Automated retention time prediction of new psychoactive substances in gas chromatography

Abstract

New psychoactive substances (NPSs) are drugs that exhibit chemical structures and pharmacological actions similar to illegal drugs. Because the health hazards caused by these drugs of abuse have become a social problem, the quick identification of NPSs is necessary for rapid regulation. We developed an automated machine learning system to predict the retention time of the gas chromatography-mass spectrometry for NPSs. The use of an automated machine learning system and Mordred that calculates molecular descriptors enables accurate retention time prediction. Because the retention time prediction is crucial for identifying NPSs, our research can contribute to the prevention of health hazards.