رابطه کمی ساختار فعالیت تعدادی از مشتق های فلاونوئیدی به عنوان مهارکننده های استیل‌کولین‌استراز برپایه الگوریتم مونت کارلو
محورهای موضوعی : شیمی تجزیهزهرا نظری 1 , شهین احمدی 2 * , علی الماسی راد 3
1 - دانشجوی دکترای عمومی داروسازی، گروه شیمی دارویی، دانشکده داروسازی، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران.
2 - دانشیار گروه شیمی دارویی، دانشکده شیمی دارویی، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران.
3 - استاد گروه شیمی دارویی، دانشکده داروسازی، علوم پزشکی تهران، دانشگاه آزاد اسلامی، تهران، ایران.
کلید واژه: QSAR, فلاونوئیدها, مهارکننده¬های استیل‌کولین‌استراز, SMILES, شاخص آرمانی همبستگی.,
چکیده مقاله :
درسالهای اخیر، روشی مبتنی بر رویکرد مستقل از بهینه‌سازی مولکول برپایه توصیفگرهای ساختاری مولکولی و توپولوژیکی، بهعنوان یک رویکرد جذاب در مدلسازی QSAR معرفی شده است. یکی از ویژگیهای کلیدی این رویکرد به استفاده از توصیفگرها برپایه سامانه ورودی-خط ورودی-مولکولی ساده‌شده (SMILES) برای توسعه مدل QSAR اشاره دارد. در این پژوهش، با هدف مطالعه و بررسی QSAR برخی از مشتقهای فلاونوئیدی بهعنوان مهارکنندههای استیل‌کولین‌استراز برپایه الگوریتم مونت کارلو، مجموعهای از 88 مهارکننده استیل‌کولین‌استراز از مقالههای معتبر انتخاب شد. ساختار ترکیبهای با نماد SMILES بهعنوان فایلهای ورودی نرم افزار تعریف و بهطور تصادفی به چهار سری شامل آموزش، آموزش نامرئی، واسنجی و مجموعه اعتبارسنجی تقسیم و مدلسازی انجام شد. توصیفگرهای بهینه مورداستفاده در مدل از ترکیب SMILES و نمودار بدون هیدروژن برپایه تابع هدف جدید انتخاب شدند. قدرت پیش‌بینی مدل‌ها با مجموعه‌های اعتبارسنجی، ارزیابی شد. نتیجههای بهدست‌آمده از چهار سری تصادفی نشان داد که مدل‌ها پیشگو و قابل اعتماد از لحاظ آماری، هستند. توصیفگرهای بااهمیت افزاینده و کاهنده فعالیت مهارکنندگی استیل‌کولین‌استراز مشتقهای فلاونوئید شناسایی شدند. همچنین، pIC50 تعداد زیادی از مشتقهای فلاونوئید طبیعی مهارکننده آنزیم استیل‌کولین‌استراز محاسبه و گزارش شد.
A method based on an independent approach of molecule optimization based on structural and topological molecular features has recently been introduced as an attractive approach in the QSAR modeling. A key feature of this approach refers to the use of descriptors based on the Simplified Molecular Input Line Input System (SMILES) to develop the QSAR model. In this research, by studying the QSAR of some flavonoid derivatives as acetylcholinesterase inhibitors based on the Monte Carlo algorithm, a set of 88 acetylcholinesterase inhibitors was selected from peer reviewed papers. The structures of the compounds in SMILES format were defined as software input files and randomly divided into four series including training, invisible training, calibration and validation sets and modeling was done. The optimal descriptors used in the model were selected from the combination of SMILES and hydrogen suppressor graph based on a new objective function. Finally, the predictive power of models was evaluated by validation sets. The results of four random series showed that predictive and statistically reliable models were obtained. The most important descriptors that increase and decrease the acetylcholinesterase inhibitory activity of flavonoid derivatives were identified. Finally, acetylcholinesterase inhibitory (pIC50) of a large number of natural flavonoid derivatives were calculated and reported. Using the obtained model, it is possible to predict pIC50 of flavonoid derivatives as acetylcholinesterase enzyme inhibitors using Monte Carlo and SMILES optimization methods
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