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Competitive Genomic Screens of Barcoded Yeast Libraries

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Combating Counterfeit Drugs via Machine Learning-Enabled Array Screening of Multilayer Evolutionary Combinatorial

Huihai Li¹, Hao Chen², Weiwei Ni¹

¹State Key Laboratory of Natural Medicines, National R&D Center for Chinese Herbal Medicine Processing, Jiangsu Key Laboratory of Drug Design and Optimization, College of Engineering, China Pharmaceutical University, Nanjing 211109, China.

ACS Applied Materials & Interfaces

|September 12, 2025

Summary

This summary is machine-generated.

A new machine learning strategy rapidly identifies optimal sensor arrays for detecting counterfeit nonsteroidal anti-inflammatory drugs (NSAIDs). This method ensures 100% accuracy in distinguishing genuine from fake NSAIDs within minutes.

Keywords:

combinatorial library identification of counterfeit drugs machine learning nonsteroidal anti-inflammatory drugs sensor array

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Area of Science:

Analytical Chemistry
Materials Science
Computational Chemistry

Background:

Counterfeit drugs pose significant global health risks, impacting patient morbidity and mortality.
Sensor arrays offer potential for discriminating drug molecules but face challenges in rapid library generation.
Developing efficient methods to identify optimal sensor combinations is crucial for drug authenticity verification.

Purpose of the Study:

To develop a machine learning-guided strategy for rapidly generating optimal sensor arrays.
To identify a minimal set of sensing elements for detecting counterfeit nonsteroidal anti-inflammatory drugs (NSAIDs).
To establish a foundation for broad application in drug authenticity verification.

Main Methods:

A three-layer screening strategy guided by machine learning was employed.
A combinatorially designed library of 100 candidate sensing elements was synthesized and screened.
A pruned 5-element array was constructed and validated for NSAID discrimination.

Main Results:

The 5-element array achieved 100% accuracy in distinguishing nine NSAIDs and their analogs.
Quantitative and multiplexed differentiation of two key NSAIDs was successfully demonstrated.
The array accurately identified five commercial over-the-counter (OTC) NSAIDs from two counterfeit versions within 5 minutes with 100% accuracy.

Conclusions:

The machine learning-guided strategy enables rapid construction of optimal combinatorial sensing libraries.
The developed sensor array provides a highly accurate and efficient method for counterfeit NSAID detection.
This approach lays the groundwork for versatile drug authenticity verification systems.