Geometric Deep Learning for Generative Modelling in Computational (Bio-)Chemistry

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Context, Novelty and Objectives: Geometric Deep Learning is a rapidly growing subfield of machine learning that focusses on developing deep learning architectures capable of using the inherent geometric structure found in data, including graphs, meshes and other topological structures, to improve model performance and sample efficiency. When coupled with recent advances in Generative Modelling, Geometric Deep Learning has demonstrated to be remarkably well suited for addressing challenges in the physical sciences, particularly in Computational (Bio-)Chemistry. A notable example of this is AlphaFolds groundbreaking advancement in protein structure prediction. However, while recent progress in applying Geometric Deep Learning for Generative Modelling in Computational (Bio-)Chemistry is impressive, an enduring challenge remains: modelling full equilibrium distributions of large molecular systems. Preciselymodelling these equilibrium distributions is a critical step in deriving quantities such as free energy differences, binding affinities, and reaction rates-key factors in drug discovery and material design. The primary objective of this project is thus to develop new algorithms and techniques based on recent advances in Geometric Deep Learning to render the modelling of equilibrium distributions feasible for large scale molecular systems, such as protein-protein complexes. In doing so, we aim to contribute tangibly to the field of computational chemistry. Concurrently, we intend to leverage existing techniques and insights from the field of computational chemistry to drive further progression both Geometric Deep Learning and Generative Modelling. EPSRC Strategies and Research Areas: The proposed project aligns with the research areas Artificial Intelligence Technologies, Chemical reaction dynamics and mechanisms and Computational and Theoretical Chemistry