Simon V. Mathis

According to our database, Simon V. Mathis authored at least 15 papers between 2023 and 2025.

Collaborative distances:

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

2025

Flows, straight but not so fast: Exploring the design space of Rectified Flows in Protein Design.

Junhua Chen, Simon V. Mathis, Charles Harris, Kieran Didi, Pietro Lio

CoRR, October, 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

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

Found. Trends Mach. Learn., 2025

gRNAde: Geometric Deep Learning for 3D RNA inverse design.

Chaitanya K. Joshi, Arian Rokkum Jamasb, Ramón Viñas Torné, Charles Harris, Simon V. Mathis, Alex Morehead, Rishabh Anand, Pietro Lio

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Improving Antibody Design with Force-Guided Sampling in Diffusion Models.

Paulina Kulyte, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, 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

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

Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation.

Divya Nori, Simon V. Mathis, Amir Shanehsazzadeh

CoRR, 2023

Multi-State RNA Design with Geometric Multi-Graph Neural Networks.

Chaitanya K. Joshi, Arian R. Jamasb, Ramón Viñas, Charles Harris, Simon V. Mathis, Pietro Liò

CoRR, 2023

On the Expressive Power of Geometric Graph Neural Networks.

Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio

Proceedings of the International Conference on Machine Learning, 2023