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Found 21 papers, 14 papers with code
Evaluating representation learning on the protein structure universe
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks.
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.
Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs
Cryo-electron microscopy (cryo-EM) remains pivotal in structural biology, yet the task of protein particle picking, integral for 3D protein structure construction, is laden with manual inefficiencies.
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action
This review aims to provide a rather thorough overview of the applications of diffusion models in bioinformatics to aid their further development in bioinformatics and computational biology.
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.
Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years.
Deep Learning Prediction of Severe Health Risks for Pediatric COVID-19 Patients with a Large Feature Set in 2021 BARDA Data Challenge
Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized or even to receive intensive medical care (e. g., invasive mechanical ventilation or cardiovascular support) to recover from the illnesses.
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.
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.
CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles
Although these models improve the recommendation quality, there are two primary aspects for further improvements: (1) multiple models focus only on some portion of the available contextual information and neglect other portions; (2) learning the feature space of the side contextual information needs to be further enhanced.
Inverse Structural Design of Graphene/Boron Nitride Hybrids by Regressional GAN
A significant novelty of the proposed RGAN is that it combines the supervised and regressional convolutional neural network (CNN) with the traditional unsupervised GAN, thus overcoming the common technical barrier in the traditional GANs, which cannot generate data associated with given continuous quantitative labels.
Computational Physics Materials Science Applied Physics
Deep Learning Bandgaps of Topologically Doped Graphene
Moreover, given ubiquitous existence of topologies in materials, this work will stimulate widespread interests in applying deep learning algorithms to topological design of materials crossing atomic, nano-, meso-, and macro- scales.
Materials Science Computational Physics
DeepSF: deep convolutional neural network for mapping protein sequences to folds
Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold.
DeepQA: Improving the estimation of single protein model quality with deep belief networks
The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method.
Evaluation of Protein Structural Models Using Random Forests
Our method uses both multi and single model quality assessment method for global quality assessment, and uses chemical, physical, geo-metrical features, and global quality score for local quality assessment.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
