molearn is a generative neural network learning protein conformational spaces from example protein conformations generated by molecular dynamics simulations, or experimentally.
AVAILABILITY
molearn is available for download on our Github page. For UNIX based systems, installation via conda-forge is also available.
Jupyter notebook tutorials showing how a trained molearn neural network can be used are available here.
REFERENCES
If you use molearn in your work, please cite:
Theory and benchmarks of neural networks trained on protein conformational ensembles are found here:
V.K. Ramaswamy, S.C. Musson, C.G. Willcocks, M.T. Degiacomi (2021). Learning protein conformational space with convolutions and latent interpolations, Physical Review X 11 (primary citation)