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Geometric Deep Learning for Structure-Based Drug Design

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Geometric deep learning is revolutionizing structure-based drug design (SBDD), enabling us to harness the full potential of three-dimensional protein structures for drug development. In this talk, I will present a comprehensive overview of how geometric deep learning approaches advance critical tasks in SBDD , from binding site prediction to linker design. I will examine the latest architectures that can effectively process and learn from 3D structural data and discuss their practical applications in drug discovery pipelines. Looking ahead, I will also highlight some emerging opportunities in this rapidly evolving field.

This talk is part of the Machine Learning Reading Group @ CUED series.

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