Artificial Intelligence in Drug Discovery · The Computational Chemistry Field is enabling the new...
Transcript of Artificial Intelligence in Drug Discovery · The Computational Chemistry Field is enabling the new...
Chris de Graaf – Director, Head of Computational Chemistry
Integrating Artificial Intelligence Approaches to Enhance GPCR Drug Discovery
NON-CONFIDENTIAL
12 September 2019 | R&D Investor Day
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Chris de Graaf – Director, Head of Computational ChemistryAbout Me
DUTCH HERITAGE;UK-BASED
UNIVERSITY OF AMSTERDAM
MSc.
VRIJE UNIVERSITEIT AMSTERDAM
PhD.
POSTDOCTORAL RESEARCH FELLOW
3 YEARS
DIRECTOR, COMPUTATIONAL
CHEMISTRY2 YEARS
ASSOCIATE PROFESSOR
10 YEARS
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Introduction
Chief R&D Officer
Platform Technology Drug Discovery Preclinical
DevelopmentClinical Drug Development
Comp.Chemistry
Medicinal Chemistry
Molecular Pharmacology
Clinical DevelopmentProtein Engineering
Biomolecular Structure
Biophysics
Translational Sciences
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What is Computational Chemistry and why is it important for Drug Discovery?
By harnessing the power of computers and data, Computational Chemistry strives to make the drug discovery more efficient
Integrates chemical theory and modelling with experimental observations using algorithms, statistics and large databases
Simulates physical processes and uses statistics/data analyses to extract useful information from large bodies of data
Presents complex analyses in an understandable form to design experiments and new materials and validate results
Influences our understanding of the way the world works and characterizes new compounds and materials
Important in pharma industry to discover and design new therapeutics and apply cheminformatics to processes data
Sosei Heptares’ GPCR structures have changed the face of drug design
Sources: Langmead et al. J. Med. Chem. 2012, 1904; Congreve et al. J. Med. Chem. 2012, 1898
The Computational Chemistry Field is enabling the new era of GPCR Structure-Based Drug Design
• Detailed structural insights into GPCR binding sites enable atom by atom design and optimization of GPCR-drug interactions using computational chemistry approaches
• Efficient optimization of physiochemical properties of drug molecules improves their pharmacokinetics, efficacy, and safety
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Structure-based Virtual Screen
(VS)
• Poor physiochemical properties• Furan containing
• Novel non-furan containing• Moderate selectivity (vs A1)
Hit 1Preladenant
NH2
NN
S
N
OH
O
N
N
N
N
O
NN
NH2NN
O
NH2
N
N N
N
F
Cl
AZD4635
BPM & VSSBDD
Design vector
SBDDLipophilic
hotspots water networks• Novel triazene template• No structural alerts• Low selectivity (vs A1)• Mod. metabolic selectivity
NH2
N
N N
Hit 2
• Improved LLE• Improved selectivity• Improved metabolic stability
ARTIFICIAL INTELLIGENCECapability of machines to perform a task by
learning from data
Artificial Intelligence (AI) and Machine Learning (ML) in Drug Discovery
AI has the potential to supercharge computational chemistry and cheminformatics approaches for GPCR structure-based drug discovery using Sosei Heptares’ integrated GPCR databases
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MACHINE LEARNINGAI technique to automatically identify
relationships between input and output data
NEURAL NETWORK
Machine Learning algorithm consisting of different layers connecting input and output
