## 3 Results and Discussion  
**Ligand-based e-pharmacophore models:** A total of 20 ligand-based e-pharmacophore models were generated (Supplementary Table S1). The best e-pharmacophore model had the highest Survival score and was tied for the highest Phase Hypo score. The best ligand-based Phase e-pharmacophore model was a five-point model (DHRRR_1) containing three aromatic rings (R), one hydrogen bond donor (D), and one hydrophobic group (H) (Figure 2A). It was used for the virtual screening of the generated Phase database. **Pharmacophore-based virtual screening of Phase database:** The generated Phase database was screened against the best ligand-based e-pharmacophore model. The top \(\sim\)35K molecules were retained that matched 5 of 5 e-pharmacophore sites in the ligand-based e-pharmacophore model. The ligand and pharmacokinetic properties of the successfully screened ligands that were retained are shown in Figure 2B-E. The Phase screen scores and the volume scores after e-pharmacophore screening are shown in Figure 2B. The ligands showed predicted binding to human serum albumin within a range of \(-\)1.0 to \(+\)1.0, thereby falling within the recommended range of values (Figure 2C). A QPPCaco value (predicted apparent Caco-2 cell permeability in nm/s) of \(>\)500 is typically recommended; all ligands showed a value of \(>\)500 (Figure 2D). A predicted IC\({}_{50}\) of \(<\)\(-\)5 for the blockage of human ether-a-go-go-related gene (hERG) K\({}^{+}\) channels (QP log\({}_{\text{hERG}}\)) is not recommended. Therefore, the ligands that showed a value \(>\)\(-\)5 could be considered for further drug design purposes (Figure 2D). The predicted apparent Madin-Darby Canine Kidney (MDCK) cell permeability values were \(>\)500 nm/s, thus falling within the recommended range (Figure 2E). However, some molecules exhibited a value of \(<\)500 nm/s for the MDCK cell permeability as well. Such molecules could be further investigated using alternate permeability prediction models for accurate prediction of their permeability properties. Additionally, the values of the predicted brain/blood partition coefficient also fell within the recommended range (\(-\)3.0 to 1.2) (Figure 2E). **Ensemble docking-based virtual screening:** Following the steps as mentioned under Section 2, ensemble docking was performed using the top \(\sim\)35K ligands that were  
Figure 2: Ligand-based e-pharmacophore model and pharmacophore screening scores and predicted physicochemical and pharmacokinetic properties of the successfully screened ligands. (**A**) Best ligand-based e-pharmacophore model generated. (**B**) Pharmacophore screening scores. (**C**–**E**) Predicted physicochemical and pharmacokinetic properties of the ligands obtained after screening through the best e-pharmacophore model. obtained from e-pharmacophore-based virtual screening. At the end of Glide XP docking, 202 molecules were retained. All 202 molecules showed a negative docking score with XP GScore of \(<\)\(-\)4.278 kcal/mol. The top hit molecule (Figure 3A) obtained at the end of ensemble docking showed a docking score of \(-\)6.530 kcal/mol. The hit compound exhibited four hydrogen bonding interactions with the 1 LKL protein (Figure 3B). The ligand binding site is depicted in Figure 3C-D. This hit molecule was then subjected to ligand-based CH and receptor-based CH for novel small molecule design. The top 10 hit molecules from the pool of 202 ligands obtained after virtual screening using ensemble docking are shown in Supplementary Figures S1 and S2. Supplementary Figure S1 shows the 2D structures of the top 10 hit ligands, while Supplementary Figure S2 depicts the docked poses into the ligand binding site on the protein. **Ligand-based core hopping (LCH):** The best scored docked hit molecule obtained from ensemble docking-based virtual screening (Figure 3A) was used as the reference in ligand-based core hopping (LCH). Two scaffold growing points were selected on the reference molecule to replace the middle scaffold of the molecule (Figure 4A).