Protein Folding
38 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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PS4
Most implemented papers
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure
lupoglaz/TorchProteinLibrary
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Predicting the structure of a protein from its sequence is a cornerstone task of molecular biology.
Geometric constraints in protein folding
The intricate three-dimensional geometries of protein tertiary structures underlie protein function and emerge through a folding process from one-dimensional chains of amino acids.
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding
Our study provides a quantitative basis to understand how DL driven MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.
Message Scheduling for Performant, Many-Core Belief Propagation
Belief Propagation (BP) is a message-passing algorithm for approximate inference over Probabilistic Graphical Models (PGMs), finding many applications such as computer vision, error-correcting codes, and protein-folding.
GraphQA: Protein Model Quality Assessment using Graph Convolutional Network
baldassarrefe/protein-quality-gn
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Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure.
Generative Models for Graph-Based Protein Design
jingraham/neurips19-graph-protein-design
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ICLR Workshop DeepGenStruct 2019
Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice.
PolyFold: an interactive visual simulator for distance-based protein folding
Here we present PolyFold, an interactive visual simulator for dynamically capturing the distance-based protein folding process through real-time rendering of a distance matrix and its compatible spatial conformation as it folds in an intuitive and easy-to-use interface.
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design.
A fast and simple modification of Newton's method helping to avoid saddle points
The main result of this paper roughly says that if $f$ is $C^3$ (can be unbounded from below) and a sequence $\{x_n\}$, constructed by the New Q-Newton's method from a random initial point $x_0$, {\bf converges}, then the limit point is a critical point and is not a saddle point, and the convergence rate is the same as that of Newton's method.
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
phermosilla/IEConv_proteins
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ICLR 2021
Proteins perform a large variety of functions in living organisms, thus playing a key role in biology.
