计算机科学与系统生物学杂志

Developing a Data-driven Framework for Predicting Drug-Target Interactions Using Network Analysis and Machine Learning Techniques

Abstract

Carole Antonio*

Drug discovery is a time-consuming and expensive process that relies on identifying compounds that interact with target proteins. In recent years, the use of network analysis and machine learning techniques has shown great promise in predicting drug-target interactions. In this paper, we present a data-driven framework for predicting drug-target interactions using network analysis and machine learning techniques. Our framework involves the construction of a drug-target interaction network and the use of various network analysis techniques to identify topological features that are indicative of drug-target interactions. We also use machine learning techniques to train a predictive model that can accurately predict drugtarget interactions. Our framework was evaluated on several benchmark datasets and demonstrated superior performance compared to existing state-of-the-art methods. We believe that our framework has the potential to significantly accelerate the drug discovery process.