SlideShare a Scribd company logo
Bowes J., et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling.
Nature Reviews Drug Discovery 2012;11:909–22.
Number in training set 4585 3106 2457
Median difference with/without feature (DpIC50) 0.35 -0.1 0
Cohan’s d 0.4 -0.26 0.02
Explainable AI
MedChemica
Virtual Toxicity Panel Screens to aid the Medicinal Chemist
A. G. Dossetter•, E. Griffen•, A. Leach•+, A. Lin‡, J. Stacey†, L. Reid§, S. Montague•.
•Medchemica Ltd, Macclesfield, UK, + Pharmacy and Biomolecular Sciences, Liverpool John Moores University, †Information School, University of Sheffield,
‡Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg,
§Bioinformatics Institute (A*STAR), 30 Biopolis Street, Matrix, Singapore 138671
Problem
Unforeseen toxicity via secondary pharmacology is a significant risk and when encountered late in a
discovery project’s life creates major issues and may even terminate it.
Chemists need to be alerted to potential risks but to be influenced they must be able to audit the reasons
and evidence for the alerts.
Solution
Build transparent models of critical toxicity targets
and communicate results in chemical structures
rather than just numbers. This is an example of
‘Explainable AI’ for chemists
contact@medchemica.com
Learning
• Models must be transparent and show structures to influence chemists
• Random Forest models with the correct descriptors can be used to show important features as pharmacophores and the evidence supporting them
• Error models can given a measure of confidence to predictions beyond use of an RMSE.
Chemists won’t make decisions without understanding
Language of medicinal chemists = structures / clear pharmacophores
Machine Learning method Description
MMPA transformations Example pairs
kNN + Morgan fp Structures of Nearest Neighbours
Random Forest + pharmacophore fp Compound highlighted with
important features
Graph analytics Connections between
compound families
Graph Convolutional Neural
Network (GCNN)
Graph node feature importance
– a work in progress
Aspects of Models
Pay attention to Feature Engineering
Clear definitions enables identifying key features
Transparency
Scientific
Sense
Consistency
Parsimony
Applicability
Performance
Modeler’s
domain
Chemist’s
domain
Interpretable
Failure cost high
Immature science
Highly skilled, critical users
Business-2-Business
Transparent and auditable
Black Box
Failure cost is low
Real time response critical
Interactive = self correcting
Business-2-consumer
User agnostic of process
Trying to explain black box models, rather than creating models that are
interpretable in the first place, is likely to perpetuate bad practice and can
potentially cause great harm to society. The way forward is to design
models that are inherently interpretable.
- Cynthia Rudin Nature Machine Intelligence (2019), 206–215.
Approach Application
Advanced Pharmacophore Features
Feature Definition
Basic Group Atom or group most likely protonated at pH 7.4
Acidic Group Atom or group most likely deprotonated at pH 7.4,
includes N and C acids
Acceptor Definitions derived from Taylor & Cosgrove
Donor Definitions derived from Taylor & Cosgrove
Hydrophobic C4 or greater cyclic or acyclic alkyl group
Aromatic Attachment connection of any group to an aromatic atom excluding
connections within rings
Aliphatic Attachment connection of any atom to an aliphatic group not in a ring.
Halo F,Cl, Br, I
Gobbi, A.; Poppinger, D. Biotechnology and Bioengineering 1998, 61 (1), 47–54.
Reutlinger, M.; Koch, C. P.; Reker, D.; Todoroff, N.; Schneider, P.; Rodrigues, T.; Schneider, G. Mol. Inf. 2013, 32 (2), 133–138.
Taylor, R.; Cole, J. C.; Cosgrove, D. A.; Gardiner, E. J.; Gillet, V. J.; Korb, O. J Comput Aided Mol Des 2012, 26 (4), 451–472.
Acid & Base definitions are SMARTS including C, N, heteroaromatic acids, bases excluding weak aniline bases, including amidines, guanidine’s - MedChemica
definitions.
Simple
H bond
acceptor
base
acid
Precise
Diclofenac
(1973)
Sulfadiazine
(1941)
Pharmacophore Pairs
• Feature 1 – topological distance - Feature 2
• Engineered for chemical relevance – pairs can
be superimposed or directly linked, e.g.
enables a group to be both a hydrogen bond
acceptor and a base
• Used as unfolded 280 bit fingerprints
• A bit identifies a pharmacophore pair e.g. :
Aromatic - 3 bonds - Base
• Random Forest feature importance and Cohan’s d for effect size allow identification of critical features in models
• Highlight atoms by S Feature Importance coloured by direction of Cohan’s d
• Show statistics on the effect and variance of each feature
• Drill back to precise features and original compounds with data supporting that feature – complete transparency
Cardiac toxicity and Seizure are key toxicological risks
Cardiac
hERG ion channel inhibitor
NaV 1.5 channel inhibitor
Ca L type channel inhibitor
Ca T-type channel inhibitor
PDE 3A inhibitor
Seizure
Dopamine D1 receptor ant/ag
Dopamine D2 receptor ant/ag
Cannabinoid CB1 receptor ant/ag
Acetylcholine a1b2 receptor
agonist / antagonists
µ opioid agonist / antagonists
k opioid agonist
d opioid agonist/ antagonists
Muscarinic M1 receptor ant/ag
Muscarinic M2 receptor ant/ag
Seizure
5HT 1A receptor antagonists
5HT 1B receptor antagonists
5HT receptor antagonists 2A
GABA a1 antagonist
NMDA-NR1 agonist
5HT Transporter inhibitor
Dopamine Transporter inhib
Noradrenaline Transporter inh
Acetylcholine esterase
inhibitor
Monoamine oxidase inhibitor
PDE 4D inhibitor
Model ‘quality’, Error models and Domain of applicability
• Build models with 10 fold CV – report CV-Pearson’s R2 and CV RMSE
• Build a Random Forest error model to generate predicted error for each compound
• Error model can be used to flag compounds out of Domain of Applicability
hERG n=5968, RMSE = 0.16, CV Pearson’s R2 = 0.27
CHEMBL12713 sertindole,
prediction pIC50 7.8 [7.1 – 8.4], actual 8.2
.
Predictions and Transparency
Medicinal Chemistry
Seizure Models – RF and kNN
Dopamine
Transporter
Norepinephrine
Transporter
5HT1a
receptor
GABA-A
receptor
d Opioid
receptor
MAO-A
inhibitor
AChE
inhibitor
Training set
size
1712 1757 400 1526 1070 1684 3283
CV-R2 0.28 0.23 0.37 0.24 0.28 0.21 0.32
RMSE 0.13 0.18 0.21 0.29 0.18 0.29 0.16
Best Random Forest based models for seizure endpoints,
All the seizure data sets delivered kNN models based on Morgan fingerprints
hERG Example

