Igi65/3/2023 ![]() The model was developed using four different machine learning algorithms on a training set called multiple linear regression, support vector regression, random forest regression, and random tree regression. ![]() This database was curated using feature selection and extraction procedures. ![]() Descriptors representing structural and functional properties were calculated using Alvadesc v.1.02. A database of around 600 compounds with known inhibitory zone values was collected. The QSAR model for the identification of inhibitors against Staphylococcus epidermidis is developed. AbstractStaphylococcus epidermidis is a common symbiont bacteria, and these are common causes of nosocomial infections.
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