In this section
@article{Hu:2024:10.3390/pharmaceutics16081014,
author = {Hu, J and Wu, P and Wang, S and Wang, B and Yang, G},
doi = {10.3390/pharmaceutics16081014},
journal = {Pharmaceutics},
title = {A human feedback strategy for photoresponsive molecules in drug delivery: utilizing GPT-2 and time-dependent density functional theory calculations},
url = {http://dx.doi.org/10.3390/pharmaceutics16081014},
volume = {16},
year = {2024}
}
TY - JOUR
AB - Photoresponsive drug delivery stands as a pivotal frontier in smart drug administration, leveraging the non-invasive, stable, and finely tunable nature of light-triggered methodologies. The generative pre-trained transformer (GPT) has been employed to generate molecular structures. In our study, we harnessed GPT-2 on the QM7b dataset to refine a UV-GPT model with adapters, enabling the generation of molecules responsive to UV light excitation. Utilizing the Coulomb matrix as a molecular descriptor, we predicted the excitation wavelengths of these molecules. Furthermore, we validated the excited state properties through quantum chemical simulations. Based on the results of these calculations, we summarized some tips for chemical structures and integrated them into the alignment of large-scale language models within the reinforcement learning from human feedback (RLHF) framework. The synergy of these findings underscores the successful application of GPT technology in this critical domain.
AU - Hu,J
AU - Wu,P
AU - Wang,S
AU - Wang,B
AU - Yang,G
DO - 10.3390/pharmaceutics16081014
PY - 2024///
SN - 1999-4923
TI - A human feedback strategy for photoresponsive molecules in drug delivery: utilizing GPT-2 and time-dependent density functional theory calculations
T2 - Pharmaceutics
UR - http://dx.doi.org/10.3390/pharmaceutics16081014
UR - https://www.mdpi.com/1999-4923/16/8/1014
VL - 16
ER -
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