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

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.