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
Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers.
Feixiong Cheng,Yue Yu,Jie Shen,Lei Yang,Weihua Li,Guixia Liu,Philip W. Lee,Philip W. Lee,Yun Tang +8 more
TL;DR: These classification models are applicable for virtual screening of the five major CYP isoforms inhibitors or can be used as simple filters of potential chemicals in drug discovery.
Journal ArticleDOI
Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach
TL;DR: The experiments show that the new ranking methods developed here give better ranking performance than both classification based methods in virtual screening and regression methods in QSAR analysis.
Journal ArticleDOI
Virtual screening of GPCRs: an in silico chemogenomics approach.
Laurent Jacob,Laurent Jacob,Laurent Jacob,Brice Hoffmann,Brice Hoffmann,Brice Hoffmann,Véronique Stoven,Véronique Stoven,Véronique Stoven,Jean-Philippe Vert,Jean-Philippe Vert,Jean-Philippe Vert +11 more
TL;DR: In this paper, the G-protein coupled receptor (GPCR) superfamily is used to characterize interactions between all members of a target class and all small molecules simultaneously, which is an interesting alternative to traditional docking or ligandbased virtual screening strategies.
Journal ArticleDOI
Collaborative filtering on a family of biological targets.
TL;DR: This work shows an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target, and evaluates JRank, a kernel-based method designed for collaborative filtering.
Posted Content
Virtual screening of GPCRs: an in silico chemogenomics approach
Laurent Jacob,Laurent Jacob,Laurent Jacob,Brice Hoffmann,Brice Hoffmann,Brice Hoffmann,Véronique Stoven,Véronique Stoven,Véronique Stoven,Jean-Philippe Vert,Jean-Philippe Vert,Jean-Philippe Vert +11 more
TL;DR: Examining the use of 2D and 3D descriptors for small molecules, and incorporating information about the known hierarchical classification of the target family and about key residues in their inferred binding pockets significantly improves the prediction accuracy of the chemogenomics model.