Generative artificial intelligence performs rudimentary structural biology modeling
Open accessPublished version of a preprint
Abstract
Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
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 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
UCSF ChimeraX : Tools for structure building and analysis
Meng, E. C., Goddard, T. D., Pettersen, E. F., Couch, G. S., Pearson, Z., Morris, J. H., & Ferrin, T. E. (2023) · Protein Science · doi:10.1002/pro.4792 · Cited by 4,575 · see citing works
The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain
Pauling, L. C., Corey, R. B., & Branson, H. R. (1951) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.37.4.205 · Cited by 2,919 · see citing works
Accurate Bond and Angle Parameters for X-ray Protein Structure Refinement
Engh, R., & Huber, R. (1991) · Acta Crystallographica Section A Foundations of Crystallography · doi:10.1107/s0108767391001071 · Cited by 2,497 · see citing works
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329 · see citing works
Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19
Hammond, J., Leister-Tebbe, H., Gardner, A., Abreu, P., Bao, W., Wisemandle, W., Baniecki, M., Hendrick, V. M., Damle, B., Simón-Campos, A., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2118542 · Cited by 2,323 · see citing works
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · see citing works
ChatGPT: five priorities for research
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023) · Nature · doi:10.1038/d41586-023-00288-7 · Cited by 1,842 · see citing works
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712 · see citing works
A Helix Propensity Scale Based on Experimental Studies of Peptides and Proteins
Pace, C. N., & Scholtz, J. M. (1998) · Biophysical Journal · doi:10.1016/s0006-3495(98)77529-0 · Cited by 1,074 · see citing works
Autonomous chemical research with large language models
Boiko, D. A., MacKnight, R., Kline, B., & Gomes, G. (2023) · Nature · doi:10.1038/s41586-023-06792-0 · Cited by 1,050 · see citing works
Shapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000 · see citing works
Critical assessment of methods of protein structure prediction (CASP)—Round <scp>XIV</scp>
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26237 · Cited by 503 · see citing works
Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir
Iketani, S., Mohri, H., Culbertson, B., Hong, S. J., Duan, Y., Luck, M. I., Annavajhala, M. K., Guo, Y., Sheng, Z., Uhlemann, A., et al. (2022) · Nature · doi:10.1038/s41586-022-05514-2 · Cited by 498 · see citing works
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455 · see citing works
Single-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455 · see citing works
Emergent analogical reasoning in large language models
Webb, T., Holyoak, K. J., & Lu, H. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01659-w · Cited by 404 · see citing works
Leveraging large language models for predictive chemistry
Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A., & Smit, B. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00788-1 · Cited by 378 · see citing works
AI-based structure prediction empowers integrative structural analysis of human nuclear pores
Mosalaganti, S., Obarska-Kosinska, A., Siggel, M., Taniguchi, R., Turoňová, B., Zimmerli, C. E., Buczak, K., Schmidt, F. H., Margiotta, E., Mackmull, M.-T., et al. (2022) · Science · doi:10.1126/science.abm9506 · Cited by 355 · see citing works
Naturally Occurring Mutations of SARS-CoV-2 Main Protease Confer Drug Resistance to Nirmatrelvir
Hu, Y., Lewandowski, E. M., Tan, H., Zhang, X., Morgan, R. T., Zhang, X., Jacobs, L. M. C., Butler, S. G., Gongora, M. V., Choy, J. S., et al. (2023) · ACS Central Science · doi:10.1021/acscentsci.3c00538 · Cited by 348 · see citing works
AlphaFold2 and the future of structural biology
