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TransIA: A transformer-based few-shot learning framework for detecting PDE5 inhibitor derivatives.

Wanhao Sun¹, Xihe Yang¹, Neng Xiong²

¹Department of Chemistry, Zhejiang University, Hangzhou, Zhejiang 310027, China; Institute of Fundamental and Transdisciplinary Research, Zhejiang University, Hangzhou, Zhejiang 310027, China.

Journal of Hazardous Materials

|July 3, 2026

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A new AI tool, TransIA, accurately detects illicit phosphodiesterase-5 inhibitor (PDE5I) derivatives in food and environmental samples. This transformer-based network enhances food safety and environmental monitoring by identifying previously unknown compounds.

Area of Science:

Analytical Chemistry
Computational Chemistry
Food Science

Background:

Illicit additives (IAs), particularly phosphodiesterase-5 inhibitor (PDE5I) derivatives, are increasingly found in food and environmental samples.
Conventional detection methods struggle to identify these IAs due to their designed evasion strategies.
There is a critical need for rapid and accurate screening tools for food safety and environmental monitoring.

Purpose of the Study:

To develop a cheminformatics tool for the rapid and accurate identification of PDE5Is from raw mass spectra.
To address the growing health concerns associated with illicit PDE5I derivatives in various matrices.
To improve food safety and environmental monitoring capabilities.

Main Methods:

Development of TransIA (Transformer-based Illicit Additives Detection Network), a deep learning model utilizing transformer architecture.

Keywords:

Deep learning Few-shot learning Illicit additives Mass spectrometry Phosphodiesterase-5 inhibitor

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Training and testing the model on raw mass spectra data.

Validation across diverse environmental and biological matrices.

Deployment on real-world samples for IA identification.

Main Results:

TransIA achieved 98% accuracy on the testing set.
The model demonstrated a false-positive rate below 3%.
Analysis of 197 real-world samples identified 21 IAs, including 16 previously unreported or uncatalogued by the model.
Identified IAs included novel compounds and those not present in existing databases.

Conclusions:

TransIA provides a highly accurate and efficient method for detecting illicit PDE5I derivatives.
The developed approach significantly enhances the ability to identify novel and previously unknown illicit additives.
This technology holds potential for integration with portable mass spectrometry for on-site environmental analysis and improved public health protection.