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 selected with
an accuracy of 0.74, an F1-score of 0.73, and an AUC of 0.94 for virtual
screening of ligands from the BindingDB database. This model successfully
identified 4454, 803, 438, and 378 ligands with over 90% precision in both
active/inactive and target prediction for the classes of EGFR+HER2, ER, NF-kB,
and PR, respectively, from the BindingDB database. Based on to the selected
ligands, we created a dendrogram that categorizes different ligands based on
their targets. This dendrogram aims to facilitate the exploration of chemical
space for various therapeutic targets.
Ligands that surpassed a 90% threshold in the product of activity probability
and correct target selection probability were chosen for further investigation
using molecular docking. The binding energy range for these ligands against
their respective targets was calculated to be between -15 and -5 kcal/mol.
Finally, based on general and common rules in medicinal chemistry, we selected
2, 3, 3, and 8 new ligands with high priority for further studies in the
EGFR+HER2, ER, NF-kB, and PR classes, respectively.

通过虚拟筛选与化学空间的探索，我们选择了针对乳腺癌治疗靶点 EGFR+HER2、ER、NF-kB 和 PR 的具有高优先级的新配体。

基于机器学习和分子对接的对 BindingDB 数据库配体在 EGFR、HER2、雌激素受体、孕激素受体和 NF-kB 受体的筛选

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