TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure
Predicting the structure of a protein from its sequence is a cornerstone task of molecular biology. Established methods in the field, such as homology modeling and fragment assembly, appeared to have reached their limit. However, this year saw the emergence of promising new approaches: end-to-end protein structure and dynamics models, as well as reinforcement learning applied to protein folding. For these approaches to be investigated on a larger scale, an efficient implementation of their key computational primitives is required. In this paper we present a library of differentiable mappings from two standard dihedral-angle representations of protein structure (full-atom representation "$\phi,\psi,\omega,\chi$" and backbone-only representation "$\phi,\psi,\omega$") to atomic Cartesian coordinates. The source code and documentation can be found at https://github.com/lupoglaz/TorchProteinLibrary.
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Georgy Derevyanko (add twitter)
Guillaume Lamoureux (edit)
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PyTorch library of layers acting on protein representations
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HubBucket: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
bsletten: @SusannaLHarris Ah, good idea. I forget about them for things like this. I bet there would be useful, non-basic material there. These are examples of why I am interested: https://t.co/2oAvjGeqKT, https://t.co/TY1QvWhmkz, https://t.co/V5HPK0iEy1
AidanRocke: RT @g_lamoureux_: Our TorchProteinLibrary paper is out! https://t.co/WGGlKoAKZN
shubh_300595: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
PerthMLGroup: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
Epsilon_Lee: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
313V: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
mahbubrob: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
Montreal_AI: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
ceobillionaire: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
PlanetCrichton: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
IntuitMachine: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
letranger14: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
AssistedEvolve: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
supernovart75: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
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HubBucket: RT @arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https:/…
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arxiv_org: TorchProteinLibrary: A computationally efficient, differentiable representation of protei... https://t.co/Vqpifkqdvy https://t.co/AdnhrWcjAD
poliu2s: RT @g_lamoureux_: Our TorchProteinLibrary paper is out! https://t.co/WGGlKoAKZN
rbhar90: RT @g_lamoureux_: Our TorchProteinLibrary paper is out! https://t.co/WGGlKoAKZN
sofroniewn: RT @g_lamoureux_: Our TorchProteinLibrary paper is out! https://t.co/WGGlKoAKZN
adelong: RT @g_lamoureux_: Our TorchProteinLibrary paper is out! https://t.co/WGGlKoAKZN
MoAlQuraishi: RT @g_lamoureux_: Our TorchProteinLibrary paper is out! https://t.co/WGGlKoAKZN
g_lamoureux_: Our TorchProteinLibrary paper is out! https://t.co/WGGlKoAKZN
Michielstock: Did all the talk of #alphafold made you excited about protein folding and differentiable computing? There is now a PyTorch protein library for your own experiments! paper: https://t.co/LIYfivZYwl repo: https://t.co/nxMCudTL1Z https://t.co/GOuQhsLe26
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