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New strategies for pharmaceutical design

S A Gillmor¹, F E Cohen

¹Graduate Group in Biophysics, University of California, San Francisco 94143-0446.

Receptor

|January 1, 1993

Summary

Accelerating drug discovery involves exploring new methods beyond peptides and nucleotides. Computational screening and homology models offer promising avenues for identifying novel nonpeptide pharmaceuticals, even without structural data.

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Area of Science:

Medicinal Chemistry
Computational Biology
Parasitology

Background:

Traditional drug discovery methods often focus on peptides and nucleotides, which have limitations such as poor bioavailability and enzymatic instability.
These limitations hinder their development into effective therapeutics.
Exploring alternative molecular scaffolds and synthesis strategies is crucial for advancing drug discovery.

Purpose of the Study:

To investigate novel approaches for accelerating the drug design and discovery process.
To identify effective nonpeptide pharmaceuticals using computational methods.
To explore the utility of homology-based models in identifying enzyme inhibitors.

Main Methods:

Investigating parallel synthesis and testing procedures.
Utilizing structural data and computational screens of compound databases.
Employing homology-based models for virtual screening when structural information is absent.

Main Results:

Nonpeptide pharmaceuticals identified through computational screening and homology modeling show potential.
Homology-based models were sufficient to identify nonpeptide inhibitors.
Identified inhibitors demonstrated activity at low micromolar concentrations against key parasitic enzymes.

Conclusions:

Computational screening and homology modeling represent viable strategies for discovering novel nonpeptide pharmaceuticals.
These methods can overcome limitations associated with peptide and nucleotide-based drug candidates.
The findings support the development of new therapeutics targeting parasitic enzymes.

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