Zobrazeno 1 - 10
of 1 370
pro vyhledávání: '"Chaitanya, K."'
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:
Lawrence, Elsa, El-Shazly, Adham, Seal, Srijit, Joshi, Chaitanya K, Liò, Pietro, Singh, Shantanu, Bender, Andreas, Sormanni, Pietro, Greenig, Matthew
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in l
Externí odkaz:
http://arxiv.org/abs/2403.04106
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
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
Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms. Little work has been done on creating expressive layers that are invariant to
Externí odkaz:
http://arxiv.org/abs/2305.19207
Autor:
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 considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA
Externí odkaz:
http://arxiv.org/abs/2305.14749