More Related Content

What's hot

Molecular docking
Molecular dockingMolecular docking
Molecular docking
Maakasaikumar
 
Fbdd
FbddFbdd
Gordon2003
Gordon2003Gordon2003
Gordon2003
toluene
 
molecular docking
molecular dockingmolecular docking
molecular docking
j janiba jeslin
 
Docking
DockingDocking
Chemoinformatic
Chemoinformatic Chemoinformatic
Chemoinformatic
Zarrin Es'haghi
 
Docking
DockingDocking
Docking
Monika Verma
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
Shrihith.A Ananthram
 
Molecular docking and_virtual_screening
Molecular docking and_virtual_screeningMolecular docking and_virtual_screening
Molecular docking and_virtual_screening
Florent Barbault
 
molecular docking
molecular dockingmolecular docking
molecular docking
KOUSHIK DEB
 
Structure based drug design- kiranmayi
Structure based drug design- kiranmayiStructure based drug design- kiranmayi
Structure based drug design- kiranmayi
KiranmayiKnv
 
Structure Based Drug Design
Structure Based Drug DesignStructure Based Drug Design
Structure Based Drug Design
dikheidi
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
Rahul B S
 
Fragment based drug design
Fragment based drug designFragment based drug design
Fragment based drug design
Ekta Tembhare
 
Structure based computer aided drug design
Structure based computer aided drug designStructure based computer aided drug design
Structure based computer aided drug design
Thanh Truong
 
Basics Of Molecular Docking
Basics Of Molecular DockingBasics Of Molecular Docking
Basics Of Molecular Docking
Satarupa Deb
 
Data analysis workflows part 1 2015
Data analysis workflows part 1 2015Data analysis workflows part 1 2015
Data analysis workflows part 1 2015
Dmitry Grapov
 
Molecular Docking
 Molecular Docking Molecular Docking
Molecular Docking
Malharraiyani
 
Computer aided-drug-design-boc sciences
Computer aided-drug-design-boc sciencesComputer aided-drug-design-boc sciences
Computer aided-drug-design-boc sciences
BOC-Sciences
 
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO
csandit
 

What's hot (20)

Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
Fbdd
FbddFbdd
Fbdd
 
Gordon2003
Gordon2003Gordon2003
Gordon2003
 
molecular docking
molecular dockingmolecular docking
molecular docking
 
Docking
DockingDocking
Docking
 
Chemoinformatic
Chemoinformatic Chemoinformatic
Chemoinformatic
 
Docking
DockingDocking
Docking
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
Molecular docking and_virtual_screening
Molecular docking and_virtual_screeningMolecular docking and_virtual_screening
Molecular docking and_virtual_screening
 
molecular docking
molecular dockingmolecular docking
molecular docking
 
Structure based drug design- kiranmayi
Structure based drug design- kiranmayiStructure based drug design- kiranmayi
Structure based drug design- kiranmayi
 
Structure Based Drug Design
Structure Based Drug DesignStructure Based Drug Design
Structure Based Drug Design
 
Molecular docking
Molecular dockingMolecular docking
Molecular docking
 
Fragment based drug design
Fragment based drug designFragment based drug design
Fragment based drug design
 
Structure based computer aided drug design
Structure based computer aided drug designStructure based computer aided drug design
Structure based computer aided drug design
 
Basics Of Molecular Docking
Basics Of Molecular DockingBasics Of Molecular Docking
Basics Of Molecular Docking
 
Data analysis workflows part 1 2015
Data analysis workflows part 1 2015Data analysis workflows part 1 2015
Data analysis workflows part 1 2015
 
Molecular Docking
 Molecular Docking Molecular Docking
Molecular Docking
 
Computer aided-drug-design-boc sciences
Computer aided-drug-design-boc sciencesComputer aided-drug-design-boc sciences
Computer aided-drug-design-boc sciences
 
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO
 

Similar to Griffen MedChemica Virtual Tox Panel

Unc slides on computational toxicology
Unc slides on computational toxicologyUnc slides on computational toxicology
Unc slides on computational toxicology
Sean Ekins
 
Finland Helsinki Drug Research slides 2011
Finland Helsinki Drug Research slides 2011Finland Helsinki Drug Research slides 2011
Finland Helsinki Drug Research slides 2011
Sean Ekins
 
Nc state lecture v2 Computational Toxicology
Nc state lecture v2 Computational ToxicologyNc state lecture v2 Computational Toxicology
Nc state lecture v2 Computational Toxicology
Sean Ekins
 
Sortase A Inhibition By Ugi Products (Complex)
Sortase A Inhibition By Ugi Products (Complex)Sortase A Inhibition By Ugi Products (Complex)
Sortase A Inhibition By Ugi Products (Complex)
Andrew Lang
 
Accelerating multiple medicinal chemistry projects using Artificial Intellige...
Accelerating multiple medicinal chemistry projects using Artificial Intellige...Accelerating multiple medicinal chemistry projects using Artificial Intellige...
Accelerating multiple medicinal chemistry projects using Artificial Intellige...
Al Dossetter
 
Grafström - Lush Prize Conference 2014
Grafström - Lush Prize Conference 2014Grafström - Lush Prize Conference 2014
Grafström - Lush Prize Conference 2014
LushPrize
 
COMPUTER AIDED DRUG DESIGN BYJayant_Nimkar
COMPUTER AIDED DRUG DESIGN BYJayant_NimkarCOMPUTER AIDED DRUG DESIGN BYJayant_Nimkar
COMPUTER AIDED DRUG DESIGN BYJayant_Nimkar
78JAYANTNIMKAR
 
COMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKAR
COMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKARCOMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKAR
COMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKAR
78JAYANTNIMKAR
 
Development and sharing of ADME/Tox and Drug Discovery Machine learning models
Development and sharing of ADME/Tox and Drug Discovery Machine learning modelsDevelopment and sharing of ADME/Tox and Drug Discovery Machine learning models
Development and sharing of ADME/Tox and Drug Discovery Machine learning models
Sean Ekins
 
COMPUTER ASSISTED DRUG DISCOVERY
COMPUTER ASSISTED DRUG DISCOVERYCOMPUTER ASSISTED DRUG DISCOVERY
COMPUTER ASSISTED DRUG DISCOVERY
Amrutha Lakshmi
 
The Utility of H/DX-MS in Biopharmaceutical Comparability Studies
The Utility of H/DX-MS in Biopharmaceutical Comparability StudiesThe Utility of H/DX-MS in Biopharmaceutical Comparability Studies
The Utility of H/DX-MS in Biopharmaceutical Comparability Studies
Abhijeet Lokras
 
An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...
An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...
An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...
Nicole Heredia
 
Talk at Yale University April 26th 2011: Applying Computational Models for To...
Talk at Yale University April 26th 2011: Applying Computational Modelsfor To...Talk at Yale University April 26th 2011: Applying Computational Modelsfor To...
Talk at Yale University April 26th 2011: Applying Computational Models for To...
Sean Ekins
 
SF and PE CTR-IN 2016 Poster_FInal
SF and PE CTR-IN 2016 Poster_FInalSF and PE CTR-IN 2016 Poster_FInal
SF and PE CTR-IN 2016 Poster_FInal
Steve Flynn
 
genotoxic_impurities-Gowtham
genotoxic_impurities-Gowthamgenotoxic_impurities-Gowtham
genotoxic_impurities-Gowtham
Yerramsetty Gowtham
 
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapyProcess Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
MilliporeSigma
 
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapyProcess Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Merck Life Sciences
 
2015 07 09__epigenetic_profiling_environmental_health_sciences_v42
2015 07 09__epigenetic_profiling_environmental_health_sciences_v422015 07 09__epigenetic_profiling_environmental_health_sciences_v42
2015 07 09__epigenetic_profiling_environmental_health_sciences_v42
Prof. Wim Van Criekinge
 
Kamilar Resume
Kamilar ResumeKamilar Resume
Kamilar Resume
Gregg Kamilar
 
Protein-protein interaction
Protein-protein interactionProtein-protein interaction
Protein-protein interaction
sigma-tau
 

Similar to Griffen MedChemica Virtual Tox Panel (20)

Unc slides on computational toxicology
Unc slides on computational toxicologyUnc slides on computational toxicology
Unc slides on computational toxicology
 
Finland Helsinki Drug Research slides 2011
Finland Helsinki Drug Research slides 2011Finland Helsinki Drug Research slides 2011
Finland Helsinki Drug Research slides 2011
 
Nc state lecture v2 Computational Toxicology
Nc state lecture v2 Computational ToxicologyNc state lecture v2 Computational Toxicology
Nc state lecture v2 Computational Toxicology
 
Sortase A Inhibition By Ugi Products (Complex)
Sortase A Inhibition By Ugi Products (Complex)Sortase A Inhibition By Ugi Products (Complex)
Sortase A Inhibition By Ugi Products (Complex)
 
Accelerating multiple medicinal chemistry projects using Artificial Intellige...
Accelerating multiple medicinal chemistry projects using Artificial Intellige...Accelerating multiple medicinal chemistry projects using Artificial Intellige...
Accelerating multiple medicinal chemistry projects using Artificial Intellige...
 
