A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is
The prediction and evaluation of the biodegradability of molecules with computational methods are becoming increasingly important. Among the various methods, quantitative structure-activity relationship (QSAR) models have been demonstrated to predict the ready biodegradation of chemicals but have limited functionality owing to their complex implementation. In this study, we employ the graph convolutional network (GCN) method to overcome these issues. A biodegradability dataset from previousdoi:10.1021/acsomega.1c06274 pmid:35128273 pmcid:PMC8811760 fatcat:2o5kyifbtzcq3ciyjgv2k4f3we