data
Solving well-defined
problems using rich
training data
Drug discovery
Solving a dynamic,
multi-objective problem? Sparse chemical, biological,
structural dataKnowing how drugs
bind in 3D
Artificial Intelligence (AI) and Machine Learning (ML) in GPCR SBDD
Sosei Heptares proprietary GPCR databases give us a competitive advantage in the identification of novel patterns and relationships between GPCR biological, chemical and structural data
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PAR2Cheng et al., Nature (2017)
CCR9Oswald et al., Nature (2016)
GCGRJazayeri et al., Nature (2016)
CRF1Hollenstein et al.,
Nature (2013)
C5aRobertson et al.,
Nature (2018)
GPCR structures GPCR binding mode diversityA B
Extending the Chemical Space of the Structural GPCRome
Source: 1 Protein Data Bank. Includes 30 GPCR complexes from Sosei Heptares disclosed in the public domain
Sosei Heptares proprietary structures enable us to uniquely extend the bioactive chemical space for GPCRs and design new drugs and explore new modes of GPCR modulation
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A
GLP-1R GCGR
CT
PTH1R
CALCRCRF1
mGlu5mGlu1
FZD1
SMO
CCR5CCR2
CXCR4
C5a1BLT1
APJAT1 AT2
DP2
δOR
µORκORNOP
LPA6
P2Y1
FFA1P2Y12
PAR1PAR2
PAF
TA2R
EP4
EP3
D4D3
D2M1
M3M2M4
5HT1B
5HT2B
5HT2C
H1
5HT2A
β1β2
Rho
MT1
MT2
A1A2A
CB1
CB2
S1P1
LPA1ETB
Ox1
Ox2
NK1
NPYY1
Structural GPCRome: GPCR-ligand structures
211Structures GPCR-ligand complexes
publicly available1
230+Unreleased structures GPCR-ligand
complexes Sosei Heptares
60+GPCR StaRs
Chemical coverage of the Structural GPCRome
chemical similarity
790.383 GPCR-ligand complexes (public)
114.673 GPCR-ligand complexes (similar)
Extending the Binding Site View on GPCR LigandsB
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Sosei Heptares combines structural information – including key structures only known to us –with cutting edge computational methods to identify, compare and analyze GPCR binding sites for SBDD
Chemical coverage of the Structural GPCRomeStructural GPCR-Ligand Interaction Descriptors for Computer-Aided Drug Design
lipophilic hotspots
water networks
similarity
cryptic pockets
GPCRstructural
chemogenomics
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How can we realistically deploy AI at Sosei Heptares?
Drug Discovery Preclinical Development
Clinical Drug Development
Commercial,Life cycle
• Validated target• Assay developed• Lead molecule demonstrated
effect on target
Out
com
esAI
App
licat
ion
• Prediction of role in target in disease
• Identication novel hit molecule (Virtual Screening)
• Design compound libraries• Prediction of drug-target
interaction• Prediction of drug impact on
signalling pathways
• Lead candidate demonstrated effect in preclinical model
• Prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties
• Drug demonstrates:• Safety in humans• Efficacy in humans• Efficacy in large patient
population
• Drug repurposing• Selection of patient population• Review of patient data
(e.g. genomic data)
• Marketing authorization• Commercial manufacture• Post-marketing surveillance• Adverse effect
• Pharmacovigilance
Source: Adapted from Biopharma Excellence
AI approaches will focus on key areas in R&D that using unique Sosei Heptares GPCR data
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We are actively rolling out AI across our R&D platform
Platform Technology Drug Discovery Preclinical
DevelopmentClinical Drug Development
Medicinal Chemistry
Molecular Pharmacology
