Prediction of gas chromatographic retention times of narcotic and hazardous drugs in blood using QSRR and machine learning models
Mohamed Abu ShuheilFaculty of Allied Medical Sciences, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, JordanAhmed AldulaimiFaculty of Pharmacy, Al-Zahrawi University, Karbala, IraqSubhashree RayDepartment of Biochemistry, IMS and SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha-751003, IndiaTalal Aziz QassemDepartment of Medical Laboratory Technics, College of Health and Medical Technology, Alnoor University, Mosul, IraqGunjan GargCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, IndiaRenu SharmaDepartment of Chemistry, University Institute of Sciences, Chandigarh University, Mohali, Punjab, IndiaDilbar UrazbaevaDepartment of Psychology and Medicine, Mamun University, Khiva, UzbekistanSabokhat SadikovaDepartment of Chemistry, Urgench State University, 220100 Urgench, UzbekistanMilad SafamaneshYoung Researchers and Elite Club, Islamic Azad University, Tehran, Iran
ABI
Аннотация
= 0.969 for the training set and 0.932 for the test set) and the lowest prediction errors. Analysis of selected descriptors revealed that molecular hydrophobicity, structural complexity, hydrogen bonding capability, and three-dimensional molecular features play a significant role in chromatographic retention behavior. Overall, the proposed QSRR and machine learning framework enables accurate prediction of GC retention times, reduces the need for extensive experimental measurements, and offers an efficient tool for the screening and analysis of novel narcotic and hazardous drug derivatives.
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