Fergus Imrie
Associate Professor of Statistics, University of Oxford
Hugh Price Fellow, Jesus College, Oxford
fergus.imrie [AT] stats.ox.ac.uk
About me
I am an Associate Professor of Statistics in the Department of Statistics at the University of Oxford and a Hugh Price Fellow at Jesus College, Oxford. My research develops machine learning methods for drug discovery and healthcare, with a particular focus on structure-based drug discovery, experimental design and decision-making, and learning from small, noisy, or biased datasets. I am interested in methods that can support scientific decisions when data are limited, measurements are uncertain, and downstream experiments are expensive.
I am one of the AI Leads for OpenBind, an open-science consortium generating large-scale open datasets to support structure-based AI for drug discovery.
I was previously a Florence Nightingale Bicentenary Fellow in the Department of Statistics at the University of Oxford from 2024 to 2026. From 2021-2024, I was a postdoctoral scholar at the University of California, Los Angeles (UCLA) in the Department of Electrical and Computer Engineering, working with Mihaela van der Schaar. Before that, I completed my DPhil (PhD) at the University of Oxford in the Department of Statistics in the Oxford Protein Informatics Group (OPIG), supervised by Charlotte Deane. My studentship was supported by an iCASE award with Exscientia, supervised by Anthony Bradley. My thesis explored deep learning approaches for pre-clinical drug discovery, with an emphasis on generative molecular design.
Working with me
If you're a current DPhil student at Oxford and would like to do a rotation project with me, please get in touch. More generally, if you'd like to collaborate, please reach out.
Recent News
- [2026/08] Our new preprint describing OpenBind's first public structure–affinity dataset and benchmark for structure-based AI is now online. [Preprint]
- [2026/08] OpenBind released OpenBind-0, its first fully open-source molecular co-folding model. [Blog] [Model]
- [2026/08] Our reinforcement learning approach for training diffusion-based docking models to generate physically valid poses was published in Chemical Science. [Paper]
- [2026/07] We have released an open-source package, NISPO, for IUPAC name generation from SMILES strings. [Preprint]
- [2026/05] OpenBind released its first public dataset, containing 925 crystallographic binding events from 699 compounds and affinity measurements for 601 compounds. [Blog]
Show more Show less
- [2026/05] I have been appointed as an Associate Professor of Statistics in the Department of Statistics at the University of Oxford and a Hugh Price Fellow at Jesus College, Oxford. [Department Webpage] [College Webpage]
- [2026/05] New preprint on molecular representations for large language models now online. [Preprint]
- [2025/12] ChemIQ, our benchmark to assess the chemical intelligence of large language models, was published in the Journal of Chemical Information and Modeling. [Paper]
- [2025/09] Our paper using agentic large language models as novel interfaces for digital health tools was accepted in Frontiers in Artificial Intelligence. [Journal]
- [2025/06] The OpenBind Consortium received an £8m grant from the UK Sovereign AI Unit (DSIT) to power the next era of AI/ML for drug discovery. [Link]
- [2025/04] MolSnapper, a method for conditioning diffusion models for structure-based drug design, was accepted by the Journal of Chemical Information and Modeling. [Paper]
- [2025/01] AutoPrognosis-Multimodal, our multimodal AutoML framework integrating imaging and tabular data, was accepted by IEEE Journal of Biomedical and Health Informatics. [Paper]
Publications
Selected publications are highlighted below. A fuller and usually more current list is available on Google Scholar.
‡ indicates equal contribution.
The first OpenBind release: An open experimental structure–affinity dataset and benchmark for structure-based AI
Jochem Nelen, Omeir Khan, Etowah Adams, Jasmin C. Aschenbrenner, Warren Thompson, Ali Ebrahim, Eda Çapkin, Cédric Vallée, OpenBind, Elisabeth J. Shotton, Ed J. Griffen, John D. Chodera, Charlotte M. Deane, Frank von Delft, Mohammed AlQuraishi, Fergus Imrie
bioRxiv (preprint). 2026.
Teaching Diffusion Models Physics: Reinforcement Learning for Physically Valid Diffusion-Based Docking
J. Henry Broster, Bojana Popovic, Diana Kondinskaia, Charlotte M. Deane, Fergus Imrie
Chemical Science. 2026.