Grafström - Lush Prize Conference 2014
Grafström - Lush Prize Conference 2014Grafström - Lush Prize Conference 2014
Grafström - Lush Prize Conference 2014
 
COMPUTER AIDED DRUG DESIGN BYJayant_Nimkar
COMPUTER AIDED DRUG DESIGN BYJayant_NimkarCOMPUTER AIDED DRUG DESIGN BYJayant_Nimkar
COMPUTER AIDED DRUG DESIGN BYJayant_Nimkar
 
COMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKAR
COMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKARCOMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKAR
COMPUTER AISES DRUG DESIGN .BY JAYA NT NIMKAR
 
Development and sharing of ADME/Tox and Drug Discovery Machine learning models
Development and sharing of ADME/Tox and Drug Discovery Machine learning modelsDevelopment and sharing of ADME/Tox and Drug Discovery Machine learning models
Development and sharing of ADME/Tox and Drug Discovery Machine learning models
 
COMPUTER ASSISTED DRUG DISCOVERY
COMPUTER ASSISTED DRUG DISCOVERYCOMPUTER ASSISTED DRUG DISCOVERY
COMPUTER ASSISTED DRUG DISCOVERY
 
The Utility of H/DX-MS in Biopharmaceutical Comparability Studies
The Utility of H/DX-MS in Biopharmaceutical Comparability StudiesThe Utility of H/DX-MS in Biopharmaceutical Comparability Studies
The Utility of H/DX-MS in Biopharmaceutical Comparability Studies
 
An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...
An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...
An Actionable Annotation Scoring Framework For Gas Chromatography - High Reso...
 
Talk at Yale University April 26th 2011: Applying Computational Models for To...
Talk at Yale University April 26th 2011: Applying Computational Modelsfor To...Talk at Yale University April 26th 2011: Applying Computational Modelsfor To...
Talk at Yale University April 26th 2011: Applying Computational Models for To...
 
SF and PE CTR-IN 2016 Poster_FInal
SF and PE CTR-IN 2016 Poster_FInalSF and PE CTR-IN 2016 Poster_FInal
SF and PE CTR-IN 2016 Poster_FInal
 
genotoxic_impurities-Gowtham
genotoxic_impurities-Gowthamgenotoxic_impurities-Gowtham
genotoxic_impurities-Gowtham
 
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapyProcess Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
 
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapyProcess Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
Process Impurities: Don’t Let PEI or HCP Derail Your BioTherapy
 
2015 07 09__epigenetic_profiling_environmental_health_sciences_v42
2015 07 09__epigenetic_profiling_environmental_health_sciences_v422015 07 09__epigenetic_profiling_environmental_health_sciences_v42
2015 07 09__epigenetic_profiling_environmental_health_sciences_v42
 
Kamilar Resume
Kamilar ResumeKamilar Resume
Kamilar Resume
 
Protein-protein interaction
Protein-protein interactionProtein-protein interaction
Protein-protein interaction
 

More from Ed Griffen

MedChemica Levinthal Lecture at Openeye CUP XX 2020
MedChemica Levinthal Lecture at Openeye CUP XX 2020MedChemica Levinthal Lecture at Openeye CUP XX 2020
MedChemica Levinthal Lecture at Openeye CUP XX 2020
Ed Griffen
 
Explainable AI in Drug Hunting
Explainable AI in Drug HuntingExplainable AI in Drug Hunting
Explainable AI in Drug Hunting
Ed Griffen
 
SCI What can Big Data do for Chemistry 2017 MedChemica
SCI What can Big Data do for Chemistry 2017 MedChemicaSCI What can Big Data do for Chemistry 2017 MedChemica
SCI What can Big Data do for Chemistry 2017 MedChemica
Ed Griffen
 
Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017
Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017
Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017
Ed Griffen
 
Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...
Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...
Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...
Ed Griffen
 
MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...
MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...
MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...
Ed Griffen
 
Extracting actionable knowledge from large scale in vitro pharmacology data
Extracting actionable knowledge from large scale in vitro pharmacology dataExtracting actionable knowledge from large scale in vitro pharmacology data
Extracting actionable knowledge from large scale in vitro pharmacology data
Ed Griffen
 
Pharmacophore extraction from Matched Molecular Pair Analysis
Pharmacophore extraction from Matched Molecular Pair AnalysisPharmacophore extraction from Matched Molecular Pair Analysis
Pharmacophore extraction from Matched Molecular Pair Analysis
Ed Griffen
 

More from Ed Griffen (8)

