Deep retrosynthetic reaction prediction using local reactivity and global attention

2021年8月5日 · Our model shows a promising 89.5 and 99.2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions. We further demonstrate the validity of LocalRetro on a large dataset containing 479 035 reactions (UTPTO-MIT) with comparable round-trip top-1 and top-5 accuracy of 87.0 and 97.4%, respectively.

S Chen and Y Jung, “Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention”, JACS Au 2021; Conference - Talks. S Chen and Y Jung,”Automatic generation of mechanistic pathways of organic reactions”, ACS Fall Meeting, online, 2024

Shuan Chen Korea Advanced Institute of Science and Technology: Daejeon, Daejeon, KR 2018-08 to 2020-07 | Master's degree (Chemical and biomolecular engineering)

2021年8月5日 · Segler and Waller proposed a hybrid neural-symbolic approach to predict the retrosynthesis pathway using either hand-coded or automatically extracted rules by a deep neural network.

Segler and Waller11 proposed a hybrid neural-symbolic approach to predict the retrosynthesis pathway using either hand-coded or automatically extracted rules by a deep neural network.

In this study, deep learning models based on hyperspectral data and time-series phenotypes from the high-throughput plant phenotyping (HTPP) platform were proposed to predict quality attributes...

Shuan Chen Affiliation Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

Shuan Chen, Yousung Jung: A generalized-template-based graph neural network for accurate organic reactivity prediction. Nat. Mac. Intell. 4 (9): 772-780 (2022)

2023年8月6日 · This study describes the initial development of a deep learning algorithm, ROP.AI, trained to automatically diagnose ROP plus disease. Our results have shown that a deep learning algorithm can successfully diagnose this form of …