On How AI Needs to Change to Advance the Science of Drug Discovery. - Kieran Didi; Matej Zecevic (CoRR, 2022)

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. - Kieran Didi; Francisco Vargas 001; Simon V. Mathis; Vincent Dutordoir; Emile Mathieu; Urszula Julia Komorowska; Pietro Lio (CoRR, 2023)

Evaluating Representation Learning on the Protein Structure Universe. - Arian Rokkum Jamasb; Alex Morehead; Chaitanya K. Joshi; Zuobai Zhang; Kieran Didi; Simon V. Mathis; Charles Harris; Jian Tang 005; Jianlin Cheng; Pietro Lio; Tom L. Blundell (ICLR, 2024)

Dynamics-Informed Protein Design with Structure Conditioning. - Urszula Julia Komorowska; Simon V. Mathis; Kieran Didi; Francisco Vargas 001; Pietro Lio; Mateja Jamnik (ICLR, 2024)

DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Riccardo Barbano; Vincent Dutordoir; Emile Mathieu; Urszula Julia Komorowska; Pietro Lió (NeurIPS, 2024)

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Vincent Dutordoir; Riccardo Barbano; Emile Mathieu; Urszula Julia Komorowska; Pietro Lio (CoRR, 2024)

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. - Rishabh Anand; Chaitanya K. Joshi; Alex Morehead; Arian R. Jamasb; Charles Harris; Simon V. Mathis; Kieran Didi; Bryan Hooi; Pietro Liò (CoRR, 2024)

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery. - Peter St. John; Dejun Lin; Polina Binder; Malcolm Greaves; Vega Shah; John St. John; Adrian Lange; Patrick D. Hsu; Rajesh Illango; Arvind Ramanathan; Anima Anandkumar; David H. Brookes; et al. (CoRR, 2024)

Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV. - Aubin Ramon; Montader Ali; Misha Atkinson; Alessio Saturnino; Kieran Didi; Cristina Visentin; Stefano Ricagno; Xing Xu; Matthew Greenig; Pietro Sormanni (Nat. Mac. Intell., 2024)

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery. - Till Siebenmorgen; Filipe Menezes; Sabrina Benassou; Erinc Merdivan; Kieran Didi; André Santos Dias Mourão; Radoslaw Kitel; Pietro Liò; Stefan Kesselheim; Marie Piraud; Fabian J. Theis; Michael Sattler; et al. (Nat. Comput. Sci., 2024)

Structure-based drug design with equivariant diffusion models. - Arne Schneuing; Charles Harris; Yuanqi Du; Kieran Didi; Arian Rokkum Jamasb; Ilia Igashov; Weitao Du; Carla P. Gomes; Tom L. Blundell; Pietro Lio; Max Welling; Michael M. Bronstein; et al. (Nat. Comput. Sci., 2024)

Proteina: Scaling Flow-based Protein Structure Generative Models. - Tomas Geffner; Kieran Didi; Zuobai Zhang; Danny Reidenbach; Zhonglin Cao; Jason Yim; Mario Geiger; Christian Dallago; Emine Küçükbenli; Arash Vahdat; Karsten Kreis (ICLR, 2025)

RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. - Rishabh Anand; Chaitanya K. Joshi; Alex Morehead; Arian Rokkum Jamasb; Charles Harris; Simon V. Mathis; Kieran Didi; Rex Ying; Bryan Hooi; Pietro Lio (Trans. Mach. Learn. Res., 2025)

MotifBench: A standardized protein design benchmark for motif-scaffolding problems. - Zhuoqi Zheng; Bo Zhang; Kieran Didi; Kevin K. Yang; Jason Yim; Joseph L. Watson; Hai-Feng Chen; Brian L. Trippe (CoRR, 2025)

Compositional Flows for 3D Molecule and Synthesis Pathway Co-design. - Tony Shen; Seonghwan Seo; Ross Irwin; Kieran Didi; Simon Olsson; Woo Youn Kim; Martin Ester (CoRR, 2025)

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching. - Tomas Geffner; Kieran Didi; Zhonglin Cao; Danny Reidenbach; Zuobai Zhang; Christian Dallago; Emine Küçükbenli; Karsten Kreis; Arash Vahdat (CoRR, 2025)