natural-language-processing-3

Protein Folding

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in protein-folding-3

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Libraries

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Use these libraries to find protein-folding-3 models and implementations

Datasets

PS4

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Most implemented papers

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

M. Bronstein, Joan Bruna, Taco Cohen, Petar Velivckovi'c•Mon Apr 26 2021

A 'geometric unification' endeavour that provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNN's, GNNs, and Transformers, and gives a constructive procedure to incorporate prior physical knowledge into neural architectures and provide principled way to build future architectures yet to be invented.

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Highly accurate protein structure prediction with AlphaFold

O. Vinyals, A. Pritzel, Pushmeet Kohli, K. Kavukcuoglu, D. Hassabis, A. Senior, Richard Evans, J. Jumper, Tim Green, Augustin Žídek, Alex Bridgland, Stig Petersen, David Silver, Bernardino Romera-Paredes, Stanislav Nikolov, Clemens Meyer, T. Back, O. Ronneberger, Michael Figurnov, Anna Potapenko, Ellen Clancy, Martin Steinegger, Kathryn Tunyasuvunakool, Russ Bates, Simon A A Kohl, Andy Ballard, A. Cowie, Rishub Jain, J. Adler, D. Reiman, Michal Zielinski, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein•Wed Jul 14 2021

This work validated an entirely redesigned version of the neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods.

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Rethinking Neural Operations for Diverse Tasks

Christopher R'e, Ameet Talwalkar, M. Khodak, Nicholas Roberts, Tri Dao, Liam Li•Sun Mar 28 2021

This work introduces a search space of operations called XD-Operations that mimic the inductive bias of standard multi-channel convolutions while being much more expressive: it is proved that it includes many named operations across multiple application areas.

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TorchMD: A Deep Learning Framework for Molecular Simulations

F. Noé, Stefan Doerr, Maciej Majewski, Adrià Pérez, Andreas Krämer, C. Clementi, T. Giorgino, G. D. Fabritiis•Mon Dec 21 2020

TorchMD is presented, a framework for molecular simulations with mixed classical and machine learning potentials that enables learning and simulating neural network potentials and provides a useful tool set to support molecular simulations of machinelearning potentials.

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ParaFold: Paralleling AlphaFold for Large-Scale Predictions

Bozitao Zhong, Xiaoming Su, Minhua Wen, Sicheng Zuo, Liang Hong, James Lin•Wed Nov 10 2021

The development of ParaFold will greatly speed up high-throughput studies and render the protein “structure-omics” feasible, leveraging the predictive power by running on supercomputers, with shorter time and at a lower cost.

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

•Mon Jul 11 2016

The HP model of protein folding, where the chain exists in a free medium, is investigated using a parallel Monte Carlo scheme based upon Wang-Landau sampling, and the problem of dynamical trapping was avoided, an enhancement in the efficiency of traversing configuration space was obtained.

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

Z. Li, Jinbo Xu, Sheng Wang, S. Sun, Renyu Zhang•Thu Sep 01 2016

A new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks that greatly outperforms existing methods and leads to much more accurate contact-assisted folding.

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Simultaneous coherent structure coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

J. Dabiri, B. Husic, Kristy L. Schlueter-Kuck•Wed Jul 11 2018

A new method, simultaneous coherent structure coloring (sCSC), is presented, which accomplishes the task of unsupervised clustering without a priori guidance regarding the underlying structure of the data.

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TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure

Georgy Derevyanko, G. Lamoureux•Thu Nov 22 2018

This paper presents a library of differentiable mappings from two standard dihedral-angle representations of protein structure to atomic Cartesian coordinates, using end-to-end protein structure and dynamics models, as well as reinforcement learning applied to protein folding.

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