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
Support vector machines for the estimation of aqueous solubility.
TL;DR: Support Vector Machines are used to estimate aqueous solubility of organic compounds with accuracy comparable to results from other reported methods where the same data sets have been studied.
Journal ArticleDOI
Classifying 'drug-likeness' with kernel-based learning methods.
Klaus-Robert Müller,Gunnar Rätsch,Sören Sonnenburg,Sebastian Mika,Michael Grimm,Nikolaus Heinrich +5 more
TL;DR: A successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the substance is reported about.
Journal ArticleDOI
Discovery of Novel Pim-1 Kinase Inhibitors by a Hierarchical Multistage Virtual Screening Approach Based on SVM Model, Pharmacophore, and Molecular Docking
Ji-Xia Ren,Lin-Li Li,Ren-Lin Zheng,Huan-Zhang Xie,Zhi-Xing Cao,Shan Feng,You-Li Pan,Xin Chen,Yuquan Wei,Shengyong Yang +9 more
TL;DR: Evaluation of this approach indicates that it can be used to screen a large chemical library rapidly with a high hit rate and a high enrichment factor, and was then applied to screen several large chemical libraries, including PubChem, Specs, and Enamine as well as an in-house database.
Journal ArticleDOI
One- to four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties.
Chloé-Agathe Azencott,Alex Ksikes,S. Joshua Swamidass,Jonathan H. Chen,Liva Ralaivola,Pierre Baldi +5 more
TL;DR: Rich molecular representations including 1D SMILES strings, 2D graphs of bonds, and 3D coordinates are leveraged to derive efficient machine learning kernels to address regression problems and improve the performance of kernels based on the three-dimensional structure of molecules, especially on challenging data sets.
Journal ArticleDOI
Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds
TL;DR: In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning.