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January 1, 2026RSC AdvancesOpen Access

Prediction of gas chromatographic retention times of narcotic and hazardous drugs in blood using QSRR and machine learning models

Mohamed Abu Shuheil Ahmed Aldulaimi Subhashree Ray Talal Aziz Qassem Gunjan Garg Rohit Sharma Dilbar Urazbaeva Sabokhat Sadikova Milad Safamanesh

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Key Points

This research aims to predict gas chromatographic retention times for narcotic drugs in blood using machine learning techniques.
Applied quantitative structure-activity relationship (QSRR) approach.
Utilized artificial neural networks (ANN) for prediction models.
Analyzed data to derive retention times of various narcotic drugs.
Achieved a significant correlation with R test 2 = 0.932 for predictions.
Demonstrated high accuracy in predicting retention times for narcotic drugs.

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Implication

Machine learning predicts gas chromatographic retention times in blood for narcotic drugs, indicating high accuracy.

Abstract

Machine learning-based QSRR prediction of GC retention times for narcotic drugs in blood (ANN R test 2 = 0.932).

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Cite This Study

Shuheil et al. (2026) studied this question.

synapsesocial.com/papers/69cd7b065652765b073a8bd5 https://doi.org/10.1039/d5ra10111e

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