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Various machine learning methods have been used in predicting bioactive molecular compounds in order to deal with the large volume of data being generated. This study investigates the Majority Voting ensemble method using different combinations of 5 commonly-used machine learning algorithms, including Support Vector Machine, Decision Tree, Na\u00efve Bayes, k-Nearest Neighbor, and Random Forest on three chemical datasets DS1, DS2, and DS3 which consist of structurally heterogeneous and homogeneous molecules and are commonly used in other studies. The results show that Majority Voting has a better performance, based on all the evaluation metrics used, compared to each of the machine learning algorithms as individual classifiers. 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