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..
Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization
Authors: Jiahao Qiu, Hui Yuan, Jinghong Zhang, Wentao Chen, Huazheng Wang, Mengdi Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test various instances of the algorithm across benchmark protein datasets using simulated screens. Experiment results demonstrate that the algorithm is both sample-efficient, diversity-promoting, and able to find top designs using reasonably small mutation counts. We experiment using three datasets from protein engineering studies and train oracles to simulate the ground-truth wet-lab fitness scores f of the landscape. |
| Researcher Affiliation | Collaboration | Jiahao Qiu*1, Hui Yuan*1, Jinghong Zhang*2, Wentao Chen3, Huazheng Wang4, Mengdi Wang1 1Princeton University 2University of California San Diego 3MLAB Biosciences Inc 4Oregon State University |
| Pseudocode | Yes | Algorithm 1: Meta algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the methodology described in this paper. |
| Open Datasets | Yes | We build experiments around real-world protein datasets, such as AAV (Bryant et al. 2021), TEM (Gonzalez and Ostermeier 2019) and AAYL49 antibody (Engelhart et al. 2022). |
| Dataset Splits | No | The paper describes an iterative exploration process where models query sequences from a black-box oracle, rather than specifying traditional fixed training, validation, and test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper mentions 'abundant computing resources' from MLAB Biosciences Inc. but does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions the use of 'neural network', 'UCB/TS formula', 'CNN', and 'TAPE embedding' but does not provide specific version numbers for any software libraries, frameworks, or dependencies. |
| Experiment Setup | Yes | In the experiment, we run each algorithm for 10 rounds with 100 query sequences per round for a fair comparison with our baselines. Each test is run for 50 repeats using 50 different random seeds. |