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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Journal ArticleDOI
Artificial Intelligence in Pharmaceutical Sciences
Mingkun Lu,Jiayi Yin,Qichao Zhu,Gaole Lin,Minjie Mou,Fuyao Liu,Ziqi Pan,Xichen Lian,Fengcheng Li,Hongning Zhang,Wei Zhang,Hanyu Zhang,Zihao Shen,Honglin Li,Feng Zhu +14 more
Journal ArticleDOI
Machine Learning Based Prediction and Optimization of Thin Film Nanocomposite Membranes for Organic Solvent Nanofiltration
Journal ArticleDOI
Machine Learning in Modeling of Mouse Behavior.
TL;DR: In this paper, the authors used logistic regression, support vector machines (SVM), random forest (RF), and one-dimensional convolutional neural networks paired with long short-term memory deep neural networks (1DConvBiLSTM) to predict mouse behavior with high accuracy.
Posted Content
Artificial Intelligence in Drug Discovery:Applications and Techniques
TL;DR: In this article, the authors present a survey on the state-of-the-art in artificial intelligence in drug discovery, including model architectures, learning paradigms, and data resources.
Journal ArticleDOI
Machine learning study: from the toxicity studies to tetrahydrocannabinol effects on Parkinson's disease.
TL;DR: In this article , a support vector machine (SVM) was used to predict the arylhydrocarbon receptor (AHR) activity of anti-Parkinson's and US FDA-approved drugs.
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.