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You can still enjoy the precious moments with your family even with kidney disease. All you have to do is just to know your kidney well. Chronic kidney disease describes the gradual loss of kidney function. It means your kidneys are damaged and can't filter blood the way they should. Chronic Kidney Disease (CKD) is caused by a wide variety of pathologic processes, including diabetes, hypertension, and autoimmune diseases.The stages of CKD, from stage 1 to stage 5, reflect progressive loss of function as quantified by the glomerular filter. If you have kidney disease it can lead to kidney failure. If your kidneys stop working you'll need dialysis or a kidney transplant to stay alive but the good news is there are actions and medicines you can take to keep your kidneys healthy. As a Chief Scientist in PrimaPharma, it is essential to develop a novel drug specific for the Chronic Kidney Disease. The Disabled Homolog 2 (DAB2) is a protein found that could be a target for chronic kidney disease treatment after researchers’ investigation. Experiments using mouse models confirmed that the reduction of DAB2 meant the mice were protected from CKD. Lowering the levels of DAB2 led to the cytokine TGF-β pathway not inducing fibrosis as a healing reaction. With the aid of computer, there are several steps involve in the novel drug design. First will be the Target Identification and Target Validation. The macromolecular structure can often be obtained from the protein database. Structures determined with X-ray diffraction data are most commonly used for drug design, although solution structures determined with NMR methods and homology models can also be effective. For example, homology modeling is one of the best and reliable approaches because it predicts the 3D structure of a target protein on the basis of the knowledge about the structure of homologous proteins with >40% similarity. Once the 3D structure of the target is predicted, it is necessary to validate the model by checking the stereochemical properties in a Ramachandran plot. It shows the possible conformations of ψ and φ angles for all amino acid residues present in the protein structure. Identify the binding pocket. After the structure of the target protein is resolved, the next step is to identify the binding pocket. This is a small cavity where ligands bind to the target to produce the desired effect. There are a few methods capable of spotting the potential binding residues. These methods consider the knowledge about interaction energy and van der Waal forces for binding site mapping. For example, Q-SiteFinder is an energy-based method commonly used for binding site prediction. This method calculates van der Waals interaction energies of proteins with a methyl probe. Those with favorable energies are retained and clustered. These probe clusters are ranked based on their total interaction energies. Pharmacophore modelling. Pharmacophore modeling is most often applied to virtual screening in order to identify molecules triggering the desired biological effect. For this purpose, we will create a pharmacophore model that most likely encodes the correct 3D organization of the required interaction pattern. In this case, structural information for the protein receptor, but no active ligands, is known. A putative pharmacophore model can be constructed by analyzing the chemical properties of the binding site of interest. There are several different computational approaches that can directly convert 3D atomic structures of protein binding sites into queries. The interaction maps of the de novo drug design tool LUDI can be used to create a pharmacophore query. HS-Pharm is a knowledge-based method that uses machine-learning algorithms to prioritize the most interesting interacting atoms and to generate an interaction map within the binding site. Subsequently, the interaction map is converted into pharmacophore features. The GRID package is another approach to analyze the pocket in order to identify the key interactions. Using molecular interaction fields, the most favorable positions of atomic probes in the binding site can be identified and converted into pharmacophore features. Ligandscout is a computer software that will be used in this case. LIGANDSCOUT (LS) is a tool that allows the automatic construction and visualization of 3D pharmacophore for structural data of macromolecule complexes. For model generation,all possible interactions between the ligand and the protein are considered. Chemical features include hydrogen bond donors and acceptors as directed vectors, and positive and negative ionizable regions as well as lipophilic areas are represented by spheres. Moreover, to increase the selectivity, the LS model includes spatial information regarding areas inaccessible to any potential ligand, thus reflecting possible steric restrictions Pharmacophore validation. Validation is necessary to get the authentic pharmacophore analysis as well as to evaluate the quality of the molecular model. Structure-based pharmacophore model generated in this study was validated before database screening to evaluate whether or not our models are capable to distinguish the active compounds from decoy set.There are 2 validation methods were used in this step. For test set, the testing set included known inhibitors with experimental activity and inactive molecules. While for the decoy set include actives known antagonists with correspondence decoy compound obtained from the enhanced Database of Useful Decoys. We will validate our pharmacophore models with decoy test.The active test set with inhibitor constant IC50 values were merged with the decoy compounds and an initial screening was run to validate to model. The performance of a classification model like the AUC value and EF value of the compounds will be estimated from the receiver operating characteristic curve (ROC). A model with higher AUC value should have better predictability. The AUC value is ranging between 0 and 1,so the model whose prediction rate is 100% correct has an AUC value 1. Lead Optimization. After validating the pharmacophore, we continue with pharmacophore based-virtual screening. ZINC is a freely available chemical database, which is being utilized to identify the potential lead compounds. Compounds from the database can be searched depending on the structure, name of the compound, or using the chemical smile ID. The previously obtained pharmacophore model generated for each active compound was submitted to ZINC Pharmer. Initially, it searches hits from the ZINC database of “ZINC natural products and ZINC natural derivatives” consist millions of Drug-like, Natural Products and FDA approved drugs. A maximum of 0.5 Å RMSD from sphere centers were used as input parameters for ZINC Pharmer The database of hit compounds from the ZINC Pharmer were saved and downloaded for further screening.In the case of the desired compound, it has given priority, the compound having the most similar features matches the required pharmacophore features and can easily interact with our target protein. It has been chosen the possible hit compounds whose maximum features were matched to query pharmacophore. Fitted hit compounds were arranged based on the pharmacophore fit score and subject to further validation. Structure based molecular docking. The selected hits compounds obtained by pharmacophore screening were subject to molecular docking. Mimics the binding of a ligand to a protein by using PyRx virtual screening software. Resultant docked compound with better binding affinity were retrieved and visualized by using BIOVA Discovery Studio Visualizer Tool 16.1.0.