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Found 5 papers, 2 papers with code
RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints.
CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization.
Improving Graph Generation by Restricting Graph Bandwidth
However, one of the main limitations of existing methods is their large output space, which limits generation scalability and hinders accurate modeling of the underlying distribution.
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning
Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction.
Attention-Based Learning on Molecular Ensembles
The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual screening approaches.
