)
A10 (antidiabetic)C02 (antihypertensive) N02 (analgesic)
Zwierzyna&Overington,unpublished

Biochemical
assay
Cell-based
screen
Functional
assay
Animal
disease
model
Human
clinical trial
Build assay networks
from literature/patent
co-occurrence
Link to animal models
and genetics
Understand target
engagement/
pharmacodynamics
through development
Directed graph of all
assays from targets to
clinical trials
AssayNet – Translational Path From Lab To Clinic
Compound

Acknowledgements
Bissan Al-Lazikani Aroon Hingorani,
Juan Pablo-Casas
Marc Marti-Renom
Francesco Martinez
Magda Zwierzyna
Mark Davies
Krister Wennerberg
Mark Warren, Gemma Holliday, Andrew Pannifer
Richard Seacome, James Welsh, Matthew Hodsgkiss
Charles Bury, Kepa Brurusco-Goni, Daiel James, Adam Poulston,
Matt Cockayne, Baydr Earls, Herve Barjat, Dave Allen, James Peach
Nathan Dedman, George Papadatos,
Grace Mugumbate, Anna Gaulton,
Prudence Mutowo, Louisa Bellis,
Anne Hersey, Jon Chambers,
Michal Nowotka, Anneli Karlsson,
Ines Smit, Francis Atkinson,
Paula Magarinos, Felix Kruger, Rita Santos

Data for AI models, the past, the present, the future

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Data for AI models, the past, the present, the future