Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, India.
Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, India.
Methods Mol Biol. 2024;2714:155-169. doi: 10.1007/978-1-0716-3441-7_9.
The pipeline of drug discovery consists of a number of processes; drug-target interaction determination is one of the salient steps among them. Computational prediction of drug-target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug-target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug-target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor.
药物发现的流程包括多个环节；药物-靶点相互作用的确定是其中一个重要步骤。药物-靶点相互作用的计算预测可以帮助缩小基于实验湿实验室验证步骤的搜索空间，从而大大减少药物发现流程所需的时间和其他资源。虽然基于机器学习的方法在药物-靶点相互作用预测中更为广泛，但以网络为中心的方法也在不断发展。在本章中，我们从使用机器学习算法的角度关注药物-靶点相互作用预测的过程，以及开发准确预测器所涉及的各个阶段。
