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Related Experiment Videos

Hit-directed nearest-neighbor searching.

Veerabahu Shanmugasundaram1, Gerald M Maggiora, Michael S Lajiness

  • 1Structural & Computational Chemistry, Pharmacia Corporation, Kalamazoo, Michigan 49007, USA. Veerabahu.Shanmugasundaram@pfizer.com

Journal of Medicinal Chemistry
|January 7, 2005
PubMed
Summary
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This study introduces a hit-directed nearest-neighbor (HDNN) search strategy using diverse molecular representations to identify novel drug compounds. Employing multiple search methods enhances hit discovery in unexplored chemical spaces.

Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • cheminformatics

Background:

  • High-throughput screening (HTS) campaigns generate initial hits but require effective strategies for follow-up.
  • Lack of 3-D structural information and preliminary SAR models presents challenges in hit expansion.
  • Identifying diverse compounds for screening is crucial for exploring chemical space and maximizing hit potential.

Purpose of the Study:

  • To describe a practical strategy for identifying follow-up screening compounds after initial HTS.
  • To leverage diverse molecular representations for comprehensive exploration of chemical space.
  • To enhance hit discovery by employing hit-directed nearest-neighbor (HDNN) searching.

Main Methods:

  • Utilized hit-directed nearest-neighbor (HDNN) searching on compound databases.

Related Experiment Videos

  • Employed four distinct molecular representations: 3-D, 2-D, 2-D topological BCUTs (2-DT), and molecular fingerprints.
  • Applied distance-based nearest-neighbor searching for BCUTs and similarity-based measures for fingerprints.

    • Main Results:

    • Significant differences and minimal overlap were observed among the compound sets generated by the four molecular representations.
    • A 3- to 4-fold enrichment in active compounds was achieved across all representations compared to random screening.
    • Active compounds identified by each representation were largely unique, highlighting the value of diverse approaches.

    Conclusions:

    • Employing multiple HDNN searches with varied molecular representations is an effective strategy for hit expansion.
    • This approach significantly increases the identification of novel hits by exploring diverse chemical spaces.
    • The method is practical for targets lacking 3-D structural data and preliminary SAR models.