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..
Evolution-Inspired Loss Functions for Protein Representation Learning
Authors: Chengyue Gong, Adam Klivans, James Madigan Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Across a variety of phenotypes and datasets, we demonstrate that Evo Rank leads to dramatic improvements in zero-shot performance and can compete with models fine-tuned on experimental data. |
| Researcher Affiliation | Collaboration | 1University of Texas at Austin 2Intelligent Proteins, LLC. |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not explicitly state that open-source code is provided for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | For the self-supervised training, we use the same procedure as Mut Compute X (d Oelsnitz et al., 2023). Briefly, this dataset consists of a 90:10 split of 2,569,256 microenvironments sampled from 22,759 protein sequences clustered at 50% sequence similarity and having a structure resolution of at least 3 A from the RCSB (November 2021). Our test data for the folding free energy changes and binding free energy changes are proposed in Diaz et al. (2023); Gong et al. (2023) |
| Dataset Splits | No | The paper mentions a |
| Hardware Specification | Yes | Training the model typically requires approximately two GPU days on one A100. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | Self-supervised training was done with the Adam W optimizer, 512 batch size, 5 10 5 learning rate, 10 5 weight decay. We first train using the soft-label loss in equation (2) for 100K iterations, and then refine with the Evo Rank loss defined in equation (4), for an additional 100K iterations. |