MedChemica Levinthal Lecture at Openeye CUP XX 2020
MedChemica Levinthal Lecture at Openeye CUP XX 2020MedChemica Levinthal Lecture at Openeye CUP XX 2020
MedChemica Levinthal Lecture at Openeye CUP XX 2020
 
Explainable AI in Drug Hunting
Explainable AI in Drug HuntingExplainable AI in Drug Hunting
Explainable AI in Drug Hunting
 
SCI What can Big Data do for Chemistry 2017 MedChemica
SCI What can Big Data do for Chemistry 2017 MedChemicaSCI What can Big Data do for Chemistry 2017 MedChemica
SCI What can Big Data do for Chemistry 2017 MedChemica
 
Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017
Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017
Learning Medicinal Chemistry ADMET rules UKQSAR Sept 2017
 
Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...
Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...
Extracting medicinal chemistry knowledge by a secured Matched Molecular Pair ...
 
MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...
MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...
MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge ...
 
Extracting actionable knowledge from large scale in vitro pharmacology data
Extracting actionable knowledge from large scale in vitro pharmacology dataExtracting actionable knowledge from large scale in vitro pharmacology data
Extracting actionable knowledge from large scale in vitro pharmacology data
 
Pharmacophore extraction from Matched Molecular Pair Analysis
Pharmacophore extraction from Matched Molecular Pair AnalysisPharmacophore extraction from Matched Molecular Pair Analysis
Pharmacophore extraction from Matched Molecular Pair Analysis
 

Recently uploaded

Oedema_types_causes_pathophysiology.pptx
Oedema_types_causes_pathophysiology.pptxOedema_types_causes_pathophysiology.pptx
Oedema_types_causes_pathophysiology.pptx
muralinath2
 
Orion Air Quality Monitoring Systems - CWS
Orion Air Quality Monitoring Systems - CWSOrion Air Quality Monitoring Systems - CWS
Orion Air Quality Monitoring Systems - CWS
Columbia Weather Systems
 
Phenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvementPhenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvement
IshaGoswami9
 
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
Abdul Wali Khan University Mardan,kP,Pakistan
 
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxThe use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
MAGOTI ERNEST
 
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
David Osipyan
 
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptxBREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
RASHMI M G
 
Shallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptxShallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptx
Gokturk Mehmet Dilci
 
Medical Orthopedic PowerPoint Templates.pptx
Medical Orthopedic PowerPoint Templates.pptxMedical Orthopedic PowerPoint Templates.pptx
Medical Orthopedic PowerPoint Templates.pptx
terusbelajar5
 
ESR spectroscopy in liquid food and beverages.pptx
ESR spectroscopy in liquid food and beverages.pptxESR spectroscopy in liquid food and beverages.pptx
ESR spectroscopy in liquid food and beverages.pptx
PRIYANKA PATEL
 
Bob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdfBob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdf
Texas Alliance of Groundwater Districts
 
What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.
moosaasad1975
 
DMARDs Pharmacolgy Pharm D 5th Semester.pdf
DMARDs Pharmacolgy Pharm D 5th Semester.pdfDMARDs Pharmacolgy Pharm D 5th Semester.pdf
DMARDs Pharmacolgy Pharm D 5th Semester.pdf
fafyfskhan251kmf
 
Eukaryotic Transcription Presentation.pptx
Eukaryotic Transcription Presentation.pptxEukaryotic Transcription Presentation.pptx
Eukaryotic Transcription Presentation.pptx
RitabrataSarkar3
 
bordetella pertussis.................................ppt
bordetella pertussis.................................pptbordetella pertussis.................................ppt
bordetella pertussis.................................ppt
kejapriya1
 
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Ana Luísa Pinho
 
aziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobelaziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobel
İsa Badur
 
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốtmô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
HongcNguyn6
 
SAR of Medicinal Chemistry 1st by dk.pdf
SAR of Medicinal Chemistry 1st by dk.pdfSAR of Medicinal Chemistry 1st by dk.pdf
SAR of Medicinal Chemistry 1st by dk.pdf
KrushnaDarade1
 
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
yqqaatn0
 

Recently uploaded (20)

Oedema_types_causes_pathophysiology.pptx
Oedema_types_causes_pathophysiology.pptxOedema_types_causes_pathophysiology.pptx
Oedema_types_causes_pathophysiology.pptx
 
