A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Structure-based drug design with geometric deep learning
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 …
A survey on generative diffusion models
Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …
capturing and generalizing patterns within data, we have entered the epoch of all …
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 …
Structure-based drug design with equivariant diffusion models
Abstract Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …
An artificial intelligence accelerated virtual screening platform for drug discovery
Abstract Structure-based virtual screening is a key tool in early drug discovery, with growing
interest in the screening of multi-billion chemical compound libraries. However, the success …
interest in the screening of multi-billion chemical compound libraries. However, the success …
Equivariant flow matching with hybrid probability transport for 3d molecule generation
The generation of 3D molecules requires simultaneously deciding the categorical features
(atom types) and continuous features (atom coordinates). Deep generative models …
(atom types) and continuous features (atom coordinates). Deep generative models …
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 …
Diffusion models in bioinformatics and computational biology
Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …
applied in computer vision, natural language processing and bioinformatics. In this Review …
A survey on graph diffusion models: Generative ai in science for molecule, protein and material
Diffusion models have become a new SOTA generative modeling method in various fields,
for which there are multiple survey works that provide an overall survey. With the number of …
for which there are multiple survey works that provide an overall survey. With the number of …