Zobrazeno 1 - 10
of 3 231
pro vyhledávání: '"Morehead A"'
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
The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and b
Externí odkaz:
http://arxiv.org/abs/2405.14108
Generative models of macromolecules carry abundant and impactful implications for industrial and biomedical efforts in protein engineering. However, existing methods are currently limited to modeling protein structures or sequences, independently or
Externí odkaz:
http://arxiv.org/abs/2401.06151
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
Autor:
Alex Morehead, Jianlin Cheng
Publikováno v:
Communications Chemistry, Vol 7, Iss 1, Pp 1-11 (2024)
Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric pro
Externí odkaz:
https://doaj.org/article/62fabf3548b2498a9e31d236fd0aca22
Autor:
Morehead, Alex, Cheng, Jianlin
Denoising diffusion probabilistic models (DDPMs) have pioneered new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to structure-guided protein desi
Externí odkaz:
http://arxiv.org/abs/2302.04313
Autor:
Morehead, Alex, Cheng, Jianlin
The field of geometric deep learning has had a profound impact on the development of innovative and powerful graph neural network architectures. Disciplines such as computer vision and computational biology have benefited significantly from such meth
Externí odkaz:
http://arxiv.org/abs/2211.02504
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 11 (2024)
IntroductionLithium is a well-known agent to cause systemic toxicity with its narrow therapeutic window. Toxic cardiac effects are known but seldomly reported and can manifest as sinus node dysfunction (SND) ranging from delayed conduction to sinus a
Externí odkaz:
https://doaj.org/article/c5dcff72ca38456f9ac435e10e06b239
Autor:
Soltanikazemi, Elham, Roy, Raj S., Quadir, Farhan, Giri, Nabin, Morehead, Alex, Cheng, Jianlin
Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protein quaternary structures using predicted i
Externí odkaz:
http://arxiv.org/abs/2205.13594