AUTOSTERE: Systematic Search for Scaffold Replacement Opportunities within Structural Databases.

Area of Science:

• Computational chemistry
• Drug discovery
• Medicinal chemistry

Background:

• Medicinal chemists often reuse known scaffolds, limiting exploration of novel drug targets.
• Existing methods for scaffold exploration are often limited in scale and scope.

Purpose of the Study:

• To develop a computational algorithm (AUTOSTERE) for systematic scaffold replacement and target identification.
• To identify new drug targets for established chemical scaffolds.

Main Methods:

• Designed the AUTOSTERE algorithm to assess scaffold replacement opportunities within a large database of ligand structures.
• Evaluated potential designs within the original ligand's protein environment.
• Applied the method to substituted triazolinone and pyridopyrimidine scaffolds.

Main Results:

• Identified approximately 600 novel triazolinone-containing ligands for targets including HSP-90, cathepsin K, and TIE-2 kinase.
• Discovered potential novel HIV reverse transcriptase inhibitors based on pyridopyrimidine cores, which were synthesized and validated.
• Demonstrated the algorithm's ability to suggest new target opportunities for preferred chemistries.

Conclusions:

• The AUTOSTERE methodology offers a powerful new approach for identifying novel drug targets for known chemical scaffolds.
• This systematic, large-scale scaffold replacement strategy can significantly broaden drug discovery efforts.
• The approach facilitates the exploration of less-conventional chemical cores for therapeutic potential.