Data Visualization in Cheminformatics

Simon XiComputational Sciences CoE

Pfizer Cambridge

My BackgroundProfessional ExperienceSenior Principal Scientist, Computational Sciences CoE, Pfizer

Cambridge9-year experience in pharmaceutical research with a focused on

developing cheminformatics and bioinformatics applications for research scientists

EducationMSc in Molecular Cell Biology in UTDallasMSc in Software Engineering in SMUFinishing Ph.D in Bioinformatics in Boston University

What we will cover today

• Introduction to drug discovery• Cheminformatics basics• Encoding of the chemical structures• Visualizing data and structures• Design and optimization of compound library• A case study

The Billion Dollar Molecules

Drug Name2006 World-Wide Sales Primary Use

Lipitor $14,385M cholesterolNexium $5,182M heartburnAdvair $6,129M asthmaPrevacid $3,425M heartburnPlavix $6,057M anticoagulantSingulair $3,579M asthmaSeroquel $3,560M depressionEffexor $3,722M depressionNorvasc $4,866M hypertension

Lipitor – 14 billion annual sales

$0

$5

$10

$15

$20

$25

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

# NM

Es

604020200

Source: Source: PhRMAPhRMA annual survey, 2000annual survey, 2000

Total R&D Investment ($ Billions)

Industry Productivity vs. InvestmentThe Challenge

Nature Reviews Drug Discovery 3, 451-456 (2004)

NME/$

Preclinical Pharmacology

Preclinical Safety

Millions ofCompounds Screened

Idea Drug11 - 15 Years

~100 Discovery Approaches~100 Discovery Approaches

1 - 2 Products

Discovery Exploratory Development Full Development

Phase I Phase II Phase III

0 155 10

Clinical Pharmacology& Safety

Attrition On The R&D Process

Nat Rev Drug Discov. 2007 6:636-49.

What is Chemoinformatics?

• Use of computer and informational techniques, applied to a range of problems in the field of chemistry.

• These in silico techniques are commonly used in pharmaceutical companies in the process of drug discovery.• Chemistry is a visual science. Data visualization is a key component of cheminformatics.

What is Chemoinformatics?

Encoding Chemical Structures

LipitorAtoms

Bonds

SD format

CC(C)C1=C(C(=O)NC2=CC=CC=C2)C(C3=CC=CC=C3)=C(N1CCC(O)CC(O)CC(O)=O)C4=CC=C(F)C=C4

SMILES format

Representing Structure as Fingerprints

010 0 100 0 1001 00000 1 00……

Compound Similarity Search

Compound Properties/Descriptors1D, 2D, 3D, multi-dimensional properties• 1D: Molecular Weight, clogP, #of Atoms,

charge, #H-Bond donors and acceptors• 2D: Atom pairs, substructures functional groups• 3D: Shape, pharmacophores• nD: Fingerprints, etc..

Chemical series – compounds sharing the same core structures

3D

Series Classifications –Wards Clustering

Iteratively merging a pair of nodes until all nodes are merged.

At each merging step, two nodes that give minimal variance are chosen and merged into one new node.

Once the tree hierarchy is generated, clusters can be defined by cutting the tree at certain dissimilarity threshold

Toxicity Properties• Inhibition of CYP450 isozymes• PXR transactivation• Human hepatocyte toxicity• Mutagenicity• Mitochondria toxicity• Covalent protein binding• Inhibition of HERG

ADME/Physicochemical Properties• Solubility• Chemical stability• Hydrophobicity/hydrogen bonding

potential• Intestinal mucosal cell permeation• Liver and kidney clearance• Metabolism• Transporters• Charge• Size• Protein binding• Blood-brain barrier permeation• Target cell permeation

Primary pharmacology• In vitro potency• Cell based potency• Functional assays• Selectivity against other targets

What makes a drug?

Drug-Likeness: Rule of Five

Proposed by C. Lipinski to describe ‘drug-like’ molecules. Molecules displaying good oral absorption and /or distribution properties are likely to possess the following characteristics:

– Molecular Weight < 500– logP < 5.0– H-donors < 5– H-acceptors (number of N and O atoms) < 10

Data VisualizationGrid View

Table View

Plot View

Heatmap View

Software RelevanceSoftware UsabilitySoftware Management

Building Predictive Models using Machine Learning Techniques

• Use computational models to understand Structure-ActivitiveRelationship (SAR)

• Use computational models to run virtual screen to guide compound selection for synthesis

Interpretability of Predictive Models

The good part The not so good part

Can we derive this for non-linear models?

Multiple Parameter Optimization in Combinatorial Library Design

Given a 100x100x100 virtual library space and a set of predictive models for various properties (e.g. potency, ADME, selectivity), select the best 300 compounds for synthesis with the highest probability of being potent and drug-like and with diverse sampling of the chemical space

N

NN N

R3

R1 R2

For example diaminopyrimidine library

The problem of Multiple Parameters Optimzation

• The chemical space is huge

• Predictive models are not very predictive

• Many parameters to optimize and sometime contradictory to each other

MPO – a case study with kinase selectivity

N

NN N

F FF

R1 R2Trifluoro-diaminopyrimidine

series (~200 cmpds)

R1

R2

Tested compounds

Only few combination Rgroup-Kinase have been previously tested

R2

Model Building R1 FW

Solving R-groups contribution using linear regression

Identify compounds with desired seletivityprofile in the expanded virtual chemical space

Virtual Library Profile

Enumeration

Predictable Virtual Chemical Space

R2

R1

5-50x expansion

~200 cmpds from a library tested against 40 kinases, can we design another 100 cmpds that are highly selective

Predictive models - Leave-One-Out Validations

Experimental Validation of PredictionsKSS pIC50 vs. FW pIC50

r2=0.45 r2=0.59 r2=0.92 r2=0.86

r2=0.74 r2=0.83 r2=0.63 r2=0.88

r2=0.85 r2=0.81 r2=0.81 r2=0.85

~40 cmpds in two series were selected for KSS testing

More promiscuous

More selective

Cheminformatics Challenges for Drug Discovery

• Information retrieval and knowledge managment - rapidly and efficiently present all relevant data/knowledge to scientists atthe right time and right place

• Predictive models - drastically improve the accuracy and interpretability of in silico models for potency and ADME endpoints

• Computer-aided design – provide easy to use software applications to help scientists analyze/visualize their data andmake efficient use of prior knowledge during compound designs

References

1. Agrafiotis, D. K., Lobanov, V. S. and Salemme, F. R. (2002) Combinatorial informatics in the post-genomics ERA. Nat Rev Drug Discov. 1, 337-3462. Lipinski, C. and Hopkins, A. (2004) Navigating chemical space for biology and medicine. Nature. 432, 855-8613. Paolini, G. V., Shapland, R. H., van Hoorn, W. P., Mason, J. S. and Hopkins, A. L. (2006) Global mapping of pharmacological space. Nat Biotechnol. 24, 805-815