## 2 Methods and Materials  
**Database generation:** The ligand studies by [PERSON] et al. were used as our training set for ligand-based e-pharmacophore model generation [7]. All ligands studied in [PERSON] et al. had molecular weights between 350-400 Da and logP values from 2-2.5. These values were used as cut-off criteria for the molecular weight and logP to obtain molecules from the ZINC15 database, focusing only on \"In-stock\" available compounds with \"Standard\" reactivity. Additionally, to enhance our searchable database, we allowed molecules with possible charge of 0 and pH selected from the Reference (ref: pH \(\sim\)7.4) and Middle (mid: approximate pH range of 6.4 to 8.4) ranges [16; 17; 18]. 782K compounds (96 tranches) were obtained [16; 17; 18]. These compounds were used to prepare a Phase database [19; 20; 21]. The small molecules were subjected to LigPrep preparation using a previously described protocol [22]. Possible ionization states of the small molecules were generated at pH 7.0 \(\pm\) 2.0. High-energy ionization/tautomer states of the molecules were removed. Duplicate ligands were skipped during database generation. A total of 50 conformers were generated and minimized for each small molecule. ADMET properties of the small molecules were calculated using QikProp during database generation [22; 23]. Prefiltering by Lipinski's rules was performed and LigFilter properties were generated. This database was then used for ligand-based e-pharmacophore virtual screening. **Ligand-based e-pharmacophore modeling:** All 26 small molecules obtained from [PERSON] et al. were aligned using the Ligand Alignment tool in Schrodinger and used as the input ligands for e-pharmacophore model generation. First, the ligand alignment tool in Schrodinger was used to align the small molecules using their common structural elements. Following this, e-pharmacophore modeling was performed using Phase [19; 20; 21]. The best alignment and common features pharmacophore method was used. A total of 50 conformers were generated for each ligand during the pharmacophore hypothesis generation. A total of 20 ligand-based e-pharmacophore models were generated. The generated Phase database was screened through the best scored e-pharmacophore model, and the top hit compounds were retained. The ligands showing a match to all the generated e-pharmacophore sites in the pharmacophore model were kept for further studies. **Ensemble docking:** The protein used in this study is the SH2 domain of p56\({}^{\text{1 ck}}\), which is expressed in T lymphocytes. The protein p56\({}^{\text{1 ck}}\) is a 56 kDa protein TK of the p60\({}^{\text{src}}\) family which has both SH2 and Src homology 3 (SH3) domains [24]. A total of seven protein structures that included PDB IDs 1 CWD [25], 1 CWE [25], 1 LKK [26], 1 LKL [26], 1 BHH [27], 1 LCJ [15], and 1 BHF [27] were used for molecular docking studies (Figure 1A). All the seven X-ray crystal structures used in this work are specifically of the Src SH2 domain of p56\({}^{\text{1 ck}}\). The drawback of protein rigidity in molecular docking is somewhat mitigated by using ensemble docking, since any variability found in the ensemble of structures can be thought of as a way of taking into account the protein's experimentally validated flexibility. [PERSON]'s virtual screening workflow was used for the ensemble docking-based virtual screening. The workflow methodology is shown in Figure 1B. **Generation of protein 3D structure:** Water molecules and the co-crystallized ligand molecules were removed from the 7 protein-ligand PDB X-ray crystal structures. The Protein Preparation tool was used, and hydrogens were added to the protein structure using PROPKA at pH 7.0. The OPLS_2005 force field [28; 29; 30] was used to minimize the protein. Two of the PDB structures (PDB ID: 1 BHH and 1 CWE) are homodimers. In each case, only one chain was retained for these two PDB structures. All protein structures were aligned. Receptor grids were generated for each of the seven protein structures.