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ORCIDSimon V. Mathis
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2020 – today
- 2024
[c3]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:
Evaluating Representation Learning on the Protein Structure Universe. ICLR 2024- [c2]Urszula Julia Komorowska, Simon V. Mathis, Kieran Didi, Francisco Vargas, Pietro Lio, Mateja Jamnik:
Dynamics-Informed Protein Design with Structure Conditioning. ICLR 2024 - [i10]Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio:
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. CoRR abs/2406.01781 (2024) - [i9]Paulina Kulyte, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò:
Improving Antibody Design with Force-Guided Sampling in Diffusion Models. CoRR abs/2406.05832 (2024) - [i8]Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb, Charles Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Liò:
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. CoRR abs/2406.13839 (2024) - [i7]Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom L. Blundell:
Evaluating representation learning on the protein structure universe. CoRR abs/2406.13864 (2024) - 2023
- [c1]Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio:
On the Expressive Power of Geometric Graph Neural Networks. ICML 2023: 15330-15355 - [i6]Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò:
On the Expressive Power of Geometric Graph Neural Networks. CoRR abs/2301.09308 (2023) - [i5]Chaitanya K. Joshi, Arian R. Jamasb, Ramón Viñas, Charles Harris, Simon V. Mathis, Pietro Liò:
Multi-State RNA Design with Geometric Multi-Graph Neural Networks. CoRR abs/2305.14749 (2023) - [i4]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, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, 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:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i3]Divya Nori, Simon V. Mathis, Amir Shanehsazzadeh:
Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation. CoRR abs/2312.05273 (2023) - [i2]Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein:
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR abs/2312.07511 (2023) - [i1]Kieran Didi, Francisco Vargas, Simon V. Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio:
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. CoRR abs/2312.09236 (2023)
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