Zobrazeno 1 - 10
of 4 902
pro vyhledávání: '"SIMON, V."'
We discuss the single-quantum positron annihilation with a bound electron in the near threshold region. The angular distribution and the total cross section are considered. We obtain a simple analytical expression for the Coulomb potential and for th
Externí odkaz:
http://arxiv.org/abs/2409.20061
Autor:
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 consider large-scale pre-training and downstream tasks on both experimental and predicted structu
Externí odkaz:
http://arxiv.org/abs/2406.13864
Autor:
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 establish protocols for data preparation and evaluation to address unique challenges posed by RNA
Externí odkaz:
http://arxiv.org/abs/2406.13839
Autor:
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 based on denoising diffusion processes emerged as a leading candidate to address this motif
Externí odkaz:
http://arxiv.org/abs/2312.09236
Autor:
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 embedded as nodes in 3D Euclidean space. In these graphs, the geometric attributes transform acco
Externí odkaz:
http://arxiv.org/abs/2312.07511
The success of therapeutic antibodies relies on their ability to selectively bind antigens. AI-based antibody design protocols have shown promise in generating epitope-specific designs. Many of these protocols use an inverse folding step to generate
Externí odkaz:
http://arxiv.org/abs/2312.05273
Publikováno v:
Microbial Cell Factories, Vol 23, Iss 1, Pp 1-12 (2024)
Abstract Background Gene expression noise (variation in gene expression among individual cells of a genetically uniform cell population) can result in heterogenous metabolite production by industrial microorganisms, with cultures containing both low-
Externí odkaz:
https://doaj.org/article/19897475c46a4b9b9b7aa402c6aa0805
Scaffold hopping is a drug discovery strategy to generate new chemical entities by modifying the core structure, the \emph{scaffold}, of a known active compound. This approach preserves the essential molecular features of the original scaffold while
Externí odkaz:
http://arxiv.org/abs/2308.07416
Autor:
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 interest in recent years. These methods offer the promise of higher-quality molecule generation
Externí odkaz:
http://arxiv.org/abs/2308.07413
Autor:
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 improving, accelerating, and enabling our understanding of natural phenomena at a wide range
Externí odkaz:
http://arxiv.org/abs/2307.08423