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dc.contributor.authorVillegas Morcillo, Amelia Otilia 
dc.contributor.authorRobinson, Louis
dc.contributor.authorFlajolet, Arthur
dc.contributor.authorBarrett, Thomas D.
dc.date.accessioned2024-02-09T08:50:53Z
dc.date.available2024-02-09T08:50:53Z
dc.date.issued2023
dc.identifier.urihttps://hdl.handle.net/10481/88789
dc.description.abstractManyFold is a flexible library for protein structure prediction with deep learning that (i) supports models that use both multiple sequence alignments (MSAs) and protein language model (pLM) embedding as inputs, (ii) allows inference of existing models (AlphaFold and OpenFold), (iii) is fully trainable, allowing for both fine-tuning and the training of new models from scratch and (iv) is written in Jax to support efficient batched operation in dis- tributed settings. A proof-of-concept pLM-based model, pLMFold, is trained from scratch to obtain reasonable results with reduced computational overheads in comparison to AlphaFold.es_ES
dc.description.sponsorshipSpanish Ministry of Science and Innovation Project No. PID2019-104206GB- I00/AEI/10.13039/501100011033es_ES
dc.language.isoenges_ES
dc.publisherOxofrdes_ES
dc.titleManyFold: An efficient and flexible library for training and validating protein folding modelses_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1093/bioinformatics/btac773
dc.type.hasVersionVoRes_ES


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