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
SVM-based feature selection for characterization of focused compound collections.
Evgeny Byvatov,Gisbert Schneider +1 more
TL;DR: An SVM-based algorithm for the selection of relevant molecular features from a trained classifier that might be important for an understanding of ligand-receptor interactions is reported on.
Journal ArticleDOI
Lead Hopping Using SVM and 3D Pharmacophore Fingerprints
TL;DR: The combination of 3D pharmacophore fingerprints and the support vector machine classification algorithm has been used to generate robust models that are able to classify compounds as active or inactive in a number of G-protein-coupled receptor assays.
Journal ArticleDOI
Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data.
TL;DR: Support vector classification models for CYP450 isozymes are developed using a set of customized generic atom types that are useful in prioritizing compounds in a drug discovery pipeline or recognizing the toxic potential of environmental chemicals.
Journal ArticleDOI
A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability.
TL;DR: This PhE/SVM model provides a fast and accurate tool for predicting liability of hERG and can be utilized to guide medicinal chemistry to avoid molecules with an inhibition potential of this potassium channel.
Journal ArticleDOI
Computational screening for active compounds targeting protein sequences: methodology and experimental validation.
Fei Wang,Dongxiang Liu,He-Yao Wang,Cheng Luo,Mingyue Zheng,Hong Liu,Weiliang Zhu,Xiaomin Luo,Jian Zhang,Hualiang Jiang,Hualiang Jiang +10 more
TL;DR: This work constructed a model for predicting ligand-protein interaction based only on the primary sequence of proteins and the structural features of small molecules and it was successfully used in discovering nine novel active compounds for four pharmacologically important targets.