Related Experiment Video
Updated: Nov 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
A graph-convolutional neural network for addressing small-scale reaction prediction
Yejian Wu1, Chengyun Zhang1, Ling Wang1
1Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China. hduan@zjut.edu.cn.
Abstract:
We describe a graph-convolutional neural network (GCN) model, the reaction prediction capabilities of which are as potent as those of the transformer model based on sufficient data, and we adopt the Baeyer-Villiger oxidation reaction to explore their performance differences based on limited data. The top-1 accuracy of the GCN model (90.4%) is higher than that of the transformer model (58.4%).
Related Concept Videos
Predicting Reaction Outcomes
Standard Entropy Change for a Reaction
Measuring Reaction Rates
Convolution: Math, Graphics, and Discrete Signals
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Coupled Reactions
Energy in adenosine triphosphate or ATP molecules is easily accessible to do work. ATP powers the majority of energy-requiring cellular reactions....