The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
HighPlay: Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction
Open accessPublished version of a preprint
Abstract
The structural diversity and good biocompatibility of cyclic peptides have led to their emergence as potential therapeutic agents. Existing cyclic peptide design methods, whether traditional or emerging AI-assisted, rely on a multitude of experiments and face challenges such as limited molecular diversity, high cost, and time-consuming. In this study, we propose HighPlay, which integrates reinforcement learning (MCTS) with the HighFold structural prediction model to design cyclic peptide sequences based solely on the target protein sequence information, to achieve the synergistic optimization of cyclic peptide sequences and binding sites and to dynamically explore the sequence space without the need for predefined target information. The model was applied to the design of cyclic peptide sequences for three different targets, which were screened and verified through molecular dynamics simulations, demonstrating good binding affinity. Specifically, the cyclic peptide sequences designed for the TEAD4 target exhibited micromolar-level affinity in further experimental validation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2022) · doi:10.1101/2022.12.09.519842 · Cited by 2,369
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325
Robust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,210
Robust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107
A graph placement methodology for fast chip design
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Nova, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03544-w · Cited by 618
Flagged first, then by citations
Research works printed but not linked: 42
- 10.1038/s41573-020-00135-8 · DOI
- 10.1038/s41392-022-00904-4 · DOI · Paper page
- 10.1016/j.drudis.2014.10.003 · DOI
- 10.1002/chem.201905385 · DOI
- 10.1021/acsmedchemlett.1c00438 · DOI
- 10.1039/D1CB00154J · DOI
- 10.1111/imm.12889 · DOI
- 10.1002/pro.177 · DOI
- 10.1021/jacs.8b11099 · DOI
- 10.1021/acs.jctc.1c00374 · DOI
- 10.1038/ja.2011.35 · DOI · Paper page
- 10.5414/CN109325 · DOI
- 10.1002/anie.202308251 · DOI
- 10.1016/j.addr.2016.05.009 · DOI
- 10.2174/1568026616666160713125555 · DOI
- 10.1002/anie.201412070 · DOI
- 10.1021/jacs.0c00269 · DOI
- 10.1016/j.pharmthera.2020.107792 · DOI
- 10.3389/fmicb.2020.582779 · DOI
- 10.1007/978-1-60327-159-2_3 · DOI
- 10.1016/j.csbj.2017.04.001 · DOI
- 10.1038/s41592-023-02086-5 · DOI · Paper page
- 10.1126/science.add1964 · DOI · Paper page
- 10.1101/2022.07.23.501214 · DOI
- 10.1038/s41586-021-04301-9 · DOI · Paper page
- 10.1038/s42256-022-00506-3 · DOI
- 10.1038/s41586-022-05172-4 · DOI · Paper page
- 10.1038/s41586-023-05732-2 · DOI
- 10.1109/TCIAIG.2012.2186810 · DOI · Paper page
- 10.1038/s42256-023-00691-9 · DOI
- 10.1093/bib/bbae215 · DOI
- Vaswani A., 2023, arXiv August 2
- 10.1021/acs.jmedchem.7b00879 · DOI
- 10.1073/pnas.2002449117 · DOI
- 10.1111/bjh.17786 · DOI
- 10.1002/cbic.201300163 · DOI
- 10.1002/pro.2187 · DOI
- 10.1021/acs.jctc.9b00591 · DOI
- 10.1093/nar/gkm276 · DOI
- 10.1021/ct400341p · DOI
- 10.1021/ct300418h · DOI
- 10.3390/s150510481 · DOI
Metadata
- Authors
- Lin, H., Zhu, C., Shang, T., Zhu, N., Lin, K., Zhang, C., Shao, X., Wang, X., & Duan, H.
- Affiliations
- Zhejiang University of Technology (CN)
- AI department, Shenzhen Highslab Therapeutics. Inc
- Macao Polytechnic University (MO)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Shao, Xiang
- Wang, Xudong
- Duan, Hongliang
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2025-05-23
- Volume
- 68
- Issue
- 11
- Pages
- 12047-12057
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W4410695808
- Cited by
- 10
- References
- 8
- Citations by year
- 2026: 8 · 2025: 2
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Medicinal Chemistry (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Fundo para o Desenvolvimento das Ci??ncias e da Tecnologia (0151/2024/RIA2)
- Natural Science Foundation of Zhejiang Province (LD22H300004)
- Macao Polytechnic University (RP/FCA-07/2024)
- Pre-research Project of Suzhou Kowloon Hospital (SZJL202203)
- Suzhou Medical and Health Science and Technology Innovation (SKY2023113)
- Topics
- Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- reinforcement learning, Monte Carlo tree search, protein structure prediction, TEAD4, binding affinity, molecular dynamics simulation, AI-assisted drug design
- MeSH terms
- Amino Acid Sequence, Binding Sites, DNA-Binding Proteins, Humans, Peptides, Cyclic, Protein Conformation, Transcription Factors, Drug Design, Molecular Dynamics Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