Assessing the Chemical Intelligence of Large Language Models
Nicholas T. Runcie, Charlotte M. Deane, Fergus Imrie
Journal of Chemical Information and Modeling. 2025.
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie‡, Stefan Denner‡, Lucas S Brunschwig, Klaus Maier-Hein, Mihaela van der Schaar
IEEE Journal of Biomedical and Health Informatics. 2025.
Deep generative design with 3D pharmacophoric constraints
Fergus Imrie, Thomas E Hadfield, Anthony R Bradley, Charlotte M Deane
Chemical Science. 2021.
Generating property-matched decoy molecules using deep learning
Fergus Imrie, Anthony R Bradley, Charlotte M Deane
Bioinformatics. 2021.
Deep Generative Models for 3D Linker Design
Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane
Journal of Chemical Information and Modeling. 2020.
The first OpenBind release: An open experimental structure–affinity dataset and benchmark for structure-based AI
Jochem Nelen, Omeir Khan, Etowah Adams, Jasmin C. Aschenbrenner, Warren Thompson, Ali Ebrahim, Eda Çapkin, Cédric Vallée, OpenBind, Elisabeth J. Shotton, Ed J. Griffen, John D. Chodera, Charlotte M. Deane, Frank von Delft, Mohammed AlQuraishi, Fergus Imrie
bioRxiv (preprint). 2026.
NISPO: Open-source IUPAC name generation tool
Nicholas T. Runcie, Fergus Imrie, Charlotte M. Deane
arXiv (preprint). 2026.
Molecular Representations for Large Language Models
Nicholas T. Runcie, Fergus Imrie, Charlotte M. Deane
arXiv (preprint). 2026.
Teaching Diffusion Models Physics: Reinforcement Learning for Physically Valid Diffusion-Based Docking
J. Henry Broster, Bojana Popovic, Diana Kondinskaia, Charlotte M. Deane, Fergus Imrie
Chemical Science. 2026.
Assessing the Chemical Intelligence of Large Language Models
Nicholas T. Runcie, Charlotte M. Deane, Fergus Imrie
Journal of Chemical Information and Modeling. 2025.
Redefining Digital Health Interfaces with Large Language Models
Fergus Imrie, Paulius Rauba, Mihaela van der Schaar
Frontiers in Artificial Intelligence. 2025.
Stochastic Encodings for Active Feature Acquisition
Alexander Norcliffe, Changhee Lee, Fergus Imrie, Mihaela van der Schaar, Pietro Lio
International Conference on Machine Learning (ICML). 2025.
MolSnapper: Conditioning Diffusion for Structure-Based Drug Design
Yael Ziv, Fergus Imrie, Brian Marsden, Charlotte M. Deane
Journal of Chemical Information and Modeling. 2025.
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie‡, Stefan Denner‡, Lucas S Brunschwig, Klaus Maier-Hein, Mihaela van der Schaar
IEEE Journal of Biomedical and Health Informatics. 2025.
You Can't Handle The (Dirty) Truth: Data-Centric Insights Improve Pseudo-Labeling
Nabeel Seedat‡, Nicolas Huynh‡, Fergus Imrie, Mihaela van der Schaar
Journal of Data-Centric Machine Learning Research. 2024.
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2024.
A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2024.
Machine Learning with Requirements: A Manifesto
Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz
Neurosymbolic Artificial Intelligence. 2024.
Navigating Data-Centric Artificial Intelligence with DC-Check: Advances, Challenges, and Opportunities
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
IEEE Transactions on Artificial Intelligence. 2023.
Can you rely on your model evaluation? Improving model evaluation with synthetic test data
Nabeel Seedat‡, Boris van Breugel‡, Fergus Imrie, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2023.
Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study
Thomas Callender, Fergus Imrie, Bogdan Cebere, Nora Pashayan, Neal Navani, Mihaela van der Schaar, Sam M Janes
PLOS Medicine. 2023.
Novel Preoperative Risk Stratification using Digital Phenotyping Applying a Scalable Machine Learning Approach
Pascal Laferriere-Lanlgois, Fergus Imrie, Marc-Andre Geraldo, Theodora Wingert, Nadia Lahrichi, Mihaela van der Schaar, Maxime Cannesson
Anesthesia & Analgesia. 2023.
