کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7633083 | 1494665 | 2012 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparative evaluation of pKa prediction tools on a drug discovery dataset
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Due to their impact on pharmacokinetic and pharmacodynamic properties the accurate prediction of dissociation constants is of outmost importance in drug discovery settings. The prediction accuracy, however, is typically assessed on public datasets most likely included in the training sets of the available tools. In this work we therefore tested five pKa prediction softwares such as ACD, Epik, Marvin, PharmaAlgorithm and Pallas on novel, never-published compounds. Our dataset consists of 177Â pKa values of 95 structurally diverse in-house compounds prepared for real-life drug discovery programs. The thorough analysis of prediction accuracy allowed us identifying the best practice and exploring the limitations of the current methods. Mean absolute errors (0.86-1.28) obtained for this set of discovery compounds indicates the potential in the improvement of the available pKa prediction approaches. Limitations were further characterized by measuring and evaluating 39Â pKa values of additional 28 commercially available compounds representing the most challenging chemotypes. We believe that these results would facilitate further developments and hopefully contribute to the necessary improvement of the prediction accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Pharmaceutical and Biomedical Analysis - Volumes 67â68, AugustâSeptember 2012, Pages 63-70
Journal: Journal of Pharmaceutical and Biomedical Analysis - Volumes 67â68, AugustâSeptember 2012, Pages 63-70
نویسندگان
György T. Balogh, Ákos Tarcsay, György M. Keserű,