Abstract
Fragment-based drug design has emerged as an important methodology for lead discovery and drug design. Different with other studies focused on fragment library design and active fragment identification, a fragment-based strategy was developed in combination with three-dimensional quantitative structure–activity relationship (3D-QSAR) for structural optimization in this study. Based on a validated scaffold or fragment hit, a series of structural optimization was conducted to convert it to lead compounds, including 3D-QSAR modelling, active site analysis, fragment-based structural optimization and evaluation of new molecules. 3D-QSAR models and active site analysis provided sufficient information for confirming the SAR and pharmacophoric features for fragments. This strategy was evaluated through the structural optimization on a c-Met inhibitor scaffold 5H-benzo[4,5]cyclohepta[1,2-b]pyridin-5-one, which resulted in an c-Met inhibitor with high inhibitory activity. Our study suggested the effectiveness of this fragment-based strategy and the druggability of our newly explored active region. The reliability of this strategy indicated it could also be applied to facilitate lead optimization of other targets.
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Acknowledgments
The authors thank Dr. Huifang Li (the University of British Columbia) for her help in drafting the manuscript. This work was supported by National Natural Science Foundation of China (81172933, 21102181 and 30973609); Fundamental Research Funds for the Central Universities (2J10004, JKZ2011004 and JKY2011020); Jiangsu Provincial Graduate Innovation Research Foundation (CXZZ12_0315); State Key Laboratory of Natural Medicines (China Pharmaceutical University) Foundation for major research projects (SKLNMZZ201205); and Specialized Research Fund for the Doctoral Program of Higher Education (No. 20100096110007).
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Haoliang Yuan and Wenting Tai have contributed equally to this work.
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Yuan, H., Tai, W., Hu, S. et al. Fragment-based strategy for structural optimization in combination with 3D-QSAR. J Comput Aided Mol Des 27, 897–915 (2013). https://doi.org/10.1007/s10822-013-9687-x
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DOI: https://doi.org/10.1007/s10822-013-9687-x