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Updated: May 14, 2026

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
Published on: December 7, 2014
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.
Abstract:
KRAS was (Kirsten rat sarcoma viral oncogene homolog) revealed as an important target in current therapeutic cancer research because alteration of RAS (rat sarcoma viral oncogene homolog) protein has a critical role in malignant modification, tumor angiogenesis, and metastasis. For cancer treatment, designing competitive inhibitors for this attractive target was difficult. Nevertheless, computational investigations of the protein's dynamic behavior displayed the existence of temporary pockets that could be used to design allosteric inhibitors. The last decade witnessed intensive efforts to discover KRAS inhibitors. In 2021, the first KRAS G12C covalent inhibitor, AMG 510, received FDA (Food and drug administration) approval as an anticancer medication that paved the path for future treatment strategies against this target. Computer-aided drug designing discovery has long been used in drug development research targeting different KRAS mutants. In this review, the major breakthroughs in computational methods adapted to discover novel compounds for different mutations have been discussed. Undoubtedly, virtual screening and molecular dynamic (MD) simulation and molecular docking are the most considered approach, producing hits that can be employed in subsequent refinements. After comprehensive analysis, Afatinib and Quercetin were computationally identified as hits in different publications. Several authors conducted covalent docking studies with acryl amide warheads groups containing inhibitors. Future studies are needed to demonstrate their true potential. In-depth studies focusing on various allosteric pockets demonstrate that the switch I/II pocket is a suitable site for drug designing. In addition, machine learning and deep learning based approaches provide new insights for developing anti-KRAS drugs. We believe that this review provides extensive information to researchers globally and encourages further development in this particular area of research.
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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