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

Lead Research Organisation: University of Oxford


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. Precisely
modelling 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

Planned Impact

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024050/1 30/09/2019 30/03/2028
2721780 Studentship EP/S024050/1 30/09/2022 29/09/2026 Lars Holdijk