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 …
Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
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 …
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 …
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 …
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 …
End-to-end latent variational diffusion models for inverse problems in high energy physics
A Shmakov, K Greif, M Fenton… - Advances in …, 2023 - proceedings.neurips.cc
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into
open questions in particle physics. However, detector effects must be corrected before …
open questions in particle physics. However, detector effects must be corrected before …
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 …