| Artificial intelligence for science in quantum, atomistic, and continuum systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423, 2023 | 119 | 2023 |
| On the expressive power of geometric graph neural networks CK Joshi, C Bodnar, SV Mathis, T Cohen, P Lio International conference on machine learning, 15330-15355, 2023 | 98 | 2023 |
| Toward scalable simulations of lattice gauge theories on quantum computers SV Mathis, G Mazzola, I Tavernelli Physical Review D 102 (9), 094501, 2020 | 76 | 2020 |
| A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems A Duval, SV Mathis, CK Joshi, V Schmidt, S Miret, FD Malliaros, T Cohen, ... arXiv preprint arXiv:2312.07511, 2023 | 59 | 2023 |
| Posecheck: Generative models for 3d structure-based drug design produce unrealistic poses C Harris, K Didi, A Jamasb, C Joshi, S Mathis, P Lio, T Blundell NeurIPS 2023 Generative AI and Biology (GenBio) Workshop, 2023 | 46* | 2023 |
| Gauge-invariant quantum circuits for (1) and Yang-Mills lattice gauge theories G Mazzola, SV Mathis, G Mazzola, I Tavernelli Physical review research 3 (4), 043209, 2021 | 38 | 2021 |
| gRNAde: Geometric Deep Learning for 3D RNA inverse design CK Joshi, AR Jamasb, R Viñas, C Harris, S Mathis, A Morehead, R Anand, ... bioRxiv, 2024 | 16* | 2024 |
| A framework for conditional diffusion modelling with applications in motif scaffolding for protein design K Didi, F Vargas, SV Mathis, V Dutordoir, E Mathieu, UJ Komorowska, ... arXiv preprint arXiv:2312.09236, 2023 | 13 | 2023 |
| Diffhopp: A graph diffusion model for novel drug design via scaffold hopping J Torge, C Harris, SV Mathis, P Lio arXiv preprint arXiv:2308.07416, 2023 | 9 | 2023 |
| DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised h-transform A Denker, F Vargas, S Padhy, K Didi, SV Mathis, R Barbano, V Dutordoir, ... NeurIPS, 2024 | 8* | 2024 |
| Evaluating representation learning on the protein structure universe AR Jamasb, A Morehead, CK Joshi, Z Zhang, K Didi, S Mathis, C Harris, ... ArXiv, arXiv: 2406.13864 v1, 2024 | 8 | 2024 |
| Computational tools for assessing forest recovery with GEDI shots and forest change maps A Holcomb, SV Mathis, DA Coomes, S Keshav Science of Remote Sensing 8, 100106, 2023 | 8 | 2023 |
| Dynamics-Informed Protein Design with Structure Conditioning UJ Komorowska, SV Mathis, K Didi, F Vargas, P Lio, M Jamnik The Twelfth International Conference on Learning Representations, 2023 | 7* | 2023 |
| Rna-frameflow for de novo 3d rna backbone design R Anand, CK Joshi, A Morehead, AR Jamasb, C Harris, SV Mathis, K Didi, ... ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 2024 | 4* | 2024 |
| Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering M Gantz, SV Mathis, FEH Nintzel, PJ Zurek, T Knaus, E Patel, D Boros, ... bioRxiv, 2024.04. 08.588565, 2024 | 3 | 2024 |
| On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering M Gantz, SV Mathis, FEH Nintzel, P Lio, F Hollfelder Faraday Discussions 252, 89-114, 2024 | 3 | 2024 |
| Thought experiments in a quantum computer N Nurgalieva, S Mathis, L del Rio, R Renner arXiv preprint arXiv:2209.06236, 2022 | 3 | 2022 |
| Improving Antibody Design with Force-Guided Sampling in Diffusion Models P Kulytė, F Vargas, SV Mathis, YG Wang, JM Hernández-Lobato, P Liò arXiv preprint arXiv:2406.05832, 2024 | 2 | 2024 |
| Predicting protein variants with equivariant graph neural networks A Boca, S Mathis arXiv preprint arXiv:2306.12231, 2023 | 2 | 2023 |
| Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation D Nori, SV Mathis, A Shanehsazzadeh arXiv preprint arXiv:2312.05273, 2023 | | 2023 |
Pietro LiòProfessor, University of CambridgeVerified email at cam.ac.uk