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.