Optimized Modeling of Anti-Breast Cancer Drug Candidates
DOI:
https://doi.org/10.62051/ijcsit.v3n2.19Keywords:
Machine learning, Random forest, LightGB regression, SVM, Feature selectionAbstract
Breast cancer is often referred to as the "Pink Killer", and its incidence rate ranks first among female malignant tumors. It has been found that the expression of estrogen receptors alpha (ERα) plays a very important role in breast lesions, and compounds that can antagonize the activity of ERα may be candidates for the treatment of breast cancer. In this paper, it is of practical significance to construct a quantitative structure-activity relationship (QSAR) model of compounds to screen potential compounds that can antagonize the activity of ERα by using a machine learning approach, and to construct a classification prediction model of ADMET properties to predict the pharmacokinetic properties and safety of compounds in the human body.
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