Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-kB receptors based on machine learning and molecular docking

arXiv:2405.00647v1 Announce Type: cross
Abstract: Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer "hormone receptor-positive and HER2-negative, hormone receptor-positive and HER2-positive, hormone receptor-negative and HER2-positive, and hormone receptor-negative and HER2-negative" it is crucial to inhibit specific targets such as EGFR, HER2, ER, NF-kB, and PR.
In this study, we evaluated various methods for binary and multiclass classification. Among them, the GA-SVM-SVM:GA-SVM-SVM model was …

abstract arxiv cancer cs.lg database exploration machine machine learning molecular docking negative novel physics.med-ph positive screening subgroups type women