Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo Optimization
Introduction: The Imatinib drug is used to treat blood cancer by inhibiting the BCR-ABL tyrosine kinase enzyme, which prevents the proliferation of cancer cells. Materials & Methods: In order to predict the binding affinity of 555 compounds of imatinib derivatives as ABL-BCR tyrosine kinase inh...
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Ilam University of Medical Sciences
2024-09-01
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| Series: | Majallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām |
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| Online Access: | http://sjimu.medilam.ac.ir/article-1-8190-en.pdf |
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| author | Shahram Lotfi Shahin Ahmadi Sharare Vardast Baghmisheh Ali Almasirad |
| author_facet | Shahram Lotfi Shahin Ahmadi Sharare Vardast Baghmisheh Ali Almasirad |
| author_sort | Shahram Lotfi |
| collection | DOAJ |
| description | Introduction: The Imatinib drug is used to treat blood cancer by inhibiting the BCR-ABL tyrosine kinase enzyme, which prevents the proliferation of cancer cells.
Materials & Methods: In order to predict the binding affinity of 555 compounds of imatinib derivatives as ABL-BCR tyrosine kinase inhibitors, quantitative structure-activity relationship (QSAR) modeling was performed using the Monte Carlo method. The data were randomly divided into four series, including training, invisible training, calibration, and validation sets, as well as they were randomly repeated three times.
Results: The results of three random divisions indicated reliable models for predicting the set of external tests with correlation coefficient (R2) and cross-validation correlation coefficient (Q2) in the range of 0.8575-0.8775 and 0.7620-0.7793. Consequently, the obtained models help identify hybrid descriptors for increasing or decreasing binding affinity (Ki) as BCR-ABL tyrosine kinase inhibitors. The mechanical interpretation of the model is given in the form of a report of descriptors that decrease and increase pKi, as well as examples of these descriptors.
Conclusion: The results reveal that the designed models can be considerably effective in estimating the biological effect of imatinib derivatives proposed by researchers and medicinal chemists. Therefore, it is possible to predict its possible biological effects by spending less time and money before conducting in vitro or in vivo experiments. |
| format | Article |
| id | doaj-art-2e6a4ae091ab491d9873eed5b55663d0 |
| institution | Kabale University |
| issn | 1563-4728 2588-3135 |
| language | fas |
| publishDate | 2024-09-01 |
| publisher | Ilam University of Medical Sciences |
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| series | Majallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām |
| spelling | doaj-art-2e6a4ae091ab491d9873eed5b55663d02025-08-25T07:34:31ZfasIlam University of Medical SciencesMajallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām1563-47282588-31352024-09-013246686Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo OptimizationShahram Lotfi0Shahin Ahmadi1Sharare Vardast Baghmisheh2Ali Almasirad3 Dept of Chemistry, Payame Noor University (PNU), Tehran, Iran Dept of Pure and Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran medical sciences, Islamic Azad University, Tehran, Iran Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran Dept of Pure and Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran medical sciences, Islamic Azad University, Tehran, Iran Introduction: The Imatinib drug is used to treat blood cancer by inhibiting the BCR-ABL tyrosine kinase enzyme, which prevents the proliferation of cancer cells. Materials & Methods: In order to predict the binding affinity of 555 compounds of imatinib derivatives as ABL-BCR tyrosine kinase inhibitors, quantitative structure-activity relationship (QSAR) modeling was performed using the Monte Carlo method. The data were randomly divided into four series, including training, invisible training, calibration, and validation sets, as well as they were randomly repeated three times. Results: The results of three random divisions indicated reliable models for predicting the set of external tests with correlation coefficient (R2) and cross-validation correlation coefficient (Q2) in the range of 0.8575-0.8775 and 0.7620-0.7793. Consequently, the obtained models help identify hybrid descriptors for increasing or decreasing binding affinity (Ki) as BCR-ABL tyrosine kinase inhibitors. The mechanical interpretation of the model is given in the form of a report of descriptors that decrease and increase pKi, as well as examples of these descriptors. Conclusion: The results reveal that the designed models can be considerably effective in estimating the biological effect of imatinib derivatives proposed by researchers and medicinal chemists. Therefore, it is possible to predict its possible biological effects by spending less time and money before conducting in vitro or in vivo experiments.http://sjimu.medilam.ac.ir/article-1-8190-en.pdfquantitative structure-activity relationship (qsar)chronic myeloid leukemiaimatinib derivativestyrosine kinase inhibitorbinding affinity |
| spellingShingle | Shahram Lotfi Shahin Ahmadi Sharare Vardast Baghmisheh Ali Almasirad Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo Optimization Majallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Īlām quantitative structure-activity relationship (qsar) chronic myeloid leukemia imatinib derivatives tyrosine kinase inhibitor binding affinity |
| title | Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo Optimization |
| title_full | Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo Optimization |
| title_fullStr | Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo Optimization |
| title_full_unstemmed | Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo Optimization |
| title_short | Predicting Binding Affinity of Some Imatinib Derivatives as BCR-ABL Tyrosine Kinase Inhibitors Based on Monte Carlo Optimization |
| title_sort | predicting binding affinity of some imatinib derivatives as bcr abl tyrosine kinase inhibitors based on monte carlo optimization |
| topic | quantitative structure-activity relationship (qsar) chronic myeloid leukemia imatinib derivatives tyrosine kinase inhibitor binding affinity |
| url | http://sjimu.medilam.ac.ir/article-1-8190-en.pdf |
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