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare
Fergus Imrie, Robert Davis, Mihaela van der Schaar
Nature Machine Intelligence. 2023.
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
Fergus Imrie, Bogdan Cebere, Eoin F McKinney, Mihaela van der Schaar
PLOS Digital Health. 2023.
Differentiable and Transportable Structure Learning
Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
International Conference on Machine Learning (ICML). 2023.
Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning
Maranga Mokaya, Fergus Imrie, Willem P van Hoorn, Aleksandra Kalisz, Anthony R Bradley, Charlotte M Deane
Nature Machine Intelligence. 2023.
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
Alan Jeffares‡, Tennison Liu‡, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2023.
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
Nabeel Seedat‡, Alan Jeffares‡, Fergus Imrie, Mihaela van der Schaar
International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar
International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
Alexander Norcliffe‡, Bogdan Cebere‡, Fergus Imrie, Pietro Lio, Mihaela van der Schaar
International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.
Composite Feature Selection using Deep Ensembles
Fergus Imrie‡, Alexander Norcliffe‡, Pietro Lio, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2022.
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat‡, Fergus Imrie‡, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar
International Conference on Machine Learning (ICML). 2022.
Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration
Thomas E Hadfield, Fergus Imrie, Andy Merritt, Kristian Birchall, Charlotte M Deane
Journal of Chemical Information and Modeling. 2022.
Self-Supervision Enhanced Feature Selection with Correlated Gates
Changhee Lee‡, Fergus Imrie‡, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2022. [Spotlight]
Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2021. [Spotlight]
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Yuchao Qin‡, Fergus Imrie‡, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2021.
Deep generative design with 3D pharmacophoric constraints
Fergus Imrie, Thomas E Hadfield, Anthony R Bradley, Charlotte M Deane
Chemical Science. 2021.
Generating property-matched decoy molecules using deep learning
Fergus Imrie, Anthony R Bradley, Charlotte M Deane
Bioinformatics. 2021.
Virtual Screening with Convolutional Neural Networks
Fergus Imrie, Anthony R Bradley, Charlotte M Deane
Artificial Intelligence in Drug Discovery (Book Chapter). 2020.
Deep Generative Models for 3D Linker Design
Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane
Journal of Chemical Information and Modeling. 2020.
Protein Family-Specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data
Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane
Journal of Chemical Information and Modeling. 2018.
The first OpenBind release: An open experimental structure–affinity dataset and benchmark for structure-based AI
Jochem Nelen, Omeir Khan, Etowah Adams, Jasmin C. Aschenbrenner, Warren Thompson, Ali Ebrahim, Eda Çapkin, Cédric Vallée, OpenBind, Elisabeth J. Shotton, Ed J. Griffen, John D. Chodera, Charlotte M. Deane, Frank von Delft, Mohammed AlQuraishi, Fergus Imrie
bioRxiv (preprint). 2026.
NISPO: Open-source IUPAC name generation tool
Nicholas T. Runcie, Fergus Imrie, Charlotte M. Deane
arXiv (preprint). 2026.
Molecular Representations for Large Language Models
Nicholas T. Runcie, Fergus Imrie, Charlotte M. Deane
arXiv (preprint). 2026.
Teaching Diffusion Models Physics: Reinforcement Learning for Physically Valid Diffusion-Based Docking
J. Henry Broster, Bojana Popovic, Diana Kondinskaia, Charlotte M. Deane, Fergus Imrie
Chemical Science. 2026.
Assessing the Chemical Intelligence of Large Language Models
Nicholas T. Runcie, Charlotte M. Deane, Fergus Imrie
Journal of Chemical Information and Modeling. 2025.
Redefining Digital Health Interfaces with Large Language Models
Fergus Imrie, Paulius Rauba, Mihaela van der Schaar
Frontiers in Artificial Intelligence. 2025.
MolSnapper: Conditioning Diffusion for Structure-Based Drug Design
Yael Ziv, Fergus Imrie, Brian Marsden, Charlotte M. Deane
Journal of Chemical Information and Modeling. 2025.
