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
ADMET modeling approaches in drug discovery.
TL;DR: In silico prediction of ADMET is an important component of pharmaceutical R&D and has advanced alongside the progress of chemoinformatics, which has evolved from traditional chemometrics to advanced machine learning methods.
Journal ArticleDOI
Machine Learning Methods in Drug Discovery.
TL;DR: In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed and the applications that produce promising results and methods will be reviewed.
Journal ArticleDOI
Quantitative structure-activity relationship: promising advances in drug discovery platforms.
TL;DR: The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization in the absence of 3D structures of specific drug targets.
Journal ArticleDOI
Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction
TL;DR: This work compared SVM and SVR calculations for the same compound data sets to evaluate which features are responsible for predictions and, on the basis of systematic feature weight analysis, rather surprising results were obtained.
Journal ArticleDOI
Drug/nondrug classification using Support Vector Machines with various feature selection strategies
TL;DR: Testing SVM as a classification tool in a real-life drug discovery problem revealed that it could be a useful method for classification task in early-phase of drug discovery.
References
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Journal ArticleDOI
Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
TL;DR: Although SVM outperformed the ANN classifiers with regard to overall prediction accuracy, both methods were shown to complement each other, as the sets of true positives, false positives, true negatives, and false negatives produced by the two classifiers were not identical.
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2005 Speical Issue: Graph kernels for chemical informatics
TL;DR: Three new graph kernels based on the idea of molecular fingerprints and counting labeled paths of depth up to d using depth-first search from each possible vertex are introduced, achieving performances at least comparable, and most often superior, to those previously reported in the literature.
Journal ArticleDOI
Protein-ligand interaction prediction
Laurent Jacob,Jean-Philippe Vert +1 more
TL;DR: Following the recent chemogenomics trend, this work adopts a cross-target view and attempts to screen the chemical space against whole families of proteins simultaneously, and reports dramatic improvements in prediction accuracy over classical ligand-based virtual screening, in particular for targets with few or no known ligands.
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Combining Global and Local Measures for Structure-Based Druggability Predictions
TL;DR: These findings for globalpocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction.
Journal ArticleDOI
Active Learning with support Vector machines in the drug discovery process
TL;DR: A thorough comparative study of various other selection strategies on data sets provided by DuPont Pharmaceuticals is performed and it is shown that the strategies based on the maximum margin hyperplane clearly outperform the simpler ones.