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8/6/2019 PhD Defense CYH
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1 21 June 2011
Development of Methods for de novo Design of
Functional Drugs and Catalyst Compounds
Yunhan Chu
Department of Chemistry,
Norwegian University of Science and Technology (NTNU)
PhD thesis defense
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2 21 June 2011
Outline
Introduction
Overview of GeneGear for de novo design
Evolutionary de novo drug design by GeneGear
Evolutionary de novo coordination catalyst design by
GeneGear A knowledge-based approach of GeneGear for
constraining de novo EA search space
Conclusions Acknowledgements
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How to explore chemical space ?
• Exploring known chemical spaceHigh Throughput Screening (HTS)
Virtual Screening (VS)
• Exploring novel chemical space
De novo design
– Using computer to produce novel molecular structures with
desired properties by taking chemical space as a source
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• How to sample chemical structures
• How to evaluate chemical structures
• How to navigate through the space
smartly
De novo Design
Exploring chemical space by de novo design
Molecular representation
Building blocks
Structural operations
Scoring function
Search algorithm
Schneider G. et al., Nat. Rev. Drug Discov., 4:649–663, 2005
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GeneGear – An open source software for
de novodesign
Advantages of GeneGear:
Freedom in how the system is used, modified and extended
Design of non-medicinal compounds
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GeneGear – De novo design by an Evolutionary
Algorithm (EA)
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GeneGear – Building blocks (fragments)
Split
Screen
Molecules
Fragments
1151 fragments
National Cancer
Institute (NCI)
diversity set (1990
molecules)
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GeneGear – Structural representation and operation
N
N
O
O
N
N
Cl
O
N
F
FN
N
N
N
O
O
N
N
Cl
O
N
F
FN
N
N
NN
N
O
N
N
O
N
F
F
O
Cl
1
N
NN
N
O
N
N
O
N
F
F
O
Cl
2 3 4
Crossover
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GeneGear – Scoring function
Multiobjective scoring
f(p) = w1p1 + w2p2 + ... + wnpn
Receptor-based scoring
Receptor-ligand binding free energy (affinity)
– Force-field based function (AutoDock)
– Empirical and knowledge-based function (Vina)
Ligand-based scoring Molecular similarity
Quantitative structure-activity relationship (QSAR)
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GeneGear application - Evolutionary
drug design
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Building a fragment library
Screen
Split
1154
Fragments
NCI diversity set
(1990 molecules)
Indinavir – a HIV-1
protease inhibitor
98 entriesselect
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Receptor-based scoring (binding energy)Ligand-based scoring (similarity)
Design of HIV-1 protease inhibitor
Indinavir fragment set NCI fragments
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Multiobjective scoring (half-to-half weighted combination of receptor- and
ligand-based strategy)
Design of HIV-1 protease inhibitor - contd
Indinavir
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GeneGear application - Evolutionary coordination
catalyst design
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Characteristics of coordination compound
covalent bond
dative bond
metal
ionic
neutralionic
neutral
Traditional de novo methods lack the following functions:
To maintain and protect the coordination center
To retrieve information associated with the coordination center
To vary ligand groups in a restricted and meaningful manner
To maintain possible characteristics of symmetric structures
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Representation of coordination compound
ExampleModel
c: core, t: trial, f: free
lead
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Growing from a “lead”
Assembly of coordination compounds
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Assembly of coordination compounds - contd
Crossover of “free” parts
Mutation of “free” part
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Case study: Ruthenium catalyst for olefin metathesis
Occhipinti G. et al., J. Am. Chem. Soc., 128:6952–6964, 2006.
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A PLSR-based QSAR model for productivity
Q2=0.85, RMSECV=1.46 kcal/mol
PM6 optimized geometry of
14-electron active complex
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Design of ruthenium catalysts
Ru
P
Cl
Cl
Ru
Cl
Cl
Ru
L
Cl
Cl
R1R3
R1
R2
N N
R2
R3R4
Ru
Cl
Cl
R1
R2
N N
R3R4
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Parameter setup for EA experiments
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Results of EA experiments – evolution trends
NHC > phosphine
(Second gen.) (First gen.)
Average of predicted
productivity increasessmoothly over generations
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Results of EA experiments – evolution trends (contd)
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Results of EA experiments – high active complex
4.8N3
1.9N2
2.8N1DFT-calc. prod. (kcal/mol)Complex
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A knowledge-based approach of GeneGear
for constraining de novo EA search space
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Advantage and challenge of evolutionary
algorithms in de novo
design
Biasfilter
Newmolecules
FitnessfunctionEA
Discarded
Newmolecules
FitnessfunctionEA
• Advantages:
– Sampling a diverse chemical space
– Providing solutions to a wide range of objective problems– Performing well in searching a large and complex space
• Challenge:
– Production of chemically insensible structures
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Building a bias filter (BF)
• A “bias molecule set” to sample positive and negative examples.
• A set of structure descriptors to characterize the “bias set” structure space.
• A classification method to model the positive/negative boundary.
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bias filter
290 Factor Xa inhibitors, 77 descriptors
high lipophilic region, logP > 4.0
93%Test set (145)
94%LOO CV
AccuracyValidation
?
Application of bias filter (BF)
k-NN (k = 2)
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Results of BF and non-BF experiment
logP > 4
logP > 4.8
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Conclusions
• De novo design is an important concept that allows a variety of computational
knowledge, methods and tools to be implemented to explore chemical space.
• GeneGear has been tested to be effective at de novo design of functional
molecules such as drugs by the implementation of a parallel EA framework.
• A new EA facilitated with special molecular representation and operations,quantum chemistry, and QSAR analysis is adapted for optimization of
coordination compounds.
• A knowledge-based approach built with chemometrics, multivariate analysis, andmachine learning is able to to constrain de novo EA searched space.
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Acknowledgments
The Department of Chemistry, NTNU is gratefully thanked for funding
this research.
Members of Physical Chemistry group are thanked for their good advice.
Prof. Bjørn K. Alsberg is thanked for all his support and help.
Thank you for your attention!