Hit expansion approaches using multiple similarity methods and virtualized query structures.

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• Ligand-based virtual screening (LBVS) and hit expansion are crucial for identifying novel active compounds.
• Combining complementary similarity search methods improves LBVS campaign performance.

Purpose of the Study:

• To evaluate virtualizing template structures (query, seed) as an extension to common similarity search methods.
• To assess the effectiveness of template virtualization for hit expansion after high-throughput screening.

Main Methods:

• Utilized virtualised template structures through bioisosteric enumeration and rule-based methods.
• Integrated template virtualization with standard fingerprint and pharmacophore graph-based similarity searches.
• Validated methods using four diverse test datasets and two epigenetic target case studies (G9a, LSD1).

Main Results:

• Template virtualization, combined with standard similarity searches, proved effective for hit expansion.
• The approach demonstrated reliability across different target classes and specific epigenetic targets.
• Enhanced identification of novel active chemical entities was achieved.

Conclusions:

• Virtualized templates represent a powerful computational strategy for hit expansion in drug discovery.
• This method complements existing virtual screening techniques, improving the discovery of active compounds.
• The approach shows promise for identifying ligands against various biological targets, including epigenetic ones.