SlideShare a Scribd company logo
1 of 1
Download to read offline
MedChemica
Large Scale Analysis and Sharing of Medicinal Chemistry Knowledge between
Multiple Companies using Matched Molecular Pair Analysis (MMPA)
E. J. Griffen† , A. Leach† , A. Dossetter† , M. Stahl§, J. Hert§, C. Kramer§, H, T. Schindler§, J. Blaney‡, H. Zheng‡,
S, A. Gobi‡, A. Ting• , G. Robb• , †MedChemica Ltd, Macclesfield. UK, §F. Hofmann La Roche, Basel,
Switzerland, ‡Genentech Inc, South San Francisco, USA, •AstraZeneca plc , Cambridge UK.
Problem
Can we extract explicit structural know-how from
large scale mining and combining ADMET datasets?
Solution
MMPA allows secure exchange of structural knowledge and exploitation to
solve ADMET problems and identify pharmacophores and toxophores	
contact@medchemica.com
Grand Rule database
Better medicinal chemistry by sharing knowledge
not data & structures
•  Matched Molecular Pairs – Molecules
that differ only by a particular, well-
defined structural transformation
•  Transformation with environment capture –
MMPs can be recorded as transformations
from Aà B
•  Environment is essential to understand
chemistry
Statistical analysis
•  Learn what effect the transformation has had on ADMET properties in
the past
Griffen, E. et al. Matched Molecular Pairs as a Medicinal Chemistry Tool.
Journal of Medicinal Chemistry. 2011, 54(22), pp.7739-7750.
	
Advanced MMPA
Δ Data A-B
1
2
2
3
3
3
4
4
4
12
23
3
34
4
4
A 	 	 	 	B 		
Environment really matters
HàMe:
•  Median Δlog(Solubility)
•  225 different
environments
2.5log	
1.5log	
HàMe:	
•  Median Δlog(Clint)
Human microsomal
clearance
•  278 different
environments
Identify and group matching SMIRKS
Calculate statistical parameters for each unique
SMIRKS (n, median, sd, se, n_up/n_down)
Is n ≥ 6?
Not enough data:
ignore transformation
Is the |median| ≤ 0.05 and the
intercentile range (10-90%) ≤ 0.3?
Perform two-tailed binomial test on the
transformation to determine the
significance of the up/ down frequency
transformation is
classified as ‘neutral’
Transformation classified as
‘NED’ (No Effect Determined)
Transformation classified as
‘increase’ or ‘decrease’
depending on which direction the
property is changing
pass	fail	
yes	no	
yes	no	
Rule selection
0 +ve-ve
Median data difference
Neutral	 Increase	Decrease	
NED	
Merging knowledge
•  Use the transforms that are robust in
both companies to calibrate assays.
•  Once the assays are calibrated
against each other the transform
data can be combined to build
support in poorly exemplified
transforms
CalibrateRobust
Robust
Weak
Weak
Discover
Novel
Pharma	1	
Pharma	2	
Pharma 1 100k rules
Pharma 2 92k rules
Pharma 3 37k rules
5.8k rules in common (pre-merge) ~ 2%
New Rules 88k
~26% of total
Merge	
Combining	data	yields	brand	new	rules	
Gains:		300	-	900%	
Merging knowledge – GRDv1
Current Knowledge sets – GRDv3
Numbers of statistically valid transforms
MCPairs Platform
•  Extract rules using Advanced Matched Molecular Pair Analysis
•  Knowledge is captured as transformations
–  divorced from structures => sharable
Measured
Data
rule
finder Exploitable
Knowledge
MC Expert
Enumerator
System
Problem molecule
Solution molecules
Pharmacophores
& toxophores
SMARTS
matching
Alerts	
Virtual	screening	
Library	design	
Protect	the	IP	jewels	
The application helped lead optimization in project 24
193 compounds
Enumerated
Objective: improve metabolic stability
MMP
Enumeration
Calculated Property
Docking
8 compounds
synthesized
Finding Pharmacophores and
Toxophores
Mining transform sets to find influential fragments
Identify the ‘Z’ fragments associated with a
significant number of potency increasing changes –
irrespective of what they are replaced with
‘Z’ is ‘worse than anything you replace it with’
Fragment A Fragment B	
Change in binding
measurement
Public
Data
Find
Matched
Pairs
Find Potent
Fragments
+2.7	
+3.2	
+0.6	
+0.6	
Identify the ‘A’ fragments associated with a
significant number of potency decreasing changes
– irrespective of what they are replaced with
‘A’ is ‘better than anything you replace it with’
A	
+2.1	+2.2	
+1.4	
+0.4	
+1.8	
Z	
pKi/
pIC50
Compounds with
destructive fragment
Compounds with
constructive fragments
Generate	Pharmacophore	dyads	by	
permutaNng	all	the	fragments	with	
the	shortest	path	between	them	
Critical safety target analysis
•  Build models using 10-fold cross validated PLS
•  Assess using ROC / BEDROC, R2 vs 100 fold y-scrambled R2 and geomean odds ratio
Public
Data
Find
Matche
d Pairs
Pharmacophores
Find
Pharmacophore
dyads
Find Potent
Fragments
Toxophores - specific & transparent
Dopamine D2 receptor human
Actual: 9.5
Predicted: 9.1
Mean with: 8.0
Mean without: 6.6
Odds Ratio: 340
Dopamine Transporter
Actual: 9.1
Predicted: 8.6
Mean with: 8.3
Mean without: 6.7
Odds Ratio: 407
GABA-A
Actual: 9.0
Predicted: 8.7
Mean with: 8.0
Mean without: 6.8
Odds Ratio: 1506
β1 adrenergic receptor
Actual: 7.8
Predicted: 7.7
Mean with: 6.5
Mean without: 5.7
Odds Ratio: 1501
Find Potent
Fragments
Matched
Pairs
Finding
Find
Pharmacophore
Dyads
Public and in-
house potency
data
Prediction of unseen new molecules
The acid test…
•  Vascular endothelial growth factor receptor 2 tyrosine kinase (KDR)
•  Inhibitors have oncology and ophthalmic indications
•  Large dataset in CHEMBL
•  10 fold cross validated PLS model
•  Selected model by minimised RMSEP
Compounds 4466
Matched Pairs 288100
Fragments 678
Pharmacophore dyads 787
RMSEP 0.8
R2 0.64
Y-scrambled R2 0.0
ROC 0.95
Geomean odds ratio 80
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
● ●●
●●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●
●
●
●●●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
4
6
8
10
5 7 9
pIC50_pred
pIC50
Domain of Applicability….
Actual: 8.4[1]
Predicted: 7.5
Actual:	7.6[1]	
Predicted:	7.5	
1.	J	MedChem(2016),	Bold	et	al.	
2.		MedChem	LeW	(2016),	Mainolfi	et	al.	
Actual:	7.7[2]	
Predicted:	7.1	
Actual:	9.0[2]	
Predicted:	Out	of	Domain	
Calibrating Assays
•  Sets of transformations can be calibrated against each other
as we are comparing Δ values in assays not absolute values
•  Assays are usually linearly displaced against each other
Compound A
Compound B
Compound C
Compound D
Transformation 1 à
Transformation 2 à
pIC50,
log(Clint),
pSol etc
Assay 1 Assay 2
ΔT1	
ΔT2	
ΔT1’=	ΔT1	
ΔT2’=	ΔT2	
	
