PubCard - Chiral detection of biomolecules based on reinforcement learning

(Peer-Reviewed) Chiral detection of biomolecules based on reinforcement learning

Yuxiang Chen 陈宇翔 ¹, Fengyu Zhang 张凤宇 ² ⁴, Zhibo Dang 党郅博 ¹, Xiao He 何霄 ¹, Chunxiong Luo 罗春雄 ² ⁴, Zhengchang Liu 刘正昌 ³, Pu Peng 彭璞 ¹, Yuchen Dai 戴宇琛 ³, Yijing Huang 黄逸婧 ¹, Yu Li 李瑜 ³, Zheyu Fang 方哲宇 ¹ ³

¹ School of Physics, Peking University, Beijing 100871, China
中国北京北京大学物理学院
² The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics & Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
中国北京北京大学前沿交叉学科研究院定量生物学中心物理学院人工微结构和介观物理国家重点实验室
³ Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
中国北京北京大学前沿交叉学科研究院
⁴ Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
中国温州中国科学院大学温州研究院

Opto-Electronic Science, 2023-02-09

https://doi.org/10.29026/oes.2023.220019

https://www.oejournal.org/article/doi/10.29026/oes.2023.220019

Abstract

Chirality plays an important role in biological processes, and enantiomers often possess similar physical properties and different physiologic functions. In recent years, chiral detection of enantiomers become a popular topic.

Plasmonic metasurfaces enhance weak inherent chiral effects of biomolecules, so they are used in chiral detection. Artificial intelligence algorithm makes a lot of contribution to many aspects of nanophotonics. Here, we propose a nanostructure design method based on reinforcement learning and devise chiral nanostructures to distinguish enantiomers.

The algorithm finds out the metallic nanostructures with a sharp peak in circular dichroism spectra and emphasizes the frequency shifts caused by nearfield interaction of nanostructures and biomolecules. Our work inspires universal and efficient machine-learning methods for nanophotonic design.