Diffusion models: A comprehensive survey of methods and applications
Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …
record-breaking performance in many applications, including image synthesis, video …
Geometric deep learning for drug discovery
Drug discovery is a time-consuming and expensive process. With the development of
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …
Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological processes at
temporal and spatial scales that would be intractable at an atomistic resolution. However …
temporal and spatial scales that would be intractable at an atomistic resolution. However …
One transformer can understand both 2d & 3d molecular data
Unlike vision and language data which usually has a unique format, molecules can naturally
be characterized using different chemical formulations. One can view a molecule as a 2D …
be characterized using different chemical formulations. One can view a molecule as a 2D …
[HTML][HTML] Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
Artificial intelligence for science in quantum, atomistic, and continuum systems
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 …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
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 still limited to small …
Transformers to the domain of 3D atomistic systems. However, they are still limited to small …
Molecular geometry pretraining with se (3)-invariant denoising distance matching
Molecular representation pretraining is critical in various applications for drug and material
discovery due to the limited number of labeled molecules, and most existing work focuses …
discovery due to the limited number of labeled molecules, and most existing work focuses …
A group symmetric stochastic differential equation model for molecule multi-modal pretraining
Molecule pretraining has quickly become the go-to schema to boost the performance of AI-
based drug discovery. Naturally, molecules can be represented as 2D topological graphs or …
based drug discovery. Naturally, molecules can be represented as 2D topological graphs or …
Symmetry-informed geometric representation for molecules, proteins, and crystalline materials
Artificial intelligence for scientific discovery has recently generated significant interest within
the machine learning and scientific communities, particularly in the domains of chemistry …
the machine learning and scientific communities, particularly in the domains of chemistry …