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Mutations in PPIs can disrupt protein binding affinity and lead to functional changes and disease. Computational methods, including physics-based and machine learning-based approaches, have been developed to address this challenge. We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning to predict mutation-induced binding free energy changes.
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