Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model
Jiangcheng Xu, Yun Zhang, Jiale Han, An Su, Haoran Qiao, Chengyun Zhang, Jing Tang, Xi Shen, Bin Sun, Wenbo Yu, Silong Zhai, Xinqiao Wang, Yejian Wu, Weike Su, Hongliang Duan
Abstract
Transformer, a sequence-to-sequence deep learning model, is capable of predicting the reaction intermediates of radical cascade cyclization. This study provides a novel approach to help chemists discover the mechanisms of organic reactions.
Topics & Concepts
CascadeChemistryTransformerSequence (biology)Combinatorial chemistryArtificial intelligenceComputer scienceChromatographyEngineeringBiochemistryElectrical engineeringVoltageMachine Learning in Materials ScienceComputational Drug Discovery MethodsChemical Synthesis and Analysis