## 3 Results and Discussion
### Pharmacokine-Based Design  
Pharmacophore modeling of the PBD compounds was performed to predict functional geometric features important for ER\(\alpha\) bioactivity. Pharmacophore modeling is an in silico method used to indicate the activity of compounds based on their pharmacophore features. Pharmacophore features encompass a drug compound's functional groups or chemical fea \begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **Donor** & **Acceptor** & **Occupancy (\%)** \\ \hline \multirow{4}{*}{4-OHT} & OHT553 & LEU387 & 0.05 \\  & OHT553 & GLU353 & 69.08 \\  & ASN532 & OHT553 & 0.25 \\  & ARG394 & OHT553 & 0.10 \\ \hline \multirow{2}{*}{Lasofoxifene} & LSF553 & GLU353 & 58.14 \\  & THR347 & LSF553 & 0.05 \\ \hline \multirow{2}{*}{Raloxifene} & RLF553 & LEU387 & 3.05 \\  & ARG394 & RLF553 & 0.10 \\  & RLF553 & GLU353 & 67.58 \\  & RLF553 & GLV521 & 48.05 \\  & RLF553 & GLV420 & 6.09 \\  & RLF553 & GLU419 & 2.40 \\ \hline \multirow{2}{*}{PBD-17} & S17553 & GLU353 & 16.28 \\ \hline \multirow{4}{*}{PBD-20} & S20553 & GLU353 & 20.68 \\  & ARG394 & S20553 & 0.70 \\ \cline{1-1}  & S20553 & VAL354 & 0.20 \\ \cline{1-1}  & S20553 & ALA350 & 0.55 \\ \cline{1-1}  & S20553 & ASP351 & 0.15 \\ \cline{1-1}  & THR347 & S20553 & 0.15 \\ \hline \end{tabular}
\end{table} 
Table 6: Hydrogen bond analysis from 200 ns MD trajectories of all systems. \begin{table}
\begin{tabular}{c c c c c} \hline
**Energy Component** & **4 OHT** & **Lasofoxifene** & **Raloxifene** & **PDB-17** & **PDB-20** \\ \hline van der Waal energy & \(-210.692\) +/ \(-49.723\) & \(-206.511\) +/ \(-85.204\) & \(-182.284\) +/ \(-89.907\) & \(-143.024\) +/ \(-125.396\) & \(-269.768\) +/ \(-13.616\) \\ \hline Electrostatic energy & \(-46.459\) +/ \(-15.339\) & \(-33.386\) +/ \(-14.431\) & \(-51.407\) +/ \(-29.272\) & \(-18.581\) +/ \(-17.862\) & \(-37.782\) +/ \(-9.535\) \\ \hline Polar solvation energy & \(134.072\) +/ \(-25.233\) & \(102.413\) +/ \(-46.862\) & \(131.862\) +/ \(-51.000\) & \(118.106\) +/ \(-59.301\) & \(194.421\) +/ \(17.221\) \\ \hline SASA energy & \(-22.228\) +/ \(-4.753\) & \(-20.402\) +/ \(-8.369\) & \(-19.327\) +/ \(-9.859\) & \(-14.730\) +/ \(-12.733\) & \(-26.333\) +/ \(-1.201\) \\ \hline Binding energy & \(-145.307\) +/ \(-46.242\) & \(-157.886\) +/ \(-62.032\) & \(-121.156\) +/ \(-74.705\) & \(-58.229\) +/ \(-100.631\) & \(-139.462\) +/ \(-15.049\) \\ \hline \end{tabular}
\end{table} 
Table 7: The decomposition of calculated binding energies using MM-PBSA. tures responsible for its biological activity [27]. The approach employed in pharmacophore modeling is to identify chemical feature similarities among the compounds being tested and compounds known to be active against the receptor [82]. Compounds with the same pharmacophore features are predicted to possess comparable biological activity, as they are likely to occupy the same binding region on the target receptor [83]. Pharmacophore modeling of ligand-based was carried out using 200 active sets (active compounds) and 4000 decoy sets (inactive compounds) against ER\(\alpha\). This resulted in ten pharmacophore models with scores ranging from 0.7400 to 0.8295 with four pharmacophore features, including hydrogen-bond donors, hydrogen bond acceptors, hydrophobic interaction, and aromatic ring (Figure 9). Before being used as a reference in virtual screening, a pharmacophore model must undergo validation to ensure its ability to accurately distinguish between active and inactive compounds. In this study, ten pharmacophore models were validated, and their performance was assessed using Receiver Operating Characteristic (ROC) curve. The ROC curve describes the relationship between the X-axis as false positive (1-specificity) and the Y-axis as true positive (sensitivity) (Figure 10). The Area Under the Curve (AUC) in the ROC curve represents a quantitative measure of model performance with a value ranging from 0 to 1 (or 0% to 100%).