Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study
Abstract
:1. Introduction
2. Results and Discussion
2.1. Chemical Distribution of the Datasets
2.2. Predictive KRAS pIC50 ML Model Based on XGBoost Algorithm of All Molecular Fingerprints
2.3. KRAS Inhibitor Structural Importance Identified by SHAP Algorithm
2.4. Applicability Domain
2.5. Prediction of FDA-Approved Drugs against KRASG12C Protein
2.6. Covalent Docking of Predicted Compounds against KRASG12C Protein
2.7. Molecular Dynamics Simulation of FDA-Approved Drugs and KRAS G12C Protein
2.8. Summary of New Predicted FDA-Approved Drugs against KRAS Mutations
3. Materials and Methods
3.1. Data Collection
3.2. Molecular Fingerprints and Molecular Descriptors Calculation
3.3. Extreme Gradient Boosting
3.4. Classification Model Construction
3.5. Classification Model Evaluation
3.6. Regression Model Construction
3.7. Regression Model Evaluation
3.8. Y-Randomization
3.9. Feature Importance
3.10. Applicability Domain
3.11. Covalent Docking
3.12. Molecular Dynamic Simulation
3.13. Statistical Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Molecular Fingerprints | Descriptions | Examples 1 |
---|---|---|
PubChemFP378 | C(~N)(:C)(:N) | |
SubFPC274 | Aromatic atom | |
SubFPC307 | Chiral center | |
SubFPC171 | Aryl Chloride | |
SubFPC1 | Primary carbon | |
SubFPC295 | C-O, N, or S bond | |
PubChemFP261 | ≥4 aromatic rings | |
PubChemFP192 | ≥3 any ring size 6 | |
SubFPC300 | 1,3-tautomerizable | |
SubFPC2 | Secondary carbon |
Name | Predicted Classes | Predicted pIC50 | Experimental pIC50 † | Primary Targets [30] | Primary Indications [30] |
---|---|---|---|---|---|
Afatinib | Active | 7.43 | No report | EGFR and HER2 | NSCLC |
Dacomitinib | Active | 7.32 | No report | EGFR T790M | NSCLC |
Acalabrutinib | Active | 6.75 | No report | BTK | MCL |
Neratinib | Active | 6.68 | No report | EGFR, HER2, HER4 | Breast cancer |
Zanubrutinib | Active | 6.49 | No report | BTK | MCL |
Dutasteride | Active | 5.94 | No report | 5α-Reductase | BPH |
Finasteride | Active | 5.05 | No report | 5α-Reductase | Alopecia, BPH |
Sotorasib * | Active | 6.95 | 7.52 | KRASG12C | NSCLC |
Adagrasib * | Active | 8.02 | 8.30 | KRASG12C | NSCLC |
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Srisongkram, T.; Weerapreeyakul, N. Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study. Int. J. Mol. Sci. 2023, 24, 669. https://doi.org/10.3390/ijms24010669
Srisongkram T, Weerapreeyakul N. Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study. International Journal of Molecular Sciences. 2023; 24(1):669. https://doi.org/10.3390/ijms24010669
Chicago/Turabian StyleSrisongkram, Tarapong, and Natthida Weerapreeyakul. 2023. "Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study" International Journal of Molecular Sciences 24, no. 1: 669. https://doi.org/10.3390/ijms24010669