Cramer, P. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00650-1 · Cited by 325 · see citing works
The discovery of the α-helix and β-sheet, the principal structural features of proteins
Eisenberg, D. S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2034522100 · Cited by 299 · see citing works
Hagendorff, T., Fabi, S., & Kosinski, M. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00527-x · Cited by 243 · see citing works
Crystal structure of SARS-CoV-2 main protease in complex with protease inhibitor PF-07321332
Zhao, Y., Fang, C., Zhang, Q., Zhang, R., Zhao, X., Duan, Y., Wang, H., Zhu, Y., Feng, L., Zhao, J., et al. (2021) · Protein & Cell · doi:10.1007/s13238-021-00883-2 · Cited by 235 · see citing works
Nirmatrelvir-resistant SARS-CoV-2 variants with high fitness in an infectious cell culture system
Zhou, Y., Gammeltoft, K. A., Ryberg, L. A., Pham, L. V., Tjørnelund, H. D., Binderup, A., Hernandez, C. R. D., Fernandez-Antunez, C., Offersgaard, A., Fahnøe, U., et al. (2022) · Science Advances · doi:10.1126/sciadv.add7197 · Cited by 227 · see citing works
Use of gpt-4 to diagnose complex clinical cases
Eriksen, A. V., Möller, S., & Ryg, J. (2024) · NEJM AI · doi:10.1056/aip2300031 · Cited by 200 · see citing works
Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold
Fontana, P., Dong, Y., Pi, X., Tong, A. B., Hecksel, C. W., Wang, L., Fu, T.-M., Bustamante, C., & Wu, H. (2022) · Science · doi:10.1126/science.abm9326 · Cited by 195 · see citing works
ChatGPT broke the Turing test — the race is on for new ways to assess AI
Biever, C. (2023) · Nature · doi:10.1038/d41586-023-02361-7 · Cited by 153 · cited 1× · see citing works
“So-called reasoning, in this regard, refers to the documented performance of benchmark reasoning exercises by GPTs [19, 20], and it should be noted that there is ongoing debate about what constitutes reasoning as it pertains to AI [57, 58].”
Fujii, N., & Saito, T. (2004) · The Chemical Record · doi:10.1002/tcr.20020 · Cited by 138 · see citing works
The impact of AlphaFold Protein Structure Database on the fields of life sciences
Váradi, M., & Velankar, S. S. (2022) · PROTEOMICS · doi:10.1002/pmic.202200128 · Cited by 132 · see citing works
Hirosawa, T., Kawamura, R., Harada, Y., Mizuta, K., Tokumasu, K., Kaji, Y., Suzuki, T., & Shimizu, T. (2023) · JMIR Medical Informatics · doi:10.2196/48808 · Cited by 130 · see citing works
Bonner, W. A. (1998) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-8837-0_10 · Cited by 116 · see citing works
Heinz, D. W., Baase, W. A., & Matthews, B. W. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.9.3751 · Cited by 113 · see citing works
ChatGPT, Bard, and large language models for biomedical research: opportunities and pitfalls
Thapa, S., & Adhikari, S. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03284-0 · Cited by 111 · see citing works
Conformation Dependence of Backbone Geometry in Proteins
Berkholz, D. S., Shapovalov, M. V., Dunbrack, R. L., & Karplus, P. A. (2009) · Structure · doi:10.1016/j.str.2009.08.012 · Cited by 107 · see citing works
AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
Bordin, N., Sillitoe, I., Nallapareddy, V., Rauer, C., Lam, S. D., Waman, V. P., Sen, N., Heinzinger, M., Littmann, M., Kim, S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04488-9 · Cited by 103 · see citing works
Zuckerman, N. S., Bucris, E. D., Keidar-Friedman, D., Amsalem, M., & Brosh‐Nissimov, T. (2023) · Clinical Infectious Diseases · doi:10.1093/cid/ciad494 · Cited by 103 · see citing works
ChatGPT in Research: Balancing Ethics, Transparency and Advancement
Graf, A., & Bernardi, R. E. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.02.008 · Cited by 91 · see citing works
Drug discovery companies are customizing ChatGPT: here’s how
Savage, N. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01788-7 · Cited by 86 · see citing works
Chakraborty, C., Bhattacharya, M., & Lee, S. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.08.009 · Cited by 81 · see citing works
