Our analysis indicated that MACCS keys (ISIS keys) 112, 122, 144, and 150 were highly prevalent in the approved drugs. Our model based on one hundred fifty nine MACCS keys predicted drug-likeness of the molecules with 89.96% accuracy along with 0.77 MCC. It was observed that the models developed using MACCS keys based fingerprints, discriminated approved and experimental drugs with higher precision. We have developed various classification models using different types of fingerprints like Estate, PubChem, Extended, FingerPrinter, MACCS keys, GraphsOnlyFP, SubstructureFP, Substructure FPCount, Klekota-RothFP, Klekota-Roth FPCount. Weka software has been used for feature selection in order to identify the best fingerprints. We have used freely available PaDEL software for computing molecular fingerprints/descriptors of the molecules for developing prediction models. In this study, we have used 1347 approved and 3206 experimental drugs for developing a knowledge-based computational model for predicting drug-likeness of a molecule.
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