PhD Defense CYH

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



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



3 21 June 2011



4 21 June 2011

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



5 21 June 2011

• 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



6 21 June 2011

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



7 21 June 2011

GeneGear – De novo design by an Evolutionary

Algorithm (EA)



8 21 June 2011

GeneGear – Building blocks (fragments)

Split

Screen

Molecules

Fragments

1151 fragments

National Cancer

Institute (NCI)

diversity set (1990

molecules)



9 21 June 2011

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



10 21 June 2011

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)



11 21 June 2011

GeneGear application - Evolutionary

drug design



12 21 June 2011

Building a fragment library

Screen

Split

1154

Fragments

NCI diversity set

(1990 molecules)

Indinavir – a HIV-1

protease inhibitor

98 entriesselect



13 21 June 2011

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



15 21 June 2011

GeneGear application - Evolutionary coordination

catalyst design



16 21 June 2011

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



20 21 June 2011

Case study: Ruthenium catalyst for olefin metathesis

Occhipinti G. et al., J. Am. Chem. Soc., 128:6952–6964, 2006.



21 21 June 2011

A PLSR-based QSAR model for productivity

Q2=0.85, RMSECV=1.46 kcal/mol

PM6 optimized geometry of

14-electron active complex



22 21 June 2011

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





24 21 June 2011

Parameter setup for EA experiments



25 21 June 2011

Results of EA experiments – evolution trends

NHC > phosphine

(Second gen.) (First gen.)

Average of predicted

productivity increasessmoothly over generations



26 21 June 2011

Results of EA experiments – evolution trends (contd)



27 21 June 2011

Results of EA experiments – high active complex

4.8N3

1.9N2

2.8N1DFT-calc. prod. (kcal/mol)Complex



28 21 June 2011

A knowledge-based approach of GeneGear

for constraining de novo EA search space



29 21 June 2011

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



30 21 June 2011

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.



31 21 June 2011

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)



32 21 June 2011

Results of BF and non-BF experiment

logP > 4

logP > 4.8



33 21 June 2011

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.



34 21 June 2011

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!

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PhD Defense CYH