Babatunde Samuel Obadawo, Oluwatoba Emmanuel Oyeneyin, Adesoji Alani Olanrewaju, Damilohun Samuel Metibemu, Sunday Adeola Emaleku, Taoreed Olakunle Owolabi, Nureni Ipinloju
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Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine Learning and Molecular Docking.
Background: The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates.
Methods: In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target.
Results: The QSAR model of choice satisfied the recommended values and was statistically significant. The R2pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds.
Conclusion: The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.
期刊介绍：
Due to the plethora of new approaches being used in modern drug discovery by the pharmaceutical industry, Current Drug Discovery Technologies has been established to provide comprehensive overviews of all the major modern techniques and technologies used in drug design and discovery. The journal is the forum for publishing both original research papers and reviews describing novel approaches and cutting edge technologies used in all stages of drug discovery. The journal addresses the multidimensional challenges of drug discovery science including integration issues of the drug discovery process.
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