کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7836968 | 1503885 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Development of CDK-targeted scoring functions for prediction of binding affinity
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی تئوریک و عملی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Cyclin-dependent kinase (CDK) is an interesting biological macromolecule due to its role in cell cycle progression, transcription control, and neuronal development, to mention the most studied biological activities. Furthermore, the availability of hundreds of structural studies focused on the intermolecular interactions of CDK with competitive inhibitors makes possible to develop computational models to predict binding affinity, where the atomic coordinates of binary complexes involving CDK and ligands can be used to train a machine learning model. The present work is focused on the development of new machine learning models to predict binding affinity for CDK. The CDK-targeted machine learning models were compared with classical scoring functions such as MolDock, AutoDock 4, and Vina Scores. The overall performance of our CDK-targeted scoring function was higher than the previously mentioned scoring functions, which opens the possibility of increasing the reliability of virtual screening studies focused on CDK.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biophysical Chemistry - Volume 235, April 2018, Pages 1-8
Journal: Biophysical Chemistry - Volume 235, April 2018, Pages 1-8
نویسندگان
Nayara Maria Bernhardt Levin, Val Oliveira Pintro, Gabriela Bitencourt-Ferreira, Bruna Boldrini de Mattos, Ariadne de Castro Silvério, Walter Filgueira Jr.,