	
ΔT1’	
ΔT2’	
Assay 2
more
sensitive
than Assay 1
Assay 1 Δ
Assay 2 Δ
Assay 2 less
sensitive
than Assay 1
T1
T2
MedChemica | 2016
ACS Philadelphia 2016
- Fix hERG problem whilst maintaining potency
Waring et al, Med. Chem. Commun., (2011), 2, 775
Glucokinase Activators
MMPA
∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5
n=33 n=32 n=22
MMPA
∆pEC50: +0.3 ∆logD: +0.3 ∆hERG pIC50 :-0.3
n=20 n=23 n=19
MMPA
∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5
n=27 n=27 n=7
MedChemica | 2016
ACS Philadelphia 2016
A Less Simple Example
Increase logD and gain solubility
Property	 Number	of	
Observa2ons	
Direc2on	 Mean	Change	 Probability	
logD	 8	 Increase	 1.2	 100%	
Log(Solubility)	 14	 Increase	 1.4	 92%	
What	is	the	effect	on	lipophilicity	and	
solubility?	
Roche	data	is	inconclusive!	(2	pairs	
for	logD,	1	pair	for	solubility)	
logD	=	2.65	
KineMc	solubility	=	84	µg/ml	
IC50	SST5	=	0.8	µM	
logD	=	3.63	
KineMc	solubility	=	>452	µg/ml	
IC50	SST5	=	0.19	µM	
Ques2on:	
Available	
Sta2s2cs:	
Roche	
Example:	
MedChemica | 2016
ACS Philadelphia 2016
Solving a tBu metabolism issue
Benchmark	
compound	
Predicted	to	offer	most	improvement	in	microsomal	stability	(in	at	least	1	species	/	assay)	
											R2	
	
R1	
tBu	 Me	 Et	 iPr	
99	
392	
16	
64	
78	
410	
53	
550	
99	
288	
78	
515	
41	
35	
98	
327	
92	
372	
24	
247	
35	
128	
24	
62	
60	
395	
39	
445	
3	
21	
20	
27	
57	
89	
54	
89	
•  Data shown are Clint for HLM and MLM (top and bottom, respectively)
R1	 R2	R1	tBu	
Roger Butlin
Rebecca Newton
Allan Jordan
Project use
Key findings
•  Secure sharing of large scale ADMET knowledge
between large Pharma has been achieved
•  The collaboration generated great synergy
•  Many findings are highly significant
•  MMP is a great tool for idea generation. 
•  The rules have been used in drug-discovery
projects and generated meaningful results
Comparing the same change in different
environments (all statistically significant “rules”)

More Related Content

What's hot

How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...
How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...
How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...Al Dossetter
 
Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...
Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...
Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...Deepak Bandyopadhyay
 
Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013
Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013
Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013Prof. Wim Van Criekinge
 
Protein Modeling And In-Silico Drug Designing Approach
Protein Modeling And In-Silico Drug Designing ApproachProtein Modeling And In-Silico Drug Designing Approach
Protein Modeling And In-Silico Drug Designing Approachbiinoida
 
Dynamic SA/Reports - ACS Philadelphia 2012
Dynamic SA/Reports - ACS Philadelphia 2012Dynamic SA/Reports - ACS Philadelphia 2012
Dynamic SA/Reports - ACS Philadelphia 2012Deepak Bandyopadhyay
 
MedChemica BigData What Is That All About?
MedChemica BigData What Is That All About?MedChemica BigData What Is That All About?
MedChemica BigData What Is That All About?Al Dossetter
 
In silico drug design/Molecular docking
In silico drug design/Molecular dockingIn silico drug design/Molecular docking
In silico drug design/Molecular dockingKannan Iyanar
 
MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...
MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...
MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...Medicines Discovery Catapult
 

What's hot (8)

How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...
How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...
How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...
 
Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...
Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...
Scaffold-based Analytics: Enabling Hit-to-Lead Decisions by Visualizing Chemi...
 
Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013
Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013
Bioinformatics t9-t10-bio cheminformatics-wimvancriekinge_v2013
 
Protein Modeling And In-Silico Drug Designing Approach
Protein Modeling And In-Silico Drug Designing ApproachProtein Modeling And In-Silico Drug Designing Approach
Protein Modeling And In-Silico Drug Designing Approach
 
Dynamic SA/Reports - ACS Philadelphia 2012
Dynamic SA/Reports - ACS Philadelphia 2012Dynamic SA/Reports - ACS Philadelphia 2012
Dynamic SA/Reports - ACS Philadelphia 2012
 
MedChemica BigData What Is That All About?
MedChemica BigData What Is That All About?MedChemica BigData What Is That All About?
MedChemica BigData What Is That All About?
 
In silico drug design/Molecular docking
In silico drug design/Molecular dockingIn silico drug design/Molecular docking
In silico drug design/Molecular docking
 
MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...
MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...
MDC Connect: In-Silico Drug Design - what to do, what not to do - project dri...
 

Viewers also liked

график консультаций гиа 9.docx
график консультаций  гиа 9.docxграфик консультаций  гиа 9.docx
график консультаций гиа 9.docxАня Иванова
 
Dell - Intimacy and Scale in Social Media
Dell - Intimacy and Scale in Social MediaDell - Intimacy and Scale in Social Media
Dell - Intimacy and Scale in Social MediaManish Mehta
 
Decreto modifica el 191 de peatonalización
Decreto modifica el 191 de peatonalizaciónDecreto modifica el 191 de peatonalización
Decreto modifica el 191 de peatonalizaciónmauricio benitez
 
05042 0-16-140 LUZ MARINA ORTIZ BEDOYA
05042 0-16-140 LUZ MARINA ORTIZ BEDOYA05042 0-16-140 LUZ MARINA ORTIZ BEDOYA
05042 0-16-140 LUZ MARINA ORTIZ BEDOYAmauricio benitez
 
How much do you know about correct posture?
How much do you know about correct posture?How much do you know about correct posture?
How much do you know about correct posture?Eason Chan
 
Students 2.0: How Social Technology is Changing Way Students Communicate, Col...
Students 2.0: How Social Technology is Changing Way Students Communicate, Col...Students 2.0: How Social Technology is Changing Way Students Communicate, Col...
Students 2.0: How Social Technology is Changing Way Students Communicate, Col...Paul Brown
 
Digital Transformation Strategy :IMC Institute's e-Magazine
Digital Transformation Strategy :IMC Institute's e-MagazineDigital Transformation Strategy :IMC Institute's e-Magazine
Digital Transformation Strategy :IMC Institute's e-MagazineIMC Institute
 
Presentación sobre Captación de fondos - ONL
Presentación sobre Captación de fondos - ONLPresentación sobre Captación de fondos - ONL
Presentación sobre Captación de fondos - ONLaartiaga
 
9.escalante garcía genesis.actividad9.docx
9.escalante garcía genesis.actividad9.docx9.escalante garcía genesis.actividad9.docx
9.escalante garcía genesis.actividad9.docxGENES3718
 
Building the New Skills of the Networked Workplace
Building the New Skills of the Networked WorkplaceBuilding the New Skills of the Networked Workplace
Building the New Skills of the Networked WorkplaceJane Hart
 
Struktur, Jenis dan Pengembangan Paragraf
Struktur, Jenis dan Pengembangan ParagrafStruktur, Jenis dan Pengembangan Paragraf
Struktur, Jenis dan Pengembangan ParagrafNasruddin Asnah
 

Viewers also liked (16)

2010 Average Tax Rates Slides
2010 Average Tax Rates Slides2010 Average Tax Rates Slides
2010 Average Tax Rates Slides
 
график консультаций гиа 9.docx
график консультаций  гиа 9.docxграфик консультаций  гиа 9.docx
график консультаций гиа 9.docx
 
Using social media for research handout
Using social media for research handoutUsing social media for research handout
Using social media for research handout
 
Polyppolyp lynch syndrome version a
Polyppolyp lynch syndrome version aPolyppolyp lynch syndrome version a
Polyppolyp lynch syndrome version a
 
Dell - Intimacy and Scale in Social Media
Dell - Intimacy and Scale in Social MediaDell - Intimacy and Scale in Social Media
Dell - Intimacy and Scale in Social Media
 
Decreto modifica el 191 de peatonalización
Decreto modifica el 191 de peatonalizaciónDecreto modifica el 191 de peatonalización
Decreto modifica el 191 de peatonalización
 
El enfoque lean startup
El enfoque lean startupEl enfoque lean startup
El enfoque lean startup
 
05042 0-16-140 LUZ MARINA ORTIZ BEDOYA
05042 0-16-140 LUZ MARINA ORTIZ BEDOYA05042 0-16-140 LUZ MARINA ORTIZ BEDOYA
05042 0-16-140 LUZ MARINA ORTIZ BEDOYA
 
How much do you know about correct posture?
How much do you know about correct posture?How much do you know about correct posture?
How much do you know about correct posture?
 
