CBMAFF-Net: An Intelligent NMR-Based Nontargeted Screening Method for New Psychoactive Substances

Abstract

With the proliferation and rapid evolution of new psychoactive substances (NPSs), traditional database-based search methods face increasing challenges in identifying NPS seizures with complex compositions, thereby complicating their regulation and early warning. To address this issue, CBMAFF-Net (CNN BiLSTM Multistep Attentional Feature Fusion Network) is proposed as an intelligent screening method to rapidly classify unknown confiscated substances using ¹³C nuclear magnetic resonance (NMR) and ¹H NMR data. Initially, we utilize the synergy of a convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) to extract the global and local features of the NMR data. These features are sequentially fused through a weighted approach guided by an attention mechanism, thoroughly capturing the essential NPS information. We evaluated the model on a generated simulated data set, where it performed with 99.8% accuracy and a 99.8% F1 score. Additionally, testing on 42 actual seizure cases yielded a recognition accuracy of 97.6%, significantly surpassing the performance of conventional database-based similarity search algorithms. These findings suggest that the proposed method holds substantial promise for the rapid screening and classification of NPSs.