Machine learning and drug discovery for neglected tropical diseases

Machine learning and drug discovery for neglected tropical diseases

Blog Article

Abstract Neglected tropical diseases affect millions of individuals and cause loss of productivity worldwide.They are common in developing countries without the financial resources for research and drug development.With increased availability of data from high throughput Trivet screening, machine learning has been introduced into the drug discovery process.

Models can be trained to predict biological activities of compounds before working in the lab.In this study, we use three publicly available, high-throughput screening datasets to train machine learning models to predict biological activities related to inhibition of species that cause leishmaniasis, American trypanosomiasis (Chagas disease), and African trypanosomiasis (sleeping sickness).We compare machine learning models (tree based models, naive Bayes classifiers, and neural networks), big boys wetsuit featurizing methods (circular fingerprints, MACCS fingerprints, and RDKit descriptors), and techniques to deal with the imbalanced data (oversampling, undersampling, class weight/sample weight).