Journal
JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume 30, Issue 2, Pages 295-304Publisher
WILEY
DOI: 10.1002/jcc.21056
Keywords
fragment-based design; QSAR; drug design; H5N1; NA inhibitors
Categories
Funding
- National High-tech Research and Development Program ('863') of China [2006AA020103]
Ask authors/readers for more resources
In cooperation with the fragment-based design a new drug design method, the so-called fragment-based quantitative structure-activity relationship (FB-QSAR) is proposed. The essence of the new method is that file molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when file whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus. (C) 2008 Wiley Periodicals, Inc. J Comput Chem 30: 295-304, 2009
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available