High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning
Peikun Zheng, Olexandr Isayev
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Ring Vault contains 201 546 cyclic molecules across 11 elements.
Abstract
Ring Vault contains 201 546 cyclic molecules across 11 elements. AIMNet2 with 3D information outperformed 2D models in predicting the electronic properties of cyclic molecules.
Keywords
Cite This Paper
@article{Zheng2025a,
author = {Zheng, Peikun and Isayev, Olexandr},
title = {High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning},
year = {2025},
journal = {Chemical Science},
volume = {16},
number = {43},
pages = {20553--20563},
doi = {10.1039/d5sc04079e},
url = {http://dx.doi.org/10.1039/D5SC04079E},
publisher = {Royal Society of Chemistry (RSC)},
keywords = {machine learning, high-throughput},
highlight = {Ring Vault contains 201 546 cyclic molecules across 11 elements.}
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