Intelligent Chemical Structure Browser for Drug Discovery and Optimization

Information

  • Research Project
  • 10241834
  • ApplicationId
    10241834
  • Core Project Number
    R44TR002699
  • Full Project Number
    2R44TR002699-02A1
  • Serial Number
    002699
  • FOA Number
    PA-20-260
  • Sub Project Id
  • Project Start Date
    2/1/2019 - 6 years ago
  • Project End Date
    3/31/2023 - 2 years ago
  • Program Officer Name
    RUDNICKI, DOBRILA DODA
  • Budget Start Date
    4/10/2021 - 4 years ago
  • Budget End Date
    3/31/2022 - 3 years ago
  • Fiscal Year
    2021
  • Support Year
    02
  • Suffix
    A1
  • Award Notice Date
    4/7/2021 - 4 years ago

Intelligent Chemical Structure Browser for Drug Discovery and Optimization

PROJECT SUMMARY Collaborative Drug Discovery, Inc. (CDD) proposes to develop a novel intelligent data browser that will enable medicinal chemists developing new drug compounds to more efficiently browse and organize experimental data in an intuitive way. The proposed browser will essentially ?hyperlink? chemical space and allow chemists to navigate easily among compounds in a chemical lead series following the same pathways that lead from one compound to the next in the mental models that they intuitively map in their heads. Navigating through and extending a lead series to discover the optimal drug candidate to advance into animal studies and clinical trials comprises a critical stage of the drug discovery pipeline: the success of large subsequent investments depends on making the right decision. This stage also especially emphasizes creative and intuitive thinking. Existing software that assists scientists engaged in this task tabulates data in formats that make it difficult to assemble and compare the essential data needed to rapidly explore ideas about how to further optimize promising candidates. Our proposed intelligent browser will support more natural and intuitive workflows. A key enabling innovation for this technology is a methodology that we have developed to organize molecular structures through a partial ordering based on the substructure-superstructure relation as a Hasse diagram. Our semilattice representation provides a machine computable format that can capture the relationships among related chemical entities that a medicinal chemist intuits. Expected key impacts include (1) faster development of lead series into drug candidates, (2) cost savings due to more efficient use of synthesis and assay resources, and most importantly (3) better scientific decisions about which compounds to pursue and advance into the clinical pipeline. Better decisions at this stage in the drug discovery process should increase the probability that drug candidates that are chosen will successfully emerge through the clinical pipeline as FDA approved drugs, and improve the effectiveness and safety profile of those drugs. Even a small increase in these probabilities multiplied by the size of the investments required to take drugs through clinical trials translates into a large value. We have validated this perception of value in preliminary market research with potential pharmaceutical company customers. !

IC Name
NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES
  • Activity
    R44
  • Administering IC
    TR
  • Application Type
    2
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    727255
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    350
  • Ed Inst. Type
  • Funding ICs
    NCATS:727255\
  • Funding Mechanism
    SBIR-STTR RPGs
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    COLLABORATIVE DRUG DISCOVERY, INC.
  • Organization Department
  • Organization DUNS
    149823846
  • Organization City
    BURLINGAME
  • Organization State
    CA
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    940101515
  • Organization District
    UNITED STATES