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
In Silico Models to Discriminate Compounds Inducing and Noninducing Toxic Myopathy
Xiaoying Hu,Aixia Yan +1 more
TL;DR: A Kohonen’s self‐organizing map (SOM) and a support vector machine (SVM) were applied to develop classification models to differentiate IM‐compound and notIM‐compounds, and extended connectivity fingerprints were calculated and analyzed to find important substructures of molecules relating to toxic myopathy.
Journal ArticleDOI
A Novel Logic‐Based Approach for Quantitative Toxicology Prediction.
TL;DR: Support vector inductive logic programming (SVILP) as mentioned in this paper is a general approach, which extends the essentially qualitative ILP-based structure activity relationship (SAR) to quantitative modeling, and is used to learn rules, the predictions of which are then used within a novel kernel to derive a support vector generalization model.
Journal ArticleDOI
Potency-directed similarity searching using support vector machines
TL;DR: This work introduces an SVM approach for potency-directed virtual screening that met or exceeded the recall performance of standard SVM ranking and led to a notable enrichment of highly potent hits in database selection sets.