Clinical DevelopmentProtein Engineering
Biomolecular Structure
Biophysics
Computational Chemistry Translational Sciences
Artificial Intelligence for Multi-Parametric GPCR Drug Discovery
Machine Learning
Data & descriptors
AI StaR® Design1
Ligand Design2
ADMET Prediction3 Target / Indication
Selection4
Sosei Heptares AI Platform – StaR® Design
StaR® Design
sequence alignment analysis structural signatures
5-HT1B 2 2 2 2 2 2
5-HT2B 4 5 5 5 5
5-HT2C 1 1 1 1
β1 2 6 5 30 9 28 34 32 34
β2 2 3 1 1 17 4 14 18 18 18
D2 1 1 1 1 1 1 1 1
D3 2 2 1 2 2 2
D4 2 2 2 2 2 2 2 2 2 2 2
H1 2 2 2
M1 1
M2 1 1 1 1 3 1 3
M3 1 9
M4 2
AT1 1 2 1 2 2 2 2 2 1 2 2
AT2 5 2 3 5 5 5 3 5 2 5 5
C5a1
ETB 1 2 1 1 1 3 1 2 1 1 3 3 3 3
NTS1 10 8 8
δ 2 4 6 6 6
κ 2 2 2 2 2 2 2 2 2 2
µ 1 1 1 1 2 2 2
NOP 6 4 6 2 6 1 5 4 6 6 6
OX1 2 2 2 2 2 1 2 2 2
OX2 1 1 1 1 1 1 1
PAR1PAR2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
CCR2 1 1 1 1 1 1 1 1 1 1 1
CCR5 1 1 2 1 2 2 4 1 4 2 1 1 3 2
CXCR4 1 10 6 10 10 10 7 10 9 10 9
US28 2 1 2 2 2 2 2 1 2 2 2 2
BLT1 1 1 1 1 1 1 1 1 1 1
CB1 2 2 2 4 3 2 4 2 2 1 4 4
FFA1 3 3 3
LPA1 2 3 3 3 1 2 2 3 3 3
S1P1 2 1 1 2 1 2 2 2 2
A1 2 2 3
A2A 2 5 1 5 6 2 1 11
P2Y1 1 1 1 1
P2Y12 2 2 2 2 2 3
TM33.30 3.32
Chem
okine
Lipid
Nucle
otide
3.26 3.27 3.28 3.2923.50 3.21 3.25IFP
Amine
rgic
Pepti
de
2.59 2.60 2.61 2.63ECL1
Receptor 1.27 1.30 1.31 1.32 1.34 2.64 2.651.35 1.39 2.53 2.54 2.56 2.57TM1 TM2
structural interaction fingerprints
Mining unique proprietary information on structural determinants of GPCR thermostabilisation to accelerate GPCR structure determination for SBDD
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GPCR structure
Biomolecular StructureProtein
Engineering
Bioinformatics StaR data
Machine Learning
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Sosei Heptares AI Platform – Ligand Design
Ligand Design
Interaction Fingerprint (IFP) based virtual GPCR ligand screening
Identifying GPCR specific structural interaction features that enable efficient Virtual Screening for novel hit molecules
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Computational Chemistry
Cheminformatics
Medicinal Chemistry
GPCR SBDD
Machine Learning
5-HT1B 2 2 2 2 2 2
5-HT2B 4 5 5 5 5
5-HT2C 1 1 1 1
β1 2 6 5 30 9 28 34 32 34
β2 2 3 1 1 17 4 14 18 18 18
D2 1 1 1 1 1 1 1 1
D3 2 2 1 2 2 2
D4 2 2 2 2 2 2 2 2 2 2 2
H1 2 2 2
M1 1
M2 1 1 1 1 3 1 3
M3 1 9
M4 2
AT1 1 2 1 2 2 2 2 2 1 2 2
AT2 5 2 3 5 5 5 3 5 2 5 5
C5a1
ETB 1 2 1 1 1 3 1 2 1 1 3 3 3 3
NTS1 10 8 8
δ 2 4 6 6 6
κ 2 2 2 2 2 2 2 2 2 2
µ 1 1 1 1 2 2 2
NOP 6 4 6 2 6 1 5 4 6 6 6
OX1 2 2 2 2 2 1 2 2 2
OX2 1 1 1 1 1 1 1
PAR1PAR2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
CCR2 1 1 1 1 1 1 1 1 1 1 1
CCR5 1 1 2 1 2 2 4 1 4 2 1 1 3 2
CXCR4 1 10 6 10 10 10 7 10 9 10 9
US28 2 1 2 2 2 2 2 1 2 2 2 2
BLT1 1 1 1 1 1 1 1 1 1 1
CB1 2 2 2 4 3 2 4 2 2 1 4 4
FFA1 3 3 3
LPA1 2 3 3 3 1 2 2 3 3 3
S1P1 2 1 1 2 1 2 2 2 2
A1 2 2 3
A2A 2 5 1 5 6 2 1 11
P2Y1 1 1 1 1
P2Y12 2 2 2 2 2 3
TM33.30 3.32
Ch
em
okin
eL
ipid
Nu
cle
oti
de
3.26 3.27 3.28 3.2923.50 3.21 3.25IFP
Am
ine
rg
icP
ep
tid
e
2.59 2.60 2.61 2.63ECL1
Receptor 1.27 1.30 1.31 1.32 1.34 2.64 2.651.35 1.39 2.53 2.54 2.56 2.57TM1 TM2
IFPs
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Sosei Heptares AI Platform – Ligand Design
Ligand Design AI molecule generation models and scoring approaches using proprietary structural descriptors and customized to GPCR targets
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Computational Chemistry
Cheminformatics
Medicinal Chemistry