Students 2.0: How Social Technology is Changing Way Students Communicate, Col...
Students 2.0: How Social Technology is Changing Way Students Communicate, Col...Students 2.0: How Social Technology is Changing Way Students Communicate, Col...
Students 2.0: How Social Technology is Changing Way Students Communicate, Col...
 
Digital Transformation Strategy :IMC Institute's e-Magazine
Digital Transformation Strategy :IMC Institute's e-MagazineDigital Transformation Strategy :IMC Institute's e-Magazine
Digital Transformation Strategy :IMC Institute's e-Magazine
 
Presentación sobre Captación de fondos - ONL
Presentación sobre Captación de fondos - ONLPresentación sobre Captación de fondos - ONL
Presentación sobre Captación de fondos - ONL
 
Midterm #2 Review 2015
Midterm #2 Review 2015Midterm #2 Review 2015
Midterm #2 Review 2015
 
9.escalante garcía genesis.actividad9.docx
9.escalante garcía genesis.actividad9.docx9.escalante garcía genesis.actividad9.docx
9.escalante garcía genesis.actividad9.docx
 
Building the New Skills of the Networked Workplace
Building the New Skills of the Networked WorkplaceBuilding the New Skills of the Networked Workplace
Building the New Skills of the Networked Workplace
 
Struktur, Jenis dan Pengembangan Paragraf
Struktur, Jenis dan Pengembangan ParagrafStruktur, Jenis dan Pengembangan Paragraf
Struktur, Jenis dan Pengembangan Paragraf
 

Similar to MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge poster

140128 use cases of giab RMs
140128 use cases of giab RMs140128 use cases of giab RMs
140128 use cases of giab RMsGenomeInABottle
 
SOT short course on computational toxicology
SOT short course on computational toxicology SOT short course on computational toxicology
SOT short course on computational toxicology Sean Ekins
 
RSC Hatfield 2018 Kinase meeting : potency patents MMPA approaches
RSC Hatfield 2018  Kinase meeting : potency patents MMPA approachesRSC Hatfield 2018  Kinase meeting : potency patents MMPA approaches
RSC Hatfield 2018 Kinase meeting : potency patents MMPA approachesEd Griffen
 
Cadd and molecular modeling for M.Pharm
Cadd and molecular modeling for M.PharmCadd and molecular modeling for M.Pharm
Cadd and molecular modeling for M.PharmShikha Popali
 
Data drivenapproach to medicinalchemistry
Data drivenapproach to medicinalchemistryData drivenapproach to medicinalchemistry
Data drivenapproach to medicinalchemistryAnn-Marie Roche
 
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...Predictive Models for Mechanism of Action Classification from Phenotypic Assa...
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...Ellen Berg
 
Explainable AI in Drug Hunting
Explainable AI in Drug HuntingExplainable AI in Drug Hunting
Explainable AI in Drug HuntingEd Griffen
 
Unc slides on computational toxicology
Unc slides on computational toxicologyUnc slides on computational toxicology
Unc slides on computational toxicologySean Ekins
 
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 AnalysisEd Griffen
 
High throughput screenig
High throughput screenigHigh throughput screenig
High throughput screenigShakeel Sha
 
2015 bioinformatics bio_cheminformatics_wim_vancriekinge
2015 bioinformatics bio_cheminformatics_wim_vancriekinge2015 bioinformatics bio_cheminformatics_wim_vancriekinge
2015 bioinformatics bio_cheminformatics_wim_vancriekingeProf. Wim Van Criekinge
 
Bigger Data to Increase Drug Discovery
Bigger Data to Increase Drug DiscoveryBigger Data to Increase Drug Discovery
Bigger Data to Increase Drug DiscoverySean Ekins
 
Metabolic engineering approaches in medicinal plants
Metabolic engineering approaches in medicinal plantsMetabolic engineering approaches in medicinal plants
Metabolic engineering approaches in medicinal plantsN Poorin
 
Drug development approaches
Drug development approaches Drug development approaches
Drug development approaches VIOLINA KALITA
 
2016 bioinformatics i_bio_cheminformatics_wimvancriekinge
2016 bioinformatics i_bio_cheminformatics_wimvancriekinge2016 bioinformatics i_bio_cheminformatics_wimvancriekinge
2016 bioinformatics i_bio_cheminformatics_wimvancriekingeProf. Wim Van Criekinge
 
Bioinformatics t9-t10-biocheminformatics v2014
Bioinformatics t9-t10-biocheminformatics v2014Bioinformatics t9-t10-biocheminformatics v2014
Bioinformatics t9-t10-biocheminformatics v2014Prof. Wim Van Criekinge
 
Insights from Building the Future of Drug Discovery with Apache Spark with Lu...
Insights from Building the Future of Drug Discovery with Apache Spark with Lu...Insights from Building the Future of Drug Discovery with Apache Spark with Lu...
Insights from Building the Future of Drug Discovery with Apache Spark with Lu...Databricks
 
Transcription Mechanism.pdf
Transcription Mechanism.pdfTranscription Mechanism.pdf
Transcription Mechanism.pdfBabita Neupane
 
EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...
EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...
EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...Kamel Mansouri
 
Presentation july 31_2015
Presentation july 31_2015Presentation july 31_2015
Presentation july 31_2015gkoytiger
 

Similar to MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge poster (20)

140128 use cases of giab RMs
140128 use cases of giab RMs140128 use cases of giab RMs
140128 use cases of giab RMs
 