Hirotsu, Y., Kobayashi, H., Kakizaki, Y., Saito, A., Tsutsui, T., Kawaguchi, M., Shimamura, S., Hata, K., Hanawa, S., Toyama, J., et al. (2023) · Med · doi:10.1016/j.medj.2023.08.001 · Cited by 77 · see citing works
Ten quick tips for harnessing the power of ChatGPT in computational biology
Lubiana, T., Lopes, R., Medeiros, P., Silva, J. C. S. E., Gonçalves, A. N. A., Maracaja‐Coutinho, V., & Nakaya, H. I. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011319 · Cited by 76 · see citing works
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72 · see citing works
Emergent autonomous scientific research capabilities of large language models
Boiko, D. A., MacKnight, R., & Gomes, G. (2023) · arXiv · doi:10.48550/arxiv.2304.05332 · Cited by 72 · see citing works
Xiao, Z., Li, W., Moon, H., Roell, G. W., Chen, Y., & Tang, Y. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00310 · Cited by 65 · see citing works
Harnessing large language models (LLMs) for candidate gene prioritization and selection
Toufiq, M., Rinchai, D., Bettacchioli, É., Kabeer, B. S. A., Khan, T., Subba, B., White, O., Yurieva, M., George, J., Jourde‐Chiche, N., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04576-8 · Cited by 64 · see citing works
Empowering beginners in bioinformatics with ChatGPT
Shue, E., Liu, L., Li, B.-X., Feng, Z.-F., Li, X., & Hu, G.-Q. (2023) · bioRxiv · doi:10.15302/j-qb-023-0327 · Cited by 62 · see citing works
D ‐amino acid residues in peptides and proteins
Mitchell, J. B. O., & Smith, J. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10320 · Cited by 59 · see citing works
ChatGPT in Drug Discovery: A Case Study on Anticocaine Addiction Drug Development with Chatbots
Wang, R., Feng, H., & Wei, G. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01429 · Cited by 56 · see citing works
Leveraging pre-trained language models for mining microbiome-disease relationships
Karkera, N., Acharya, S., & Palaniappan, S. K. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05411-z · Cited by 41 · see citing works
Burley, S. K., & Berman, H. M. (2021) · Structure · doi:10.1016/j.str.2021.04.010 · Cited by 40 · see citing works
Self-citationChatterjee, S., Bhattacharya, M., Dhama, K., Lee, S., & Chakraborty, C. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.03.013 · Cited by 40 · see citing works
Studying and improving reasoning in humans and machines
Yax, N., Anlló, H., & Palminteri, S. (2024) · Communications Psychology · doi:10.1038/s44271-024-00091-8 · Cited by 40 · see citing works
Thinking Fast and Slow in Large Language Models
Hagendorff, T., Fabi, S., & Kosiński, M. (2022) · PubMed Central · doi:10.48550/arxiv.2212.05206 · Cited by 36 · see citing works
Predicting Proteome-Scale Protein Structure with Artificial Intelligence
Burley, S. K., Arap, W., & Pasqualini, R. (2021) · New England Journal of Medicine · doi:10.1056/nejmcibr2113027 · Cited by 28 · see citing works
Self-citationThe impact of AlphaFold2 on experimental structure solution
Edich, M., Briggs, D. C., Kippes, O., Gao, Y., & Thorn, A. (2022) · Faraday Discussions · doi:10.1039/d2fd00072e · Cited by 28 · see citing works
Structure, stability and folding of the α-helix
Doig, A. J., Andrew, C. D., Cochran, D. A. E., Hughes, E., Penel, S., Sun, J. K., Stapley, B. J., Clarke, D. T., & Jones, G. R. (2001) · Biochemical Society Symposia · doi:10.1042/bss0680095 · Cited by 13 · see citing works
Eisenstein, M. S. (2023) · Nature · doi:10.1038/d41586-023-03875-w · Cited by 9 · see citing works
AI interprets the Central Dogma and Genetic Code
Ille, A. M., & Mathews, M. B. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2023.09.004 · Cited by 5 · see citing works
Self-citationThe impact of AlphaFold on experimental structure solutionPreprint
Edich, M., Briggs, D. C., Kippes, O., Gao, Y., & Thorn, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.07.487522 · Cited by 4 · see citing works