GPCR SBDD
Reinforcement Learning
Artificial Intelligence driven molecule generation
Recurrent Neural Networkscoring
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Sosei Heptares AI Platform – Ligand Design
Ligand Design
3D alignment/docking
2
Computational Chemistry
Cheminformatics
Medicinal Chemistry
GPCR SBDD
iteration GPCR
-liga
nd d
ocki
ng sc
ore
3D li
gand
sim
ilarit
y sc
ore
GPCR-ligand docking score3D li
gand
sim
ilarit
y sc
ore
iteration
Multiparameter scoring using proprietary structural descriptors
Artificial intelligence driven molecule generation
AI molecule generation models and scoring approaches using proprietary structural descriptors and customized to GPCR targets
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Sosei Heptares AI Platform – Ligand Design2
Representative ligand LHS and variable RHS
FEP based ligand binding mode prediction
The computational prediction of ligand binding affinity has been a treasured goal for many years: FEP has been customised for GPCR SBDD through a collaborative arrangement with the leaders in the field
Free Energy Pertubation (FEP) In-house GPCR structures FEP vs. experimentally determined ligand affinity
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Sosei Heptares AI Platform – ADMET Prediction
Ligand Design Combining external/in-house data sets to train ADMET prediction models to guide the design of GPCR ligands with improved drug properties
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Computational Chemistry
Cheminformatics
Medicinal Chemistry
GPCR SBDD
ADMET Prediction
Hit 2A2A pKi 6.9
Mod solubilityLow selectivity (vs A1)
Mod.metabolic stability
HTL1071/AZD4635A2A pKi 8.0
Improved solubilityImproved selectivityImproved metabolic
stability
SBDD
A1A2A
NH2
N
N N
N
F
ClNH2
N
N N
Solubility prediction model
Hepatic Clearance prediction model
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Sosei Heptares AI Platform – Target / Indication Selection4
Target / Indication Selection
Biology/GenomicsPharmacology
TranslationDevelopment
GPCR structureBioinformatics Identification Disease Associated GPCR Variants for SBDD
Genome/phenome data mining to identify
GPCR variants associated with
specific disease areas
PHEnomeRare Diseases
VariantsUK Biobank 100K genomes gnomADCOSMICClinVar
GPCRomeSelection StAR design
AgonistAntagonist
CHEMomeDesign
In Vitro ScreenCombinationsMachine Learning
Structural cheminformatics
analysis GPCR variants
Comparative pharmacological
evaluation identified GPCR variants
Generation StaR of disease associated GPCR variant for SBDD
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Uniquely positioned to leverage next generation AI / ML methods
FASTERStaR® generation
1
BETTERGPCR ligand design
2
PREDICTEDADMET properties
3
SMARTERGPCR target /
indication selection
4
Artificial Intelligence (AI) and Machine Learning (ML) can be gamechangers for drug discovery if used with the correct understandingof how drug ligands bind
Sosei Heptares has the technology to gather unique insights in thecomplex biological, structural and chemical space of GPCRs andtheir ligands
Sosei Heptares’ proprietary engine delivers innovative new GPCRstructures, enabling the generation of GPCR SBDD data andtechnologies that uniquely enable AI / ML
Sosei Heptares’ proprietary GPCR databases, in-house knowledge,and collaborative initiatives enable AI / ML driven data mining thatgive us a competitive advantage in GPCR drug discovery
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