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Exploring antimalarial activity of drugs using weighted atomic vectors and artificial intelligence

Abstract:

Background & objectives: Malaria is a global health issue, causing over two million deaths annually. The development of new and potent antimalarial drugs is essential to combat the disease. Machine learning has been increasingly applied to predict antimalarial activity of compounds, offering a promising approach for antimalarial pharmaceutical research. This study aims to predict the antimalarial activity of potential compounds using weighted atomic vectors and machine learning algorithms. Methods: The research employs several machine learning algorithms, such as Decision Tree, Bagging Regressor, and Ada Boost. The study uses weighted atomic vectors to represent compounds and employs machine learning algorithms for prediction. The performance of the models is assessed using metrics like R2, MAE, and RMSLE, statistical validation using Friedman and Wilcoxon Tests. Results: The results highlight the remarkable efficacy of Ada Boost in predicting antimalarial activity, consistently outperforming other algorithms across different datasets, achieving a maximum precision of 93%. Interpretation & conclusion: The combination of weighted atomic vectors and machine learning emerges as a promising approach for antimalarial pharmaceutical research, emphasizing the significance of artificial intelligence in this field.