SOT short course on computational toxicology
SOT short course on computational toxicology SOT short course on computational toxicology
SOT short course on computational toxicology
 
RSC Hatfield 2018 Kinase meeting : potency patents MMPA approaches
RSC Hatfield 2018  Kinase meeting : potency patents MMPA approachesRSC Hatfield 2018  Kinase meeting : potency patents MMPA approaches
RSC Hatfield 2018 Kinase meeting : potency patents MMPA approaches
 
Cadd and molecular modeling for M.Pharm
Cadd and molecular modeling for M.PharmCadd and molecular modeling for M.Pharm
Cadd and molecular modeling for M.Pharm
 
Data drivenapproach to medicinalchemistry
Data drivenapproach to medicinalchemistryData drivenapproach to medicinalchemistry
Data drivenapproach to medicinalchemistry
 
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...Predictive Models for Mechanism of Action Classification from Phenotypic Assa...
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...
 
Explainable AI in Drug Hunting
Explainable AI in Drug HuntingExplainable AI in Drug Hunting
Explainable AI in Drug Hunting
 
Unc slides on computational toxicology
Unc slides on computational toxicologyUnc slides on computational toxicology
Unc slides on computational toxicology
 
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
 
High throughput screenig
High throughput screenigHigh throughput screenig
High throughput screenig
 
2015 bioinformatics bio_cheminformatics_wim_vancriekinge
2015 bioinformatics bio_cheminformatics_wim_vancriekinge2015 bioinformatics bio_cheminformatics_wim_vancriekinge
2015 bioinformatics bio_cheminformatics_wim_vancriekinge
 
Bigger Data to Increase Drug Discovery
Bigger Data to Increase Drug DiscoveryBigger Data to Increase Drug Discovery
Bigger Data to Increase Drug Discovery
 
Metabolic engineering approaches in medicinal plants
Metabolic engineering approaches in medicinal plantsMetabolic engineering approaches in medicinal plants
Metabolic engineering approaches in medicinal plants
 
Drug development approaches
Drug development approaches Drug development approaches
Drug development approaches
 
2016 bioinformatics i_bio_cheminformatics_wimvancriekinge
2016 bioinformatics i_bio_cheminformatics_wimvancriekinge2016 bioinformatics i_bio_cheminformatics_wimvancriekinge
2016 bioinformatics i_bio_cheminformatics_wimvancriekinge
 
Bioinformatics t9-t10-biocheminformatics v2014
Bioinformatics t9-t10-biocheminformatics v2014Bioinformatics t9-t10-biocheminformatics v2014
Bioinformatics t9-t10-biocheminformatics v2014
 
Insights from Building the Future of Drug Discovery with Apache Spark with Lu...
Insights from Building the Future of Drug Discovery with Apache Spark with Lu...Insights from Building the Future of Drug Discovery with Apache Spark with Lu...
Insights from Building the Future of Drug Discovery with Apache Spark with Lu...
 
Transcription Mechanism.pdf
Transcription Mechanism.pdfTranscription Mechanism.pdf
Transcription Mechanism.pdf
 
EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...
EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...
EDSP Prioritization: Collaborative Estrogen Receptor Activity Prediction Proj...
 
Presentation july 31_2015
Presentation july 31_2015Presentation july 31_2015
Presentation july 31_2015
 

Recently uploaded

Advances in AI-driven Image Recognition for Early Detection of Cancer
Advances in AI-driven Image Recognition for Early Detection of CancerAdvances in AI-driven Image Recognition for Early Detection of Cancer
Advances in AI-driven Image Recognition for Early Detection of CancerLuis Miguel Chong Chong
 
Anatomy of Duodenum- detailed ppt presentation for science students
Anatomy of Duodenum- detailed ppt presentation for science studentsAnatomy of Duodenum- detailed ppt presentation for science students
Anatomy of Duodenum- detailed ppt presentation for science studentsgargipatil3210
 
6.2 Pests of Sesame_Identification_Binomics_Dr.UPR
6.2 Pests of Sesame_Identification_Binomics_Dr.UPR6.2 Pests of Sesame_Identification_Binomics_Dr.UPR
6.2 Pests of Sesame_Identification_Binomics_Dr.UPRPirithiRaju
 
𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀
𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀
𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀Tokyo Chemicals Industry (TCI)
 
Production technology of Brinjal -Solanum melongena
Production technology of Brinjal -Solanum melongenaProduction technology of Brinjal -Solanum melongena
Production technology of Brinjal -Solanum melongenajana861314
 
Pests of Sunflower_Binomics_Identification_Dr.UPR
Pests of Sunflower_Binomics_Identification_Dr.UPRPests of Sunflower_Binomics_Identification_Dr.UPR
Pests of Sunflower_Binomics_Identification_Dr.UPRPirithiRaju
 
Loudspeaker- direct radiating type and horn type.pptx
Loudspeaker- direct radiating type and horn type.pptxLoudspeaker- direct radiating type and horn type.pptx
Loudspeaker- direct radiating type and horn type.pptxpriyankatabhane
 
Combining Asynchronous Task Parallelism and Intel SGX for Secure Deep Learning
Combining Asynchronous Task Parallelism and Intel SGX for Secure Deep LearningCombining Asynchronous Task Parallelism and Intel SGX for Secure Deep Learning
Combining Asynchronous Task Parallelism and Intel SGX for Secure Deep Learningvschiavoni
 
STELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By Karishma
STELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By KarishmaSTELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By Karishma
STELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By KarishmaAMiracle3
 
Remote patient monitoring :Health care transformation
Remote patient monitoring :Health care transformationRemote patient monitoring :Health care transformation
Remote patient monitoring :Health care transformationfahad Alotaibiu
 
Science (Communication) and Wikipedia - Potentials and Pitfalls
Science (Communication) and Wikipedia - Potentials and PitfallsScience (Communication) and Wikipedia - Potentials and Pitfalls
Science (Communication) and Wikipedia - Potentials and PitfallsDobusch Leonhard
 
6.1 Pests of Groundnut_Binomics_Identification_Dr.UPR
6.1 Pests of Groundnut_Binomics_Identification_Dr.UPR6.1 Pests of Groundnut_Binomics_Identification_Dr.UPR
6.1 Pests of Groundnut_Binomics_Identification_Dr.UPRPirithiRaju
 
Rabies ,a deadly viral disease transmitted by the most beautiful beings. by ...
Rabies ,a deadly viral disease transmitted by the most beautiful beings.  by ...Rabies ,a deadly viral disease transmitted by the most beautiful beings.  by ...
Rabies ,a deadly viral disease transmitted by the most beautiful beings. by ...uzmashireenmbe01
 
DNA isolation molecular biology practical.pptx
DNA isolation molecular biology practical.pptxDNA isolation molecular biology practical.pptx
DNA isolation molecular biology practical.pptxGiDMOh
 
Think Science: What Are Eclipses, by Craig Bobchin
Think Science: What Are Eclipses, by Craig BobchinThink Science: What Are Eclipses, by Craig Bobchin
Think Science: What Are Eclipses, by Craig BobchinNathan Cone
 
Timeless Cosmology: Towards a Geometric Origin of Cosmological Correlations
Timeless Cosmology: Towards a Geometric Origin of Cosmological CorrelationsTimeless Cosmology: Towards a Geometric Origin of Cosmological Correlations
Timeless Cosmology: Towards a Geometric Origin of Cosmological CorrelationsDanielBaumann11
 
Environmental acoustics- noise criteria.pptx
Environmental acoustics- noise criteria.pptxEnvironmental acoustics- noise criteria.pptx
Environmental acoustics- noise criteria.pptxpriyankatabhane
 
ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...
ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...
ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...Chayanika Das
 
Food_safety_Management_pptx.pptx in microbiology
Food_safety_Management_pptx.pptx in microbiologyFood_safety_Management_pptx.pptx in microbiology
Food_safety_Management_pptx.pptx in microbiologyHemantThakare8
 

Recently uploaded (20)

Advances in AI-driven Image Recognition for Early Detection of Cancer
Advances in AI-driven Image Recognition for Early Detection of CancerAdvances in AI-driven Image Recognition for Early Detection of Cancer
Advances in AI-driven Image Recognition for Early Detection of Cancer
 
Anatomy of Duodenum- detailed ppt presentation for science students
Anatomy of Duodenum- detailed ppt presentation for science studentsAnatomy of Duodenum- detailed ppt presentation for science students
Anatomy of Duodenum- detailed ppt presentation for science students
 
6.2 Pests of Sesame_Identification_Binomics_Dr.UPR
6.2 Pests of Sesame_Identification_Binomics_Dr.UPR6.2 Pests of Sesame_Identification_Binomics_Dr.UPR
6.2 Pests of Sesame_Identification_Binomics_Dr.UPR
 
𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀
𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀
𝗧𝗖𝗢 (𝙩𝙧𝙖𝙣𝙨-𝗰𝘆𝗰𝗹𝗼𝗼𝗰𝘁𝗲𝗻𝗲) 𝗗𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲𝘀: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗴𝗲𝗻𝘁𝘀
 
Production technology of Brinjal -Solanum melongena
Production technology of Brinjal -Solanum melongenaProduction technology of Brinjal -Solanum melongena
Production technology of Brinjal -Solanum melongena
 
Pests of Sunflower_Binomics_Identification_Dr.UPR
Pests of Sunflower_Binomics_Identification_Dr.UPRPests of Sunflower_Binomics_Identification_Dr.UPR
Pests of Sunflower_Binomics_Identification_Dr.UPR
 
Loudspeaker- direct radiating type and horn type.pptx
Loudspeaker- direct radiating type and horn type.pptxLoudspeaker- direct radiating type and horn type.pptx
Loudspeaker- direct radiating type and horn type.pptx
 
Introduction Classification Of Alkaloids
Introduction Classification Of AlkaloidsIntroduction Classification Of Alkaloids
Introduction Classification Of Alkaloids
 
Combining Asynchronous Task Parallelism and Intel SGX for Secure Deep Learning
Combining Asynchronous Task Parallelism and Intel SGX for Secure Deep LearningCombining Asynchronous Task Parallelism and Intel SGX for Secure Deep Learning
Combining Asynchronous Task Parallelism and Intel SGX for Secure Deep Learning
 
STELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By Karishma
STELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By KarishmaSTELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By Karishma
STELLAR SYSTEM IN PTERIDOPHYTE Seminar 2023- By Karishma
 
Remote patient monitoring :Health care transformation
Remote patient monitoring :Health care transformationRemote patient monitoring :Health care transformation
Remote patient monitoring :Health care transformation
 
Science (Communication) and Wikipedia - Potentials and Pitfalls
Science (Communication) and Wikipedia - Potentials and PitfallsScience (Communication) and Wikipedia - Potentials and Pitfalls
Science (Communication) and Wikipedia - Potentials and Pitfalls
 
6.1 Pests of Groundnut_Binomics_Identification_Dr.UPR
6.1 Pests of Groundnut_Binomics_Identification_Dr.UPR6.1 Pests of Groundnut_Binomics_Identification_Dr.UPR
6.1 Pests of Groundnut_Binomics_Identification_Dr.UPR
 
Rabies ,a deadly viral disease transmitted by the most beautiful beings. by ...
Rabies ,a deadly viral disease transmitted by the most beautiful beings.  by ...Rabies ,a deadly viral disease transmitted by the most beautiful beings.  by ...
Rabies ,a deadly viral disease transmitted by the most beautiful beings. by ...
 
