GPT4 aided biomaterials research use case: stabilization of selenium nanoparticles with proteins Abstract

Main Article Content

Zoran Stojanović
https://orcid.org/0000-0002-5989-0031
Nenad Filipović
https://orcid.org/0000-0003-2261-8066
Maja Kuzmanović
https://orcid.org/0000-0002-8160-4804
Sara Lukač
https://orcid.org/0009-0009-9491-8712
Magdalena Stevanović
https://orcid.org/0000-0002-3989-0237

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.

Article Details

How to Cite
[1]
Z. . Stojanović, N. Filipović, M. Kuzmanović, S. . Lukač, and M. Stevanović, “GPT4 aided biomaterials research use case: stabilization of selenium nanoparticles with proteins: Abstract”, Hem Ind, vol. 78, no. 1S, p. 68, Mar. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://www.ache-pub.org.rs/index.php/HemInd/article/view/1315
Section
Various applications of novel materials

How to Cite

[1]
Z. . Stojanović, N. Filipović, M. Kuzmanović, S. . Lukač, and M. Stevanović, “GPT4 aided biomaterials research use case: stabilization of selenium nanoparticles with proteins: Abstract”, Hem Ind, vol. 78, no. 1S, p. 68, Mar. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://www.ache-pub.org.rs/index.php/HemInd/article/view/1315

References

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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

https://www.promptingguide.ai/

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