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie‡, Stefan Denner‡, Lucas S Brunschwig, Klaus Maier-Hein, Mihaela van der Schaar
IEEE Journal of Biomedical and Health Informatics. 2025.
You Can't Handle The (Dirty) Truth: Data-Centric Insights Improve Pseudo-Labeling
Nabeel Seedat‡, Nicolas Huynh‡, Fergus Imrie, Mihaela van der Schaar
Journal of Data-Centric Machine Learning Research. 2024.
Machine Learning with Requirements: A Manifesto
Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz
Neurosymbolic Artificial Intelligence. 2024.
Navigating Data-Centric Artificial Intelligence with DC-Check: Advances, Challenges, and Opportunities
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
IEEE Transactions on Artificial Intelligence. 2023.
Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study
Thomas Callender, Fergus Imrie, Bogdan Cebere, Nora Pashayan, Neal Navani, Mihaela van der Schaar, Sam M Janes
PLOS Medicine. 2023.
Novel Preoperative Risk Stratification using Digital Phenotyping Applying a Scalable Machine Learning Approach
Pascal Laferriere-Lanlgois, Fergus Imrie, Marc-Andre Geraldo, Theodora Wingert, Nadia Lahrichi, Mihaela van der Schaar, Maxime Cannesson
Anesthesia & Analgesia. 2023.
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare
Fergus Imrie, Robert Davis, Mihaela van der Schaar
Nature Machine Intelligence. 2023.
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
Fergus Imrie, Bogdan Cebere, Eoin F McKinney, Mihaela van der Schaar
PLOS Digital Health. 2023.
Testing the Limits of SMILES-based De Novo Molecular Generation with Curriculum and Deep Reinforcement Learning
Maranga Mokaya, Fergus Imrie, Willem P van Hoorn, Aleksandra Kalisz, Anthony R Bradley, Charlotte M Deane
Nature Machine Intelligence. 2023.
Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration
Thomas E Hadfield, Fergus Imrie, Andy Merritt, Kristian Birchall, Charlotte M Deane
Journal of Chemical Information and Modeling. 2022.
Deep generative design with 3D pharmacophoric constraints
Fergus Imrie, Thomas E Hadfield, Anthony R Bradley, Charlotte M Deane
Chemical Science. 2021.
Generating property-matched decoy molecules using deep learning
Fergus Imrie, Anthony R Bradley, Charlotte M Deane
Bioinformatics. 2021.
Deep Generative Models for 3D Linker Design
Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane
Journal of Chemical Information and Modeling. 2020.
Protein Family-Specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data
Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, Charlotte M Deane
Journal of Chemical Information and Modeling. 2018.
Stochastic Encodings for Active Feature Acquisition
Alexander Norcliffe, Changhee Lee, Fergus Imrie, Mihaela van der Schaar, Pietro Lio
International Conference on Machine Learning (ICML). 2025.
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2024.
A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2024.
Can you rely on your model evaluation? Improving model evaluation with synthetic test data
Nabeel Seedat‡, Boris van Breugel‡, Fergus Imrie, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2023.
Differentiable and Transportable Structure Learning
Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
International Conference on Machine Learning (ICML). 2023.
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
Alan Jeffares‡, Tennison Liu‡, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2023.
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
Nabeel Seedat‡, Alan Jeffares‡, Fergus Imrie, Mihaela van der Schaar
International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar
International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
Alexander Norcliffe‡, Bogdan Cebere‡, Fergus Imrie, Pietro Lio, Mihaela van der Schaar
International Conference on Artificial Intelligence and Statistics (AISTATS). 2023.
Composite Feature Selection using Deep Ensembles
Fergus Imrie‡, Alexander Norcliffe‡, Pietro Lio, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2022.
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat‡, Fergus Imrie‡, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar
International Conference on Machine Learning (ICML). 2022.
Self-Supervision Enhanced Feature Selection with Correlated Gates
Changhee Lee‡, Fergus Imrie‡, Mihaela van der Schaar
International Conference on Learning Representations (ICLR). 2022. [Spotlight]
Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2021. [Spotlight]
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Yuchao Qin‡, Fergus Imrie‡, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar
Neural Information Processing Systems (NeurIPS). 2021.