Solvation directed drug design: from molecular physics to lead optimization

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: 5R35GM144089-03

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2022
    2027
  • Known Financial Commitments (USD)

    $381,372
  • Funder

    National Institutes of Health (NIH)
  • Principal Investigator

    ASSISTANT PROFESSOR Thomas Kurtzman
  • Research Location

    United States of America
  • Lead Research Institution

    HERBERT H. LEHMAN COLLEGE
  • Research Priority Alignment

    N/A
  • Research Category

    Therapeutics research, development and implementation

  • Research Subcategory

    Pre-clinical studies

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Project Summary This project aims to develop new methods and computational tools that will speed structure-based drug- discovery and apply these methods to identify new lead drug candidates for the mu-opioid receptor and SARS- CoV-2 main proteases. This will be accomplished by providing a detailed analysis of hydration structure and thermodynamics in targeted protein binding pockets then incorporating this information into docking and water-based pharmacophore virtual screens. Key aims are to develop analysis tools that characterize and map out solvation on the surfaces of drug target then utilize these solvation structural and thermodynamic maps to improve computational methods of binding pocket druggability, virtual screening of purchasable compound databases, and rational lead modification. Preliminary results for a new method of virtual screening that combines constructing pharmacophores based on water-protein interactions with ROCS fast shape and pattern matching show the method is greater than 3 orders of magnitude faster than computational docking.