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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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[In Silico Drug Design Using an Evolutionary Algorithm and Compound Database].

Kentaro Kawai1, Yoshimasa Takahashi

  • 1Drug Research Center, Kaken Pharmaceutical Co. Ltd.

Yakugaku Zasshi : Journal of the Pharmaceutical Society of Japan
|January 5, 2016
PubMed
Summary

This study introduces a novel computational drug design algorithm that uses fragments and evolutionary operations to create new drug-like molecules. The method successfully generated valid molecules with desired properties for G protein-coupled receptors (GPCRs).

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Computational drug design is crucial for identifying novel therapeutics.
  • Existing methods can be enhanced by leveraging structural information of known active molecules.

Purpose of the Study:

  • To develop and validate a new algorithm for designing drug-like molecules.
  • To utilize fragment-based building blocks and evolutionary algorithms for molecular generation.
  • To explore the application of this method in designing ligands for G protein-coupled receptors (GPCRs).

Main Methods:

  • Defined three fragment types (ring, linker, side-chain) from the GPCR-SARfari database.
  • Implemented an evolutionary algorithm with operations like crossover, mutation, and selection.
  • Employed the Tanimoto coefficient as a fitness function for molecular evolution.
  • Conducted docking studies to assess binding site complementarity.

Main Results:

  • The algorithm successfully designed novel molecules with structural similarity to known active compounds.
  • Generated molecules exhibited scaffold diversity and chemical validity.
  • Docking studies confirmed shape complementarity of designed molecules with target GPCR binding sites.

Conclusions:

  • The proposed algorithm is a powerful tool for de novo drug design.
  • It facilitates the generation of diverse and valid drug-like molecules.
  • This approach holds significant promise for accelerating drug discovery projects.