Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Authors: Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian FATRAS, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we validate FOLDFLOW on protein backbone generation of up to 300 amino acids leading to high-quality designable, diverse, and novel samples. |
| Researcher Affiliation | Collaboration | 1Mc Gill University, 2Mila, 3Dreamfold, 4Université de Montréal, 5University of Oxford |
| Pseudocode | Yes | Algorithm 1 FOLDFLOW-SFM training on SE(3) N, Algorithm 2 FOLDFLOW-SFM training on SO(3), Algorithm 3 Fold Flow-SFM Inference |
| Open Source Code | Yes | Our code can be found at https://github.com/DreamFold/FoldFlow. |
| Open Datasets | Yes | We evaluate FOLDFLOW models in generating valid, diverse, and novel backbones by training on a subset of the Protein Data Bank (PDB) with 22,248 proteins. We use a subset of PDB filtered with the same criteria as Frame Diff, specifically, we filter for monomers of length between 60 and 512 (inclusive) with resolution < 5Å downloaded from PDB (Berman et al., 2000) on July 20, 2023. |
| Dataset Splits | No | The paper mentions 'training dataset' and 'test samples' but does not explicitly describe a validation dataset split or percentages used for validation. |
| Hardware Specification | Yes | We train our model in Pytorch using distributed data-parallel (DDP) across four NVIDIA A100-80GB GPUs for roughly 2.5 days. |
| Software Dependencies | No | The paper mentions 'Pytorch' and 'Open Fold' but does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | We use the Adam optimizer with constant learning rate 10^-4, β1 = 0.9, β2 = 0.99. The batch size depends on the length of the protein to maintain roughly constant memory usage. In practice, we set the effective batch size to eff_bs = max(round(#GPUs * 500, 000/N^2), 1) (46) for each step. We set λaux = 0.25 and weight the rotation loss with coefficient 0.5 as compared to the translation loss which has weight 1.0. |