Search Results - "Mathis, Simon V"

On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering by Gantz, Maximilian, Mathis, Simon V, Nintzel, Friederike E H, Lio, Pietro, Hollfelder, Florian

“…Protein design and directed evolution have separately contributed enormously to protein engineering. Without being mutually exclusive, the former relies on…”
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Gauge-invariant quantum circuits for U (1) and Yang-Mills lattice gauge theories by Mazzola, Giulia, Mathis, Simon V., Mazzola, Guglielmo, Tavernelli, Ivano

“…Quantum computation represents an emerging framework to solve lattice gauge theories (LGTs) with arbitrary gauge groups, a general and long-standing problem in…”
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Computational tools for assessing forest recovery with GEDI shots and forest change maps by Holcomb, Amelia, Mathis, Simon V., Coomes, David A., Keshav, Srinivasan

“…Tropical secondary forests are ecosystems of critical importance for protecting biodiversity, buffering primary forest loss, and sequestering atmospheric…”
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Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation by Nori, Divya, Mathis, Simon V, Shanehsazzadeh, Amir

“…The success of therapeutic antibodies relies on their ability to selectively bind antigens. AI-based antibody design protocols have shown promise in generating…”
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DiffHopp: A Graph Diffusion Model for Novel Drug Design via Scaffold Hopping by Torge, Jos, Harris, Charles, Mathis, Simon V, Lio, Pietro

“…Scaffold hopping is a drug discovery strategy to generate new chemical entities by modifying the core structure, the \emph{scaffold}, of a known active…”
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A framework for conditional diffusion modelling with applications in motif scaffolding for protein design by Didi, Kieran, Vargas, Francisco, Mathis, Simon V, Dutordoir, Vincent, Mathieu, Emile, Komorowska, Urszula J, Lio, Pietro

“…Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms…”
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Toward scalable simulations of Lattice Gauge Theories on quantum computers by Mathis, Simon V, Mazzola, Guglielmo, Tavernelli, Ivano

“…Phys. Rev. D 102, 094501 (2020) The simulation of real-time dynamics in lattice gauge theories is particularly hard for classical computing due to the…”
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Gauge invariant quantum circuits for $U(1)$ and Yang-Mills lattice gauge theories by Mazzola, Giulia, Mathis, Simon V, Mazzola, Guglielmo, Tavernelli, Ivano

“…Phys. Rev. Research, 2021 Quantum computation represents an emerging framework to solve lattice gauge theories (LGT) with arbitrary gauge groups, a general and…”
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On the Expressive Power of Geometric Graph Neural Networks by Joshi, Chaitanya K, Bodnar, Cristian, Mathis, Simon V, Cohen, Taco, Liò, Pietro

“…Proceedings of the 40th International Conference on Machine Learning, PMLR 202:15330-15355, 2023 The expressive power of Graph Neural Networks (GNNs) has been…”
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Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models? by Harris, Charles, Didi, Kieran, Jamasb, Arian R, Joshi, Chaitanya K, Mathis, Simon V, Lio, Pietro, Blundell, Tom

“…Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable…”
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Evaluating representation learning on the protein structure universe by Jamasb, Arian R, Morehead, Alex, Joshi, Chaitanya K, Zhang, Zuobai, Didi, Kieran, Mathis, Simon V, Harris, Charles, Tang, Jian, Cheng, Jianlin, Lio, Pietro, Blundell, Tom L

“…We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We…”
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RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design by Anand, Rishabh, Joshi, Chaitanya K, Morehead, Alex, Jamasb, Arian R, Harris, Charles, Mathis, Simon V, Didi, Kieran, Hooi, Bryan, Liò, Pietro

“…We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and…”
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gRNAde: Geometric Deep Learning for 3D RNA inverse design by Joshi, Chaitanya K, Jamasb, Arian R, Viñas, Ramon, Harris, Charles, Mathis, Simon V, Morehead, Alex, Anand, Rishabh, Liò, Pietro

“…Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without…”
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A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems by Duval, Alexandre, Mathis, Simon V, Joshi, Chaitanya K, Schmidt, Victor, Miret, Santiago, Malliaros, Fragkiskos D, Cohen, Taco, Liò, Pietro, Bengio, Yoshua, Bronstein, Michael

“…Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms…”
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Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems by Zhang, Xuan, Wang, Limei, Helwig, Jacob, Luo, Youzhi, Fu, Cong, Xie, Yaochen, Liu, Meng, Lin, Yuchao, Xu, Zhao, Yan, Keqiang, Adams, Keir, Weiler, Maurice, Li, Xiner, Fu, Tianfan, Wang, Yucheng, Yu, Haiyang, Xie, YuQing, Fu, Xiang, Strasser, Alex, Xu, Shenglong, Liu, Yi, Du, Yuanqi, Saxton, Alexandra, Ling, Hongyi, Lawrence, Hannah, Stärk, Hannes, Gui, Shurui, Edwards, Carl, Gao, Nicholas, Ladera, Adriana, Wu, Tailin, Hofgard, Elyssa F, Tehrani, Aria Mansouri, Wang, Rui, Daigavane, Ameya, Bohde, Montgomery, Kurtin, Jerry, Huang, Qian, Phung, Tuong, Xu, Minkai, Joshi, Chaitanya K, Mathis, Simon V, Azizzadenesheli, Kamyar, Fang, Ada, Aspuru-Guzik, Alán, Bekkers, Erik, Bronstein, Michael, Zitnik, Marinka, Anandkumar, Anima, Ermon, Stefano, Liò, Pietro, Yu, Rose, Günnemann, Stephan, Leskovec, Jure, Ji, Heng, Sun, Jimeng, Barzilay, Regina, Jaakkola, Tommi, Coley, Connor W, Qian, Xiaoning, Qian, Xiaofeng, Smidt, Tess, Ji, Shuiwang

“…Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by…”
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