Evolution of computational techniques against various KRAS mutants in search for therapeutic drugs: a review article

Ayesha Mehmood1, Mohammed Ageeli Hakami2, Hanan A Ogaly3

  • 1Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan.

Insights

Computational methods are advancing cancer therapy by identifying new KRAS inhibitors. Researchers are exploring allosteric pockets and using machine learning to develop novel anti-KRAS drugs, building on recent FDA approvals.

Area of Science:

  • Oncology
  • Computational Chemistry
  • Drug Discovery

Background:

  • KRAS mutations are critical in cancer development, driving malignancy, angiogenesis, and metastasis.
  • Targeting KRAS for cancer treatment has been challenging due to difficulties in designing competitive inhibitors.
  • Recent advances include the FDA approval of the first KRAS G12C covalent inhibitor, AMG 510.

Purpose of the Study:

  • To review computational methods for discovering novel compounds targeting various KRAS mutations.
  • To highlight breakthroughs in computer-aided drug design for KRAS-targeted therapies.
  • To identify promising drug candidates and suitable binding sites for future anti-KRAS drug development.

Main Methods:

  • Virtual screening
  • Molecular dynamic (MD) simulations
  • Molecular docking, including covalent docking studies
  • Machine learning and deep learning approaches

Main Results:

  • Computational investigations revealed temporary pockets suitable for allosteric inhibitor design.
  • Afatinib and Quercetin were identified as computationally derived hits.
  • The switch I/II pocket was confirmed as a viable site for drug design.

Conclusions:

  • Computational methods, particularly virtual screening and MD simulations, are crucial for identifying KRAS inhibitor candidates.
  • Emerging machine learning and deep learning approaches offer new avenues for anti-KRAS drug development.
  • Further research is needed to validate computationally identified inhibitors and explore allosteric inhibition strategies.

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