DNA isolation molecular biology practical.pptx
DNA isolation molecular biology practical.pptxDNA isolation molecular biology practical.pptx
DNA isolation molecular biology practical.pptx
 
Think Science: What Are Eclipses, by Craig Bobchin
Think Science: What Are Eclipses, by Craig BobchinThink Science: What Are Eclipses, by Craig Bobchin
Think Science: What Are Eclipses, by Craig Bobchin
 
Timeless Cosmology: Towards a Geometric Origin of Cosmological Correlations
Timeless Cosmology: Towards a Geometric Origin of Cosmological CorrelationsTimeless Cosmology: Towards a Geometric Origin of Cosmological Correlations
Timeless Cosmology: Towards a Geometric Origin of Cosmological Correlations
 
Environmental acoustics- noise criteria.pptx
Environmental acoustics- noise criteria.pptxEnvironmental acoustics- noise criteria.pptx
Environmental acoustics- noise criteria.pptx
 
ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...
ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...
ESSENTIAL FEATURES REQUIRED FOR ESTABLISHING FOUR TYPES OF BIOSAFETY LABORATO...
 
Food_safety_Management_pptx.pptx in microbiology
Food_safety_Management_pptx.pptx in microbiologyFood_safety_Management_pptx.pptx in microbiology
Food_safety_Management_pptx.pptx in microbiology
 

MedChemica Large scale analysis and sharing of Medicinal chemistry Knowledge poster

