Zobrazeno 1 - 10
of 550
pro vyhledávání: '"Dror, Ron"'
Autor:
Suriana, Patricia, Dror, Ron O.
Deep learning promises to dramatically improve scoring functions for molecular docking, leading to substantial advances in binding pose prediction and virtual screening. To train scoring functions-and to perform molecular docking-one must generate a
Externí odkaz:
http://arxiv.org/abs/2312.00191
Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as molecule design. Inspired by the recent huge success of Stable (latent) D
Externí odkaz:
http://arxiv.org/abs/2305.01140
Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due
Externí odkaz:
http://arxiv.org/abs/2303.11494
Few-shot learning is a promising approach to molecular property prediction as supervised data is often very limited. However, many important molecular properties depend on complex molecular characteristics -- such as the various 3D geometries a molec
Externí odkaz:
http://arxiv.org/abs/2302.02055
Autor:
Vögele, Martin, Thomson, Neil J., Truong, Sang T., McAvity, Jasper, Zachariae, Ulrich, Dror, Ron O.
Atomic-level simulations are widely used to study biomolecules and their dynamics. A common goal in such studies is to compare simulations of a molecular system under several conditions -- for example, with various mutations or bound ligands -- in or
Externí odkaz:
http://arxiv.org/abs/2212.02714
Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning. Here, we extend recent work on geometric vector perceptrons and apply equivariant graph neural networks to a wide range of tasks
Externí odkaz:
http://arxiv.org/abs/2106.03843
Autor:
Townshend, Raphael J. L., Vögele, Martin, Suriana, Patricia, Derry, Alexander, Powers, Alexander, Laloudakis, Yianni, Balachandar, Sidhika, Jing, Bowen, Anderson, Brandon, Eismann, Stephan, Kondor, Risi, Altman, Russ B., Dror, Ron O.
Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural networks have gained significant attention, but their widespread adoption
Externí odkaz:
http://arxiv.org/abs/2012.04035
Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A major bottleneck involves selecting the most accurate structural model
Externí odkaz:
http://arxiv.org/abs/2011.13557
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem do
Externí odkaz:
http://arxiv.org/abs/2009.01411
Many quantities we are interested in predicting are geometric tensors; we refer to this class of problems as geometric prediction. Attempts to perform geometric prediction in real-world scenarios have been limited to approximating them through scalar
Externí odkaz:
http://arxiv.org/abs/2006.14163