@misc{10481/88789,
year = {2023},
url = {https://hdl.handle.net/10481/88789},
abstract = {ManyFold 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.},
organization = {Spanish Ministry of Science and Innovation Project No. PID2019-104206GB- I00/AEI/10.13039/501100011033},
publisher = {Oxofrd},
title = {ManyFold: An efficient and flexible library for training and validating protein folding models},
doi = {10.1093/bioinformatics/btac773},
author = {Villegas Morcillo, Amelia Otilia and Robinson, Louis and Flajolet, Arthur and Barrett, Thomas D.},
}