Although inhibition of OAT1 can result in an elevation of drug concentration in blood; however, a consistent drug level monitoring is required to maintain the efficacy. This is because a low concentration of a drug will reduce its activity but a high concentration can result in drug toxicity. Therefore, further research is required to find the effect of OAT1 inhibitors in the blood drug concentrations. Some of the limitations of our research are as follows: molecular docking simulations depend on the initial protein structure. However, the OAT1 protein is highly flexible in nature and it undergoes different conformational changes in the protein. Therefore, the effect of ligands binding based on the protein conformational changes should be done by using molecular dynamics simulations. In addition, further confirmation of ligands binding to the protein should be done by using SPR experiments. In the current investigation, we have performed molecular docking simulations to find potential inhibitors against OAT1 protein. Inhibiting the protein can be used to control the drug concentration in the body. 5000 chemical compounds (obtained from Zinc20 database) were docked on OAT1 out of which five promising ligands were selected based on the binding energy (docking score). These ligands were further analyzed for their pharmaceutical properties like blood brain barrier penetration, gastrointestinal absorption, and drug likeliness. The work will lay foundation for future experimental validations and offers a novel approach to elevate drug efficacy and safety by controlling drug concentration in blood. approximately 5000 compounds were selected for virtual screening. The following are the Zinc Database . Tranches settings that were used to download the ligands files: only 3D models were selected; in the reactivity section, "Standard" and "Exclusive" were chosen; in purchasability, "In-Stock" and "Exclusive" options were selected; Reference (R) pH was chosen; Charge: "0"; and "Lead-Like" compounds were selected. In the next step, ligands were selected based on LogP values. Since, chemical compounds with LogP=2 have good oral and intestinal absorption only these compounds were selected for virtual screening. Finally, the compounds were downloaded in pdbqt file format. The binding poses of ligands to the ZnT opening were explored using AutoDock Vina 1.5.6 software. For each protocol, 10 poses were generated for all four protein-substrate complexes. Autodock scoring method was used to select the top five candidates that bind strongly to the protein. The scoring criteria is that the ligand should fit in the binding site and form strong interactions with the protein. Protein Ligand Interaction Profiler (PLIP) web server was used to find the protein-ligand interactions. The ChimeraX and PyMol software were utilized to visualize and analyze the protein-ligand complexes.