Orion Air Quality Monitoring Systems - CWS
Orion Air Quality Monitoring Systems - CWSOrion Air Quality Monitoring Systems - CWS
Orion Air Quality Monitoring Systems - CWS
 
Phenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvementPhenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvement
 
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
 
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxThe use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
 
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
 
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptxBREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
 
Shallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptxShallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptx
 
Medical Orthopedic PowerPoint Templates.pptx
Medical Orthopedic PowerPoint Templates.pptxMedical Orthopedic PowerPoint Templates.pptx
Medical Orthopedic PowerPoint Templates.pptx
 
ESR spectroscopy in liquid food and beverages.pptx
ESR spectroscopy in liquid food and beverages.pptxESR spectroscopy in liquid food and beverages.pptx
ESR spectroscopy in liquid food and beverages.pptx
 
Bob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdfBob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdf
 
What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.
 
DMARDs Pharmacolgy Pharm D 5th Semester.pdf
DMARDs Pharmacolgy Pharm D 5th Semester.pdfDMARDs Pharmacolgy Pharm D 5th Semester.pdf
DMARDs Pharmacolgy Pharm D 5th Semester.pdf
 
Eukaryotic Transcription Presentation.pptx
Eukaryotic Transcription Presentation.pptxEukaryotic Transcription Presentation.pptx
Eukaryotic Transcription Presentation.pptx
 
bordetella pertussis.................................ppt
bordetella pertussis.................................pptbordetella pertussis.................................ppt
bordetella pertussis.................................ppt
 
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
 
aziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobelaziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobel
 
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốtmô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
 
SAR of Medicinal Chemistry 1st by dk.pdf
SAR of Medicinal Chemistry 1st by dk.pdfSAR of Medicinal Chemistry 1st by dk.pdf
SAR of Medicinal Chemistry 1st by dk.pdf
 
