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DrugLikeness prediction is a time-consuming and tedious process. An in-vitro method the drug development takes a long time to come to market. The failure rate is also another one to think about in this method. There are many in-silico methods currently available and developing to help the drug discovery and development process. Many online tools are available for predicting and classifying a drug after analyzing the drug-likeness properties of compounds. But most tools have their advantages and disadvantages. In this study, a tool is developed to predict the drug-likeness of compounds given as input to this software. This may help the chemists in analyzing a compound before actually preparing a compound for the drug discovery process. The tool includes both descriptor-based calculation and ingerprint-based calculation of the particular compounds. The descriptor-calculation also includes a set of rules and ilters like Lipinski’s rule, Ghose ilter, Veber ilter and BBB likeness. The previous studies proved that the ingerprint-based prediction is more accurate than descriptor-based prediction. So, in the current study, the drug-likeness prediction tool incorporated the molecular descriptors and ingerprint-based calculations based on ive different ingerprint types. The current study incorporated ive differentmachine learning algorithms for prediction of drug-likeness and selected the algorithm, which has a high accuracy rate. When a chemist inputs a particular compound in SMILES format, the drug-likeness prediction tool predictswhether the given candidate compound is drug or non-drug.

In Silico Prediction Tool for Drug-likeness of Compounds based on Ligand Based Screening