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Updated: Jun 29, 2025
Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
Published on: October 23, 2019
Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the
Amar Ajmal1, Hind A Alkhatabi2, Roaa M Alreemi2
1Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, 23200, Pakistan.
Abstract:
Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).
Insights
Machine learning identified novel inhibitors for KRAS G12C lung cancer. These new compounds show promise in preventing KRAS-associated lung cancer, offering potential new treatments.
Area of Science:
- Oncology
- Computational Chemistry
- Drug Discovery
Background:
- Lung cancer is a leading global cause of cancer death.
- KRAS G12C mutations drive 12-14% of non-small cell lung cancers, historically considered undruggable.
- Emerging targeted therapies like sotorasib and adagrasib face challenges with drug resistance.
Purpose of the Study:
- To identify novel inhibitors targeting the KRAS G12C mutant using machine learning-based virtual screening.
- To validate computational models and assess the binding potential of newly identified compounds.
Main Methods:
- Machine learning models (Random Forest, k-NN, Gaussian Naive Bayes, SVM) were developed and validated.
- Virtual screening of multiple databases (ZINC15, in-house, phytochemicals) was performed using the best-performing Random Forest model.
- Molecular docking, molecular dynamics simulations, and binding energy calculations were employed to evaluate top candidates.
Main Results:
- The Random Forest model demonstrated superior performance in predicting KRAS G12C inhibitors.
- Four novel compounds were identified with enhanced stability and binding affinities compared to the standard drug.
- These hits showed significant potential for inhibiting KRAS G12C.
Conclusions:
- Machine learning and computational methods can accelerate the discovery of targeted cancer therapies.
- The identified novel inhibitors represent promising candidates for preventing KRAS-associated lung cancer.
- This study provides a valuable resource for future drug development against KRAS G12C.
