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Understanding Drug Synergy and Its Role in Predicting Effective Therapies
Drug synergy is a concept that has revolutionized the way we approach combination therapies in modern medicine. By combining two or more drugs, researchers aim to enhance the overall therapeutic outcome in ways that surpass what each drug can achieve individually. In this blog, we will dive into the fundamentals of drug synergy, explore the metrics used to quantify drug interactions, and examine several models used in drug synergy prediction, including Loewe Additivity, Bliss Independence, and the ZIP model.
Applications of Graph Neural Networks (GNNs) in Molecular Structure and Drug Discovery
Graph Neural Networks (GNNs) have emerged as a powerful machine learning framework that extends the capabilities of traditional neural networks to operate on graph-structured data. By leveraging the connectivity and relational information within graphs, GNNs have found applications in a wide range of domains, including social networks, biological data analysis, recommendation systems, and drug discovery.
Introduction to Graph Theory and Graph Neural Networks (GNNs)
Graphs are powerful tools for representing and analysing complex relationships in data. By converting real-world data into graphs, we can uncover patterns and insights that are often hidden in raw datasets. In this blog, we’ll explore the basics of graph theory, its applications in various domains, the common formats and datasets used in graph analysis, and how graphs are utilized in machine learning, particularly with Graph Neural Networks (GNNs).