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 (J. Chem. Inf. Model., 2018)

Deep Generative Models for 3D Linker Design. - Fergus Imrie; Anthony R. Bradley; Mihaela van der Schaar; Charlotte M. Deane (J. Chem. Inf. Model., 2020)

Explaining Latent Representations with a Corpus of Examples. - Jonathan Crabbé; Zhaozhi Qian; Fergus Imrie; Mihaela van der Schaar (NeurIPS, 2021)

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 (NeurIPS, 2021)

Generating property-matched decoy molecules using deep learning. - Fergus Imrie; Anthony R. Bradley; Charlotte M. Deane (Bioinform., 2021)

Self-Supervision Enhanced Feature Selection with Correlated Gates. - Changhee Lee; Fergus Imrie; Mihaela van der Schaar (ICLR, 2022)

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. - Nabeel Seedat; Fergus Imrie; Alexis Bellot; Zhaozhi Qian; Mihaela van der Schaar (ICML, 2022)

Composite Feature Selection Using Deep Ensembles. - Fergus Imrie; Alexander Norcliffe; Pietro Lió; Mihaela van der Schaar (NeurIPS, 2022)

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 (CoRR, 2022)

DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems. - Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar (CoRR, 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 (J. Chem. Inf. Model., 2022)

To Impute or not to Impute? Missing Data in Treatment Effect Estimation. - Jeroen Berrevoets; Fergus Imrie; Trent Kyono; James Jordon; Mihaela van der Schaar (AISTATS, 2023)

SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. - Alexander Norcliffe; Bogdan Cebere; Fergus Imrie; Pietro Liò; Mihaela van der Schaar (AISTATS, 2023)

Improving Adaptive Conformal Prediction Using Self-Supervised Learning. - Nabeel Seedat; Alan Jeffares; Fergus Imrie; Mihaela van der Schaar (AISTATS, 2023)

TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. - Alan Jeffares; Tennison Liu; Jonathan Crabbé; Fergus Imrie; Mihaela van der Schaar (ICLR, 2023)

Differentiable and Transportable Structure Learning. - Jeroen Berrevoets; Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar (ICML, 2023)

Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. - Boris van Breugel; Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar (NeurIPS, 2023)

Machine Learning with Requirements: a Manifesto. - Eleonora Giunchiglia; Fergus Imrie; Mihaela van der Schaar; Thomas Lukasiewicz (CoRR, 2023)

Redefining Digital Health Interfaces with Large Language Models. - Fergus Imrie; Paulius Rauba; Mihaela van der Schaar (CoRR, 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 (Nat. Mac. Intell., 2023)

Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare. - Fergus Imrie; Robert Davis; Mihaela van der Schaar (Nat. Mac. Intell., 2023)

A Neural Framework for Generalized Causal Sensitivity Analysis. - Dennis Frauen; Fergus Imrie; Alicia Curth; Valentyn Melnychuk; Stefan Feuerriegel; Mihaela van der Schaar (ICLR, 2024)

Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI. - Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar (ICLR, 2024)

You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling. - Nabeel Seedat; Nicolas Huynh; Fergus Imrie; Mihaela van der Schaar (CoRR, 2024)

Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities. - Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar (IEEE Trans. Artif. Intell., 2024)

MolSnapper: Conditioning Diffusion for Structure-Based Drug Design. - Yael Ziv; Fergus Imrie; Brian D. Marsden; Charlotte M. Deane (J. Chem. Inf. Model., 2025)

Assessing the Chemical Intelligence of Large Language Models. - Nicholas T. Runcie; Charlotte M. Deane; Fergus Imrie (CoRR, 2025)

Automated Ensemble Multimodal Machine Learning for Healthcare. - Fergus Imrie; Stefan Denner; Lucas S. Brunschwig; Klaus H. Maier-Hein; Mihaela van der Schaar (IEEE J. Biomed. Health Informatics, 2025)

Stochastic Encodings for Active Feature Acquisition. - Alexander Norcliffe; Changhee Lee; Fergus Imrie; Mihaela van der Schaar; Pietro Lio (CoRR, 2025)