نام و نام خانوادگی مهدی قربان زاده شغل دانشجو تحصیلات 0 وبسایت پست الکترونیک ghorbanzade [at] yahoo [dot] com مقاله چاپ شده مقاله ارائه شده طرح پژوهشی خاتمه یافته کتاب برگزاری همایش سخنرانی برگزاری پانل تخصصی شرکت در کارگاهها یا همایشهای علمی کرسی نظریهپردازی کسب عنوان برتر پژوهشی ابداع، اختراع یا اکتشاف نشریات فعالیت های علمی اجرایی پایان نامه فرصت مطالعاتی جذب گرنت مشاوره یا مشارکت در کمیته علمی شرکت در برنامههای بازدید از جامعه و صنعت اثر بدیع و ارزنده هنری عنوانمجله 1 Predictions of Retention Factors for Some Organic Nucleuphiles in Complexation Gas Chromatography Journal of Chromatographic Science 2 Quantitative Structure Retention Relationship Modeling of Retention Time for Some Organic Pollutants Analytical Letters 3 Predictions of chromatographic retention indices of alkylphenols with support vector machines and multiple linear regression Journal of Separation Science 4 Classification of central nervous system agents by least squares support vector machine based on their structural descriptors: A comparative study Chemometrics and Intelligent Laboratory Systems 5 Modeling the Cellular Uptake of Magnetofluorescent Nanoparticles in Pancreatic Cancer Cells: A Quantitative Structure Activity Relationship Study Industrial & Engineering Chemistry Research 6 In silico prediction of free-radical chain transfer constants for some organic agents in styrene polymerization Monatshefte für Chemie - Chemical Monthly 7 Estimation of the volume of distribution of some pharmacologically important compounds from their structural descriptors Journal of the Serbian Chemical Society 8 Classification of drugs according to their milk/plasma concentration ratio European Journal of Medicinal Chemistry 9 Prediction of Aqueous Solubility of Drug-Like Compounds by Using an Artificial Neural Network and Least-Squares Support Vector Machine Bulletin of the Chemical Society of Japan 10 Quantitative and qualitative prediction of corneal permeability for drug-like compounds Talanta 11 In silico prediction of nematic transition temperature for liquid crystals using quantitative structure–property relationship approaches Molecular Diversity