Kieran Didi

According to our database, Kieran Didi authored at least 16 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, July, 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, April, 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, February, 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

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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, Bruno E. Correia

Nat. Comput. Sci., December, 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, Grzegorz M. Popowicz

Nat. Comput. Sci., May, 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

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, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Saee Paliwal, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C. Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Küçükbenli, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B. Costa

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

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2024

DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Riccardo Barbano, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lió

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Dynamics-Informed Protein Design with Structure Conditioning.

Urszula Julia Komorowska, Simon V. Mathis, Kieran Didi, Francisco Vargas, Pietro Lio, Mateja Jamnik

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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, Jianlin Cheng, Pietro Lio, Tom L. Blundell

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design.

Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2023

2022

On How AI Needs to Change to Advance the Science of Drug Discovery.

Kieran Didi, Matej Zecevic

CoRR, 2022