Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds
Muhammad Ishfaq, Muhammad Aamir, Farooq Ahmad, Abdelazim M. Mebed, Sayed Elshahat
Abstract
values for linear regression, random forest regression, gradient boosting regression, and bagging regression are 0.58, 0.84, 0.77, and 0.80, respectively. Using these models, it is possible to predict the activity of new molecules in a short period of time and at a reasonable cost. Furthermore, Tanimoto similarity is used for similarity analysis, as well as a chemical database is mined to search for similar molecules. Nonetheless, this study provides a framework for repurposing other effective drug molecules to prevent cancer.
Topics & Concepts
Random forestMachine learningGradient boostingMolecular descriptorRegressionArtificial intelligenceSimilarity (geometry)Computer scienceLinear regressionRegression analysisQuantitative structure–activity relationshipBoosting (machine learning)Drug discoveryData miningMathematicsBioinformaticsStatisticsBiologyImage (mathematics)Computational Drug Discovery MethodsSynthesis and biological activityEstrogen and related hormone effects