Welcome to the research papers section of the “Awesome GNN-Based Drug Discovery” repository. Here, we’ve meticulously curated a collection of influential and groundbreaking research papers that leverage Graph Neural Networks (GNNs) in various aspects of drug discovery. Our goal is to provide a comprehensive resource that not only showcases the latest advancements in the field but also serves as an educational platform for those interested in the intersection of GNNs and pharmaceutical research.
The research papers are categorized into four primary domains, each with its dedicated markdown file:
This section includes papers focusing on the prediction of interactions between different drugs using GNNs. Understanding these interactions is crucial for assessing the safety and efficacy of drug combinations.
Here, we explore studies that use GNNs to predict interactions between drugs and their target molecules within the body. These interactions are fundamental for understanding the mechanism of action of drugs and for the development of new therapeutics.
De novo drug design is a critical area of research where GNNs are used to generate novel molecular structures with potential therapeutic effects. This section compiles papers that demonstrate how GNNs can innovate in creating new drug candidates.
This collection encompasses research on using GNNs to predict various molecular properties. Accurate prediction of these properties is essential for understanding compound behavior and guiding the drug development process.
Each markdown file includes a list of papers with links to the original publications and a brief citation. We encourage you to delve into these resources to gain insights into how GNNs are revolutionizing drug discovery.