Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
Sign Up to like & getrecommendations! Published in 2022 at "Advanced Science"
DOI: 10.1002/advs.202203796
Abstract: The latest biological findings observe that the motionless “lock‐and‐key” theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure… read more here.
Keywords: equivariant graph; graph matching; matching networks; drug binding ... See more keywords
Discovery of Spin-Crossover Materials with Equivariant Graph Neural Networks and Relevance-Based Classification.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.4c01690
Abstract: Swift discovery of spin-crossover materials for their potential application in electronic and quantum devices requires techniques that enable efficient identification of suitable candidates. To this end, we screened the Cambridge Structural Database to develop a… read more here.
Keywords: crossover materials; spin crossover; discovery spin; equivariant graph ... See more keywords
Structure-aware diffusion model for molecule generation based on K-Nearest Neighbor and equivariant graph neural network.
Sign Up to like & getrecommendations! Published in 2025 at "Future medicinal chemistry"
DOI: 10.1080/17568919.2025.2552638
Abstract: AIM Generating molecules with specific chemical properties for target proteins can accelerate the drug development process and open new avenues for developing treatments for diseases with known pathogenic target proteins. However, current approaches to generate… read more here.
Keywords: graph neural; diffusion model; model; equivariant graph ... See more keywords