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Related Experiment Videos

Computational chemistry-driven decision making in lead generation.

Volker Schnecke1, Jonas Boström

  • 1Computational Lead Discovery, Department of Medicinal Chemistry, AstraZeneca R&D Mölndal, S-43183 Mölndal, Sweden. volker.schnecke@astrazeneca.com

Drug Discovery Today
|February 16, 2006
PubMed
Summary
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Drug discovery projects identify many initial compounds, then use computational chemistry to narrow them down to a few promising lead series for drug development. Making the right choice early is critical to avoid costly setbacks.

Area of Science:

  • Medicinal Chemistry
  • Computational Drug Discovery
  • Pharmacology

Background:

  • Drug discovery projects initiate with screening large or focused compound libraries to identify active molecules.
  • A significant number of initial active compounds require reduction to a manageable set of promising lead series.
  • The hit-to-lead phase is critical for selecting series with the highest potential for successful drug development.

Purpose of the Study:

  • To outline the process of reducing numerous initial drug discovery hits to a few viable lead series.
  • To highlight the role of computational chemistry in navigating the hit-to-lead and lead optimization stages.
  • To emphasize the importance of strategic decision-making in selecting lead series for drug development.

Main Methods:

Related Experiment Videos

  • Utilizing computational chemistry tools for property prediction and hit clustering.
  • Designing focused compound libraries for efficient screening.
  • Employing analogue searching to explore the potential of hit series.
  • Sequential filtering of active compounds based on predicted properties and potential.

    • Main Results:

    • Identification of hundreds or thousands of initial active compounds from screening efforts.
    • Application of computational methods to prioritize and select a small number of promising lead series.
    • Successful progression of selected lead series through hit-to-lead refinement.

    Conclusions:

    • Computational chemistry is indispensable for efficient hit-to-lead progression in drug discovery.
    • Strategic selection of lead series early in the process is paramount for successful drug candidate development.
    • The hit-to-lead phase demands rigorous evaluation to mitigate the risks of costly, irreversible decisions.