On the Expressive Power of Geometric Graph Neural Networks. - Chaitanya K. Joshi; Cristian Bodnar; Simon V. Mathis; Taco Cohen; Pietro Lio (ICML, 2023)

Multi-State RNA Design with Geometric Multi-Graph Neural Networks. - Chaitanya K. Joshi; Arian R. Jamasb; Ramón Viñas 001; Charles Harris; Simon V. Mathis; Pietro Liò (CoRR, 2023)

Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation. - Divya Nori; Simon V. Mathis; Amir Shanehsazzadeh (CoRR, 2023)

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. - Alexandre Duval; Simon V. Mathis; Chaitanya K. Joshi; Victor Schmidt; Santiago Miret; Fragkiskos D. Malliaros; Taco Cohen; Pietro Lio; Yoshua Bengio; Michael M. Bronstein (CoRR, 2023)

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)

Improving Antibody Design with Force-Guided Sampling in Diffusion Models. - Paulina Kulyte; Francisco Vargas 001; Simon Valentin Mathis; Yu Guang Wang 001; José Miguel Hernández-Lobato; Pietro Liò (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)

gRNAde: Geometric Deep Learning for 3D RNA inverse design. - Chaitanya K. Joshi; Arian Rokkum Jamasb; Ramón Viñas Torné 001; Charles Harris; Simon V. Mathis; Alex Morehead; Rishabh Anand; Pietro Lio (ICLR, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 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)