Drug design by machine learning: support vector machines for pharmaceutical data analysis.
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A composite score for predicting errors in protein structure modelsMolecular ChemometricsGPURFSCREEN: a GPU based virtual screening tool using random forest classifierLarge-scale ligand-based predictive modelling using support vector machinesA two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligandsIn silico identification of potent pancreatic triacylglycerol lipase inhibitors from traditional Chinese medicineA systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological dataMachine learning for in silico virtual screening and chemical genomics: new strategiesMachine learning algorithms for mode-of-action classification in toxicity assessmentComputational methods in drug discovery.An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.Evaluation of an in silico cardiac safety assay: using ion channel screening data to predict QT interval changes in the rabbit ventricular wedge.Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen StorageSupport vector machines in HTS data mining: Type I MetAPs inhibition study.A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model.Efficacy of different protein descriptors in predicting protein functional families.Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins.Treatment of acute lymphoblastic leukemia from traditional chinese medicineCoMFA analysis of tgDHFR and rlDHFR based on antifolates with 6-5 fused ring system using the all-orientation search (AOS) routine and a modified cross-validated r(2)-guided region selection (q(2)-GRS) routine and its initial application.In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance.Machine learning and radiology.Prediction of RNA-binding proteins from primary sequence by a support vector machine approach.Using support vector classification for SAR of fentanyl derivatives.Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries.Predicting protein-protein interactions based only on sequences information.Novel Scaffold Identification of mGlu1 Receptor Negative Allosteric Modulators Using a Hierarchical Virtual Screening Approach.The parameter sensitivity of random forests.Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length.Machine learning bandgaps of double perovskites.Computational prediction of human proteins that can be secreted into the bloodstream.Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).Using genetic findings in autism for the development of new pharmaceutical compounds.Support vector machines for drug discovery