GPT4 aided biomaterials research use case: stabilization of selenium nanoparticles with proteins Abstract
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Abstract
Recent advancements in LLMs based on various transformer architectures such as BERT and GPT family models, brought many new possibilities for application in scientific research. The specific architecture and broad knowledge of these models give them the ability to understand concepts, to plan and solve different kinds of problems, including various chemistry-related tasks. In this work, we are evaluating a case of GPT4 performance for recommending proteins suitable for the stabilization of selenium nanoparticles (SeNPs). SeNPs exhibit diverse beneficial bioactivities, including antioxidant, antibacterial, and anticancer properties, and stabilization of SeNPs with suitable proteins may be an effective approach to improve their bioactivities.
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References
J. Yang, H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, B. Yin, X. Hu, Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond, arXiv:2304.13712 27 April 2023, https://doi.org/10.48550/arXiv.2304.13712
A. D. White, G. M. Hocky, H. A. Gandhi, M. Ansari, S. Cox, G. P. Wellawatte, S. Sasmal, Z. Yang, K. Liu, Y. Singh, W. J. Peña Ccoa, Assessment of chemistry knowledge in large language models that generate code. ChemRxiv 2022 https://doi.org/10.26434/chemrxiv-2022-3md3n-v2
K. Hatakeyama-Sato, N. Yamane, Y. Igarashi, Y. Nabae & T. Hayakawa, Prompt engineering of GPT-4 for chemical research: what can/cannot be done?, STAM Methods, 3:1, 2260300, 2023, https://doi.org/10.1080/27660400.2023.2260300