Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models tate-specific protein-ligand complex structure prediction with a multi-scale deep generative model (new ...

The prediction of crystal properties is crucial in crystal design. Currently, most methods employ graph neural networks to model crystal structures and have achieved satisfactory prediction accuracy.

While this repository primarily focuses on text-guided generation, we also provide pre-trained models for the DNG (De novo generation) and CSP (Crystal Structure Prediction) tasks in the chemeleon-dng ...

In a paper published in National Science Review, a research team of has released a “Machine learning And Graph theory assisted Universal structure Searcher” (MAGUS). Their tests have ...

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input ...

Accomplishment: A GCNN model was trained to predict the formation energy and bulk modulus of solid solution alloys with different crystal lattice structures, element composition, and atomic ...