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Rohan Chandraghatgi1, Hai-Feng Ji2, Gail L Rosen3

  • 1Department of Biology, Drexel University, Philadelphia, Pennsylvania 19104, United States.

Journal of Chemical Information and Modeling
|May 2, 2024
PubMed
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This summary is machine-generated.

This study enhances computational drug discovery by optimizing ligand synthesis using a two-stage method. It efficiently identifies high-affinity drug candidates for various diseases, accelerating therapeutic development.

Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Bioinformatics

Background:

  • Computational methods, including machine learning, are crucial for drug discovery but face challenges with vast chemical spaces.
  • Existing fragment-based drug discovery (FBDD) methods can be further optimized for efficiency.

Purpose of the Study:

  • To introduce a two-stage optimization method for computational ligand synthesis, building upon the fragment databases from screened ligand drug discovery (FDSL-DD) approach.
  • To improve the efficiency and precision of identifying high-affinity drug candidates by focusing on promising chemical regions.

Main Methods:

  • Utilizing in silico screening to identify and fragment ligands, attaching attributes based on predicted binding affinity.
  • Implementing a two-stage optimization: genetic algorithms for fragment assembly, followed by iterative refinement for enhanced bioactivity.

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  • Demonstrating the methodology on diverse targets: human solid cancers, bacterial antimicrobial resistance, and SARS-CoV-2.

    • Main Results:

    • The proposed FDSL-DD with two-stage optimization yields high-affinity ligand candidates more efficiently than current state-of-the-art computational FBDD methods.
    • A multiobjective optimization approach incorporating drug-likeness also produced potential high-affinity ligands.
    • The integrated approach significantly optimizes the initial drug discovery process.

    Conclusions:

    • Integrating detailed chemical information with a constrained search framework markedly optimizes early-stage drug discovery.
    • The developed methodology offers a more precise and efficient route for developing novel therapeutics.
    • This approach accelerates the identification of drug candidates for critical diseases.