A test of artificial intelligence
Eisenstein, M. S. (2023) · Nature · doi:10.1038/d41586-023-02822-z · Cited by 3 · see citing works
Artificial intelligence utility for drug development: ChatGPT and beyond
Gurwitz, D., & Shomron, N. (2023) · Drug Development Research · doi:10.1002/ddr.22121 · Cited by 3 · see citing works
AlphaFold and the future of structural biology
Read, R. J., Baker, E. N., Bond, C. S., Garman, E. F., & van Raaij, M. J. (2023) · Acta Crystallographica Section F Structural Biology Communications · doi:10.1107/s2053230x23004934 · Cited by 3 · see citing works
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
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Sigot, M., & Tassoti, S. (2025) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c01287 · Cited by 21 · see citing works
Large language models transform biological research: from architecture to utilization
Wang, T., & Luo, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4466-3 · Cited by 14 · see citing works
Artificial Intelligence agents for biological research: a survey
Qi, C., Wang, W., Jiang, S., Liu, Q., Song, X., Fang, H., & Wei, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag075 · Cited by 11 · see citing works
AI-Powered Neurogenetics: Supporting Patient’s Evaluation with Chatbot
Zampatti, S., Farro, J., Peconi, C., Cascella, R., Strafella, C., Calvino, G., Megalizzi, D., Trastulli, G., Caltagirone, C., & Giardina, E. (2024) · Genes · doi:10.3390/genes16010029 · Cited by 7 · see citing works
Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery
Hu, Z., Hong, H., & Lin, T. (2026) · Biomimetics · doi:10.3390/biomimetics11050320 · Cited by 3 · see citing works
GL4SDA: Predicting snoRNA-disease associations using GNNs and LLM embeddings
Rosa, M. L., Fiannaca, A., Mendolia, I., Paglia, L. L., & Urso, A. M. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.014 · Cited by 3 · see citing works
Computational approaches for designing viral protease inhibitors
Bonardi, A. (2025) · The Enzymes · doi:10.1016/bs.enz.2025.06.005 · Cited by 2 · see citing works
ADAR-GPT : A continually fine-tuned language model for predicting A-to-I RNA editing sites
Rosenwasser, Z., Cohen‐Fultheim, R., Levitt, M., Levanon, E. Y., & Oren, G. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2529073123 · Cited by 2 · see citing works
Duncan, M., Sylvester, T. P., Walden, E., Lozano, J. R., Turner, E., Duncan, S., Mitchell, R. F., McKenna, D. D., & Adams, R. (2026) · Insects · doi:10.3390/insects17060587 · Cited by 1 · see citing works
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Qiao, N., Ishikawa, J., Yasui, F., Sano, K., Miyakawa, K., Hayashi, A., Amiry, A. F., Haonan, L., Xueyang, Z., Sato, T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.24.696305
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- Authors
- Ille, A. M., Markosian, C., Burley, S. K., Mathews, M. B., Pasqualini, R., & Arap, W.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Rutgers Cancer Institute (US)
- Corresponding author
- Pasqualini, Renata
- Arap, Wadih
- ORCID
- Venue
- Scientific Reports
- Published
- 2024-08-21
- Volume
- 14
- Issue
- 1
- Pages
- 19372-19372
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11339285
- OpenAlex
- W4401759647
- Cited by
- 16
- References
- 69
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 1 · 2023: 2
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (DBI-1832184)
- U.S. Department of Energy (DE-SC0019749)
- National Institutes of Health (R01GM133198, R01CA226537)
- Levy-Longenbaugh Donor-Advised Fund
- Rutgers Cancer Institute (P30CA072720)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI, GPT-4, protein structure prediction, α-helical polypeptides, nirmatrelvir, main protease, large language models
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 ›
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