  • 1. MedChemica Large Scale Analysis and Sharing of Medicinal Chemistry Knowledge between Multiple Companies using Matched Molecular Pair Analysis (MMPA) E. J. Griffen† , A. Leach† , A. Dossetter† , M. Stahl§, J. Hert§, C. Kramer§, H, T. Schindler§, J. Blaney‡, H. Zheng‡, S, A. Gobi‡, A. Ting• , G. Robb• , †MedChemica Ltd, Macclesfield. UK, §F. Hofmann La Roche, Basel, Switzerland, ‡Genentech Inc, South San Francisco, USA, •AstraZeneca plc , Cambridge UK. Problem Can we extract explicit structural know-how from large scale mining and combining ADMET datasets? Solution MMPA allows secure exchange of structural knowledge and exploitation to solve ADMET problems and identify pharmacophores and toxophores contact@medchemica.com Grand Rule database Better medicinal chemistry by sharing knowledge not data & structures •  Matched Molecular Pairs – Molecules that differ only by a particular, well- defined structural transformation •  Transformation with environment capture – MMPs can be recorded as transformations from Aà B •  Environment is essential to understand chemistry Statistical analysis •  Learn what effect the transformation has had on ADMET properties in the past Griffen, E. et al. Matched Molecular Pairs as a Medicinal Chemistry Tool. Journal of Medicinal Chemistry. 2011, 54(22), pp.7739-7750. Advanced MMPA Δ Data A-B 1 2 2 3 3 3 4 4 4 12 23 3 34 4 4 A B Environment really matters HàMe: •  Median Δlog(Solubility) •  225 different environments 2.5log 1.5log HàMe: •  Median Δlog(Clint) Human microsomal clearance •  278 different environments Identify and group matching SMIRKS Calculate statistical parameters for each unique SMIRKS (n, median, sd, se, n_up/n_down) Is n ≥ 6? Not enough data: ignore transformation Is the |median| ≤ 0.05 and the intercentile range (10-90%) ≤ 0.3? Perform two-tailed binomial test on the transformation to determine the significance of the up/ down frequency transformation is classified as ‘neutral’ Transformation classified as ‘NED’ (No Effect Determined) Transformation classified as ‘increase’ or ‘decrease’ depending on which direction the property is changing pass fail yes no yes no Rule selection 0 +ve-ve Median data difference Neutral Increase Decrease NED Merging knowledge •  Use the transforms that are robust in both companies to calibrate assays. •  Once the assays are calibrated against each other the transform data can be combined to build support in poorly exemplified transforms CalibrateRobust Robust Weak Weak Discover Novel Pharma 1 Pharma 2 Pharma 1 100k rules Pharma 2 92k rules Pharma 3 37k rules 5.8k rules in common (pre-merge) ~ 2% New Rules 88k ~26% of total Merge Combining data yields brand new rules Gains: 300 - 900% Merging knowledge – GRDv1 Current Knowledge sets – GRDv3 Numbers of statistically valid transforms MCPairs Platform •  Extract rules using Advanced Matched Molecular Pair Analysis •  Knowledge is captured as transformations –  divorced from structures => sharable Measured Data rule finder Exploitable Knowledge MC Expert Enumerator System Problem molecule Solution molecules Pharmacophores & toxophores SMARTS matching Alerts Virtual screening Library design Protect the IP jewels The application helped lead optimization in project 24 193 compounds Enumerated Objective: improve metabolic stability MMP Enumeration Calculated Property Docking 8 compounds synthesized Finding Pharmacophores and Toxophores Mining transform sets to find influential fragments Identify the ‘Z’ fragments associated with a significant number of potency increasing changes – irrespective of what they are replaced with ‘Z’ is ‘worse than anything you replace it with’ Fragment A Fragment B Change in binding measurement Public Data Find Matched Pairs Find Potent Fragments +2.7 +3.2 +0.6 +0.6 Identify the ‘A’ fragments associated with a significant number of potency decreasing changes – irrespective of what they are replaced with ‘A’ is ‘better than anything you replace it with’ A +2.1 +2.2 +1.4 +0.4 +1.8 Z pKi/ pIC50 Compounds with destructive fragment Compounds with constructive fragments Generate Pharmacophore dyads by permutaNng all the fragments with the shortest path between them Critical safety target analysis •  Build models using 10-fold cross validated PLS •  Assess using ROC / BEDROC, R2 vs 100 fold y-scrambled R2 and geomean odds ratio Public Data Find Matche d Pairs Pharmacophores Find Pharmacophore dyads Find Potent Fragments Toxophores - specific & transparent Dopamine D2 receptor human Actual: 9.5 Predicted: 9.1 Mean with: 8.0 Mean without: 6.6 Odds Ratio: 340 Dopamine Transporter Actual: 9.1 Predicted: 8.6 Mean with: 8.3 Mean without: 6.7 Odds Ratio: 407 GABA-A Actual: 9.0 Predicted: 8.7 Mean with: 8.0 Mean without: 6.8 Odds Ratio: 1506 β1 adrenergic receptor Actual: 7.8 Predicted: 7.7 Mean with: 6.5 Mean without: 5.7 Odds Ratio: 1501 Find Potent Fragments Matched Pairs Finding Find Pharmacophore Dyads Public and in- house potency data Prediction of unseen new molecules The acid test… •  Vascular endothelial growth factor receptor 2 tyrosine kinase (KDR) •  Inhibitors have oncology and ophthalmic indications •  Large dataset in CHEMBL •  10 fold cross validated PLS model •  Selected model by minimised RMSEP Compounds 4466 Matched Pairs 288100 Fragments 678 Pharmacophore dyads 787 RMSEP 0.8 R2 0.64 Y-scrambled R2 0.0 ROC 0.95 Geomean odds ratio 80 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 4 6 8 10 5 7 9 pIC50_pred pIC50 Domain of Applicability…. Actual: 8.4[1] Predicted: 7.5 Actual: 7.6[1] Predicted: 7.5 1. J MedChem(2016), Bold et al. 2. MedChem LeW (2016), Mainolfi et al. Actual: 7.7[2] Predicted: 7.1 Actual: 9.0[2] Predicted: Out of Domain Calibrating Assays •  Sets of transformations can be calibrated against each other as we are comparing Δ values in assays not absolute values •  Assays are usually linearly displaced against each other Compound A Compound B Compound C Compound D Transformation 1 à Transformation 2 à pIC50, log(Clint), pSol etc Assay 1 Assay 2 ΔT1 ΔT2 ΔT1’= ΔT1 ΔT2’= ΔT2 ΔT1’ ΔT2’ Assay 2 more sensitive than Assay 1 Assay 1 Δ Assay 2 Δ Assay 2 less sensitive than Assay 1 T1 T2 MedChemica | 2016 ACS Philadelphia 2016 - Fix hERG problem whilst maintaining potency Waring et al, Med. Chem. Commun., (2011), 2, 775 Glucokinase Activators MMPA ∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5 n=33 n=32 n=22 MMPA ∆pEC50: +0.3 ∆logD: +0.3 ∆hERG pIC50 :-0.3 n=20 n=23 n=19 MMPA ∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5 n=27 n=27 n=7 MedChemica | 2016 ACS Philadelphia 2016 A Less Simple Example Increase logD and gain solubility Property Number of Observa2ons Direc2on Mean Change Probability logD 8 Increase 1.2 100% Log(Solubility) 14 Increase 1.4 92% What is the effect on lipophilicity and solubility? Roche data is inconclusive! (2 pairs for logD, 1 pair for solubility) logD = 2.65 KineMc solubility = 84 µg/ml IC50 SST5 = 0.8 µM logD = 3.63 KineMc solubility = >452 µg/ml IC50 SST5 = 0.19 µM Ques2on: Available Sta2s2cs: Roche Example: MedChemica | 2016 ACS Philadelphia 2016 Solving a tBu metabolism issue Benchmark compound Predicted to offer most improvement in microsomal stability (in at least 1 species / assay) R2 R1 tBu Me Et iPr 99 392 16 64 78 410 53 550 99 288 78 515 41 35 98 327 92 372 24 247 35 128 24 62 60 395 39 445 3 21 20 27 57 89 54 89 •  Data shown are Clint for HLM and MLM (top and bottom, respectively) R1 R2 R1 tBu Roger Butlin Rebecca Newton Allan Jordan Project use Key findings •  Secure sharing of large scale ADMET knowledge between large Pharma has been achieved •  The collaboration generated great synergy •  Many findings are highly significant •  MMP is a great tool for idea generation.  •  The rules have been used in drug-discovery projects and generated meaningful results Comparing the same change in different environments (all statistically significant “rules”)