Nath Abhigyan, Kumari Priyanka, Chaube Radha
Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Department of Biotechnology, Delhi Technological University, Delhi, India.
Methods Mol Biol. 2018;1762:21-30. doi: 10.1007/978-1-4939-7756-7_2.
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.
识别药物靶点和药物靶点相互作用是药物研发流程中的重要步骤。成功的计算预测方法可以降低实验方法所需的成本和时间。了解潜在的药物靶点及其相互作用对于药物重新利用非常有用。监督式机器学习方法在药物靶点预测和药物靶点相互作用预测方面非常有用。在这里，我们描述了使用监督学习技术开发用于人类药物靶点预测及其相互作用的预测模型的详细信息。
