Computational techniques in support of drug discovery

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

Lead Identification

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
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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

Lead Optimization

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 chemical features 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
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