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Found 12 papers, 10 papers with code
Deep Learning for Protein-Ligand Docking: Are We There Yet?
The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery.
Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion
In this work, we introduce MMDiff, a generative model that jointly designs sequences and structures of nucleic acid and protein complexes, independently or in complex, using joint SE(3)-discrete diffusion noise.
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity.
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
Importantly, we demonstrate that the geometry-complete denoising process of GCDM learned for 3D molecule generation enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn.
Geometry-Complete Perceptron Networks for 3D Molecular Graphs
The field of geometric deep learning has had a profound impact on the development of innovative and powerful graph neural network architectures.
DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning
Utilizing true contacts as input, DRLComplex achieved high average TM-scores of 0. 9895 and 0. 9881 and a low average interface RMSD (I_RMSD) of 0. 2197 and 0. 92 on the two datasets, respectively.
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment
We challenge this significant task with DProQ, which introduces a gated neighborhood-modulating Graph Transformer (GGT) designed to predict the quality of 3D protein complex structures.
EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
Protein complexes are macromolecules essential to the functioning and well-being of all living organisms.
A Region-Based Deep Learning Approach to Automated Retail Checkout
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking.
Semi-Supervised Graph Learning Meets Dimensionality Reduction
In this work, we investigate the use of dimensionality reduction techniques such as PCA, t-SNE, and UMAP to see their effect on the performance of graph neural networks (GNNs) designed for semi-supervised propagation of node labels.
Geometric Transformers for Protein Interface Contact Prediction
Computational methods for predicting the interface contacts between proteins come highly sought after for drug discovery as they can significantly advance the accuracy of alternative approaches, such as protein-protein docking, protein function analysis tools, and other computational methods for protein bioinformatics.
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
In this work, we expand on a dataset recently introduced for this task, the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42, 112 complexes for geometric deep learning of protein interfaces.
