Abstract
Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.
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Partial support from UGC-CAS to RC is acknowledged.
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Nath, A., Kumari, P., Chaube, R. (2018). Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_2
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DOI: https://doi.org/10.1007/978-1-4939-7756-7_2
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