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Structure-based drug design with geometric deep learning
C Isert, K Atz, G Schneider - Current Opinion in Structural Biology, 2023 - Elsevier
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda… - Chemical …, 2021 - ACS Publications
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …
numerous advances previously out of reach due to the computational complexity of …
Geometric latent diffusion models for 3d molecule generation
Generative models, especially diffusion models (DMs), have achieved promising results for
generating feature-rich geometries and advancing foundational science problems such as …
generating feature-rich geometries and advancing foundational science problems such as …
MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Creating fast and accurate force fields is a long-standing challenge in computational
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
chemistry and materials science. Recently, Equivariant Message Passing Neural Networks …
Learning local equivariant representations for large-scale atomistic dynamics
A Musaelian, S Batzner, A Johansson, L Sun… - Nature …, 2023 - nature.com
A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …
energy surface of molecules and materials is a long-standing goal in the natural sciences …
Pure transformers are powerful graph learners
We show that standard Transformers without graph-specific modifications can lead to
promising results in graph learning both in theory and practice. Given a graph, we simply …
promising results in graph learning both in theory and practice. Given a graph, we simply …
Geodiff: A geometric diffusion model for molecular conformation generation
Predicting molecular conformations from molecular graphs is a fundamental problem in
cheminformatics and drug discovery. Recently, significant progress has been achieved with …
cheminformatics and drug discovery. Recently, significant progress has been achieved with …
Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations
Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying
Transformers to the domain of 3D atomistic systems. However, they are limited to small …
Transformers to the domain of 3D atomistic systems. However, they are limited to small …
Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
YL Liao, T Smidt - arxiv preprint arxiv:2206.11990, 2022 - arxiv.org
Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …
e3nn: Euclidean neural networks
We present e3nn, a generalized framework for creating E (3) equivariant trainable functions,
also known as Euclidean neural networks. e3nn naturally operates on geometry and …
also known as Euclidean neural networks. e3nn naturally operates on geometry and …