Graph Kernels for Molecular Structure−Activity Relationship Analysis with Support Vector Machines
نویسندگان
چکیده
منابع مشابه
Graph Kernels for Molecular Structure-Activity Relationship Analysis with Support Vector Machines
The support vector machine algorithm together with graph kernel functions has recently been introduced to model structure-activity relationships (SAR) of molecules from their 2D structure, without the need for explicit molecular descriptor computation. We propose two extensions to this approach with the double goal to reduce the computational burden associated with the model and to enhance its ...
متن کاملQSAR Analysis with Support Vector Machines and Graph Kernels
Kernel methods, such as support vector machines, have been applied to solving various problems in bioinformatics. Recently, marginalized kernels between labeled graphs have been proposed [2, 3], which enable the application of kernel methods to the analysis and classification of chemical compounds such as QSAR (quantitative structure-activity relationship). These graph kernels are based on the ...
متن کاملSupport vector machines with indefinite kernels
Training support vector machines (SVM) with indefinite kernels has recently attracted attention in the machine learning community. This is partly due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite, i.e. the Mercer condition is not satisfied, or the Mercer condition is difficult to verify. Previous work on training SVM with indefinite ke...
متن کاملData-driven Kernels for Support Vector Machines
Kernel functions can map data points to a non-linear feature space implicitly, and thus significantly enhance the effectiveness of some linear models like SVMs. However, in current literature, there is a lack of a proper kernel that can deal with categorical features naturally. In this paper, we present a novel kernel for SVM classification based on data-driven similarity measures to compute th...
متن کاملFast Support Vector Machines for Structural Kernels
In this paper, we propose three important enhancements of the approximate cutting plane algorithm (CPA) to train Support Vector Machines with structural kernels: (i) we exploit a compact yet exact representation of cutting plane models using directed acyclic graphs to speed up both training and classification, (ii) we provide a parallel implementation, which makes the training scale almost line...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Chemical Information and Modeling
سال: 2005
ISSN: 1549-9596,1549-960X
DOI: 10.1021/ci050039t