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
 

Griffen MedChemica Virtual Tox Panel

  • 1. Bowes J., et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nature Reviews Drug Discovery 2012;11:909–22. Number in training set 4585 3106 2457 Median difference with/without feature (DpIC50) 0.35 -0.1 0 Cohan’s d 0.4 -0.26 0.02 Explainable AI MedChemica Virtual Toxicity Panel Screens to aid the Medicinal Chemist A. G. Dossetter•, E. Griffen•, A. Leach•+, A. Lin‡, J. Stacey†, L. Reid§, S. Montague•. •Medchemica Ltd, Macclesfield, UK, + Pharmacy and Biomolecular Sciences, Liverpool John Moores University, †Information School, University of Sheffield, ‡Laboratory of Chemoinformatics, Faculty of Chemistry, University of Strasbourg, §Bioinformatics Institute (A*STAR), 30 Biopolis Street, Matrix, Singapore 138671 Problem Unforeseen toxicity via secondary pharmacology is a significant risk and when encountered late in a discovery project’s life creates major issues and may even terminate it. Chemists need to be alerted to potential risks but to be influenced they must be able to audit the reasons and evidence for the alerts. Solution Build transparent models of critical toxicity targets and communicate results in chemical structures rather than just numbers. This is an example of ‘Explainable AI’ for chemists contact@medchemica.com Learning • Models must be transparent and show structures to influence chemists • Random Forest models with the correct descriptors can be used to show important features as pharmacophores and the evidence supporting them • Error models can given a measure of confidence to predictions beyond use of an RMSE. Chemists won’t make decisions without understanding Language of medicinal chemists = structures / clear pharmacophores Machine Learning method Description MMPA transformations Example pairs kNN + Morgan fp Structures of Nearest Neighbours Random Forest + pharmacophore fp Compound highlighted with important features Graph analytics Connections between compound families Graph Convolutional Neural Network (GCNN) Graph node feature importance – a work in progress Aspects of Models Pay attention to Feature Engineering Clear definitions enables identifying key features Transparency Scientific Sense Consistency Parsimony Applicability Performance Modeler’s domain Chemist’s domain Interpretable Failure cost high Immature science Highly skilled, critical users Business-2-Business Transparent and auditable Black Box Failure cost is low Real time response critical Interactive = self correcting Business-2-consumer User agnostic of process Trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. - Cynthia Rudin Nature Machine Intelligence (2019), 206–215. Approach Application Advanced Pharmacophore Features Feature Definition Basic Group Atom or group most likely protonated at pH 7.4 Acidic Group Atom or group most likely deprotonated at pH 7.4, includes N and C acids Acceptor Definitions derived from Taylor & Cosgrove Donor Definitions derived from Taylor & Cosgrove Hydrophobic C4 or greater cyclic or acyclic alkyl group Aromatic Attachment connection of any group to an aromatic atom excluding connections within rings Aliphatic Attachment connection of any atom to an aliphatic group not in a ring. Halo F,Cl, Br, I Gobbi, A.; Poppinger, D. Biotechnology and Bioengineering 1998, 61 (1), 47–54. Reutlinger, M.; Koch, C. P.; Reker, D.; Todoroff, N.; Schneider, P.; Rodrigues, T.; Schneider, G. Mol. Inf. 2013, 32 (2), 133–138. Taylor, R.; Cole, J. C.; Cosgrove, D. A.; Gardiner, E. J.; Gillet, V. J.; Korb, O. J Comput Aided Mol Des 2012, 26 (4), 451–472. Acid & Base definitions are SMARTS including C, N, heteroaromatic acids, bases excluding weak aniline bases, including amidines, guanidine’s - MedChemica definitions. Simple H bond acceptor base acid Precise Diclofenac (1973) Sulfadiazine (1941) Pharmacophore Pairs • Feature 1 – topological distance - Feature 2 • Engineered for chemical relevance – pairs can be superimposed or directly linked, e.g. enables a group to be both a hydrogen bond acceptor and a base • Used as unfolded 280 bit fingerprints • A bit identifies a pharmacophore pair e.g. : Aromatic - 3 bonds - Base • Random Forest feature importance and Cohan’s d for effect size allow identification of critical features in models • Highlight atoms by S Feature Importance coloured by direction of Cohan’s d • Show statistics on the effect and variance of each feature • Drill back to precise features and original compounds with data supporting that feature – complete transparency Cardiac toxicity and Seizure are key toxicological risks Cardiac hERG ion channel inhibitor NaV 1.5 channel inhibitor Ca L type channel inhibitor Ca T-type channel inhibitor PDE 3A inhibitor Seizure Dopamine D1 receptor ant/ag Dopamine D2 receptor ant/ag Cannabinoid CB1 receptor ant/ag Acetylcholine a1b2 receptor agonist / antagonists µ opioid agonist / antagonists k opioid agonist d opioid agonist/ antagonists Muscarinic M1 receptor ant/ag Muscarinic M2 receptor ant/ag Seizure 5HT 1A receptor antagonists 5HT 1B receptor antagonists 5HT receptor antagonists 2A GABA a1 antagonist NMDA-NR1 agonist 5HT Transporter inhibitor Dopamine Transporter inhib Noradrenaline Transporter inh Acetylcholine esterase inhibitor Monoamine oxidase inhibitor PDE 4D inhibitor Model ‘quality’, Error models and Domain of applicability • Build models with 10 fold CV – report CV-Pearson’s R2 and CV RMSE • Build a Random Forest error model to generate predicted error for each compound • Error model can be used to flag compounds out of Domain of Applicability hERG n=5968, RMSE = 0.16, CV Pearson’s R2 = 0.27 CHEMBL12713 sertindole, prediction pIC50 7.8 [7.1 – 8.4], actual 8.2 . Predictions and Transparency Medicinal Chemistry Seizure Models – RF and kNN Dopamine Transporter Norepinephrine Transporter 5HT1a receptor GABA-A receptor d Opioid receptor MAO-A inhibitor AChE inhibitor Training set size 1712 1757 400 1526 1070 1684 3283 CV-R2 0.28 0.23 0.37 0.24 0.28 0.21 0.32 RMSE 0.13 0.18 0.21 0.29 0.18 0.29 0.16 Best Random Forest based models for seizure endpoints, All the seizure data sets delivered kNN models based on Morgan fingerprints hERG Example