Computational techniques in support of drug discovery

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Computational Techniques in Support of Drug Discovery
Jeffrey Wolbach, Ph. D.

October 2, 2002

Who Is Tripos?
Discovery Software & Methods Research

Core Science & Technology

Software Consulting Services

Chemistry Products & Services

Discovery Research & Process Implementation

Sequential Drug Discovery

Choose Disease

Target Identification

Target Validation

LeadIdentification

Lead Validation

Lead Optimization

ADME

Candidate to Clinic

 

Many cycles of synthesis/testing to identify and optimize lead Role of molecular modeling
o o o o

unrealistic to jump from validated target to optimized lead useful to reduce the number of synthesis/testing cycles enables “first to file” enlarge number of targets

Drug Discovery in Parallel
•Choose a Disease Target Identification

Target Validation

Knowledge-sharing environment: genomics, HTS, chemistry, ADME, toxicology • Collect more data, on more compounds, more quickly • Apply predictive models of “developability” early

Lead Identification

Lead Validation

• •

Enhanced understanding & predictive model building Increase share of patented time on market

LeadOptimization

ADME

Candidate to Clinic

Ligand-Based Design
Ligand Structures w/Activities No Target Structure

Pharmacophore Analysis

QSAR

Discern Similarities and Differences in Active Structures

Database Searching

New Candidate Structures for Synthesis/Testing

Pharmacophore Analysis
• •
Assume active molecules share a binding mode
o

Search for common chemicalfeatures of active molecules

Don’t know binding mode, so active molecules are considered flexible
o o

Search set of pre-determined conformers Allow molecules to flex during search



Typical features:
o o o

H-Bond Donors H-bond acceptors Hydrophobic groups

Pharmacophore Models
• •
Chemical features in 3-D space Distance constraints between chemical features

QSAR

• •Relates bioactivity differences to molecular structure differences Structure represented by numerical descriptors
o o

Traditional (2D) QSAR 3D QSAR - CoMFA

Statistical techniques relate descriptors to activity D Activity ++ + + ++

+

+ ++ + +

Activity = D0 + 0.5 D1 + 0.17 D2 + ...

D Descriptor

QSAR - Traditional (2D)

Descriptors are molecular properties
o

logP, dipolemoment, connectivity indices ...
Descriptors logP = 1.9 m = 2.8 Estate = 7.2 logP = 1.7 m = 2.3 Estate = 6.7 logP = 2.1 m = 3.5 Estate = 5.5 Predictive Model (QSAR Equation)

Structures + Activity pKi=5.3

pKi=3.7

pKi=A + B(logP) + C(m) + D(Estate) + ...
PLS MLR . .

pKi=2.9

QSAR - 3D QSAR - CoMFA
• •

Comparative Molecular Field Analysis Descriptors are field strengths aroundmolecules electrostatic, steric, H-bond .. Fields can have easy physical interpretation

pKi=A + B(D1) + C(D2) + ...

QSAR/CoMFA - Interpretation

High Coefficient (important) lattice points can be plotted around molecular structures

2D Database Searching
O O N N O S N O O OH

010110010010101

O

• • •

Searches often performed on bit-strings
o o

“Fingerprints” (many types)Fingerprints display neighborhood behavior

Also includes substructure searching Can search for similarity or dissimilarity

3D Database Searching

Query is a collection of features in 3-D space
o o

Pharmacophore Lead compound / specific atomic groups



Search a database of flexible, 3-D molecules
o

o

Molecules can’t be stored in every possible conformation Allow molecules toflex to fit the query

Example of Structure-Based Design

3D Database Searching
• •
Not restricted to ligand-based design Information about target can be included in the query
o

o

o

Can define steric hindrances Additional interaction sites Serves to filter hits from the search

Identification of Novel Matrix Metalloproteinase (MMP) Inhibitors
MMPs
•Zinc-dependent proteases...
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