Protein Folding

26 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

elias-sundqvist/obsidian-annotator 27 Apr 2021

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods.

Highly accurate protein structure prediction with AlphaFold

deepmind/alphafold Nature 2021

Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.

Rethinking Neural Operations for Diverse Tasks

mkhodak/relax NeurIPS 2021

An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains.

Variational Encoding of Complex Dynamics

msmbuilder/vde 23 Nov 2017

Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds.

TorchMD: A deep learning framework for molecular simulations

torchmd/torchmd 22 Dec 2020

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials.

ParaFold: Paralleling AlphaFold for Large-Scale Predictions

zuricho/parallelfold 11 Nov 2021

We evaluated the accuracy and efficiency of optimizations on CPUs and GPUs, and showed the large-scale prediction capability by running ParaFold inferences of 19, 704 small proteins in five hours on one NVIDIA DGX-2.

A Parallel Trajectory Swapping Wang - Landau Study Of The HP Protein Model

patherlkd/Monte-Carlo-Lattice-Polymers 12 Jul 2016

The native results for the benchmark sequences and lattice polymers were compared with varying computational methods.

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

j3xugit/RaptorX-Contact 2 Sep 2016

Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0. 5.

Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

brookehus/sCSC 12 Jul 2018

sCSC performs a sequence of binary splittings on the dataset such that the most dissimilar data points are required to be in separate clusters.

TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure

lupoglaz/TorchProteinLibrary 23 Nov 2018

Predicting the structure of a protein from its sequence is a cornerstone task of molecular biology.