Journal ArticleDOI
Support vector machines for drug discovery.
Kathrin Heikamp,Jürgen Bajorath +1 more
TLDR
SVMs are currently among the best-performing approaches for chemical and biological property prediction and the computational identification of active compounds and it is anticipated that their use in drug discovery will further increase.Abstract:
Introduction: Support vector machines (SVMs) are supervised machine learning algorithms for binary class label prediction and regression-based prediction of property values. In recent years, SVMs h...read more
Citations
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Book ChapterDOI
In Silico Drug-Target Profiling
Jean-Yves Trosset,Christian Cavé +1 more
TL;DR: This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.
Journal ArticleDOI
Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods
TL;DR: In this article, 14 quantitative structure-activity relationship (QSAR) models were built by four machine learning methods, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF) and deep neural networks (DNlN), for the best model Model2G constructed by DNN algorithm, the coefficient of determination (R2) of 0.88 and 0.51 were obtained on training set and test set, respectively.
Journal ArticleDOI
Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine
Zhan Tong,Yuan Zhou,Juan Wang +2 more
TL;DR: Performance evaluation results suggested the in-silico HCC drug target predictor could effectively identify putative drug target genes for further research.
Journal ArticleDOI
Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis.
Xin Wang,Alexander L. Perryman,Shao-Gang Li,Steve D. Paget,Thomas P. Stratton,Alex Lemenze,Arthur J. Olson,Sean Ekins,Pradeep Kumar,Joel S. Freundlich +9 more
TL;DR: A new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying antitubercular mechanism of action is demonstrated.
Journal ArticleDOI
Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery.
TL;DR: This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future and CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.