wells-wood-research/timed-design: Models 03-2023

All models were trained using the following dataset settings from aposteriori poetry run make-frame-dataset /scratch/datasets/biounit/ -d benchmarking_set.csv -e .pdb1.gz --voxels-per-side 21 --frame-edge-length 21 -g True -p 35 -n benchmark_set -v -r -z -cb True -ae CNOCBCA --compression_gzip True -o /scratch/timed_dataset/ We retrained all models with the same dataset and tested on the PDBench benchmark. Accuracy Macro-Recall Macro-Recall is accuracy averaged per residue - resistant to class imbalance. RMSD We sampled 10% of the dataset and ran it through AlphaFold2 + Amber relaxation

Leonardo Castorina. (2023). wells-wood-research/timed-design: Models 03-2023 (Version model0323). Zenodo. https://doi.org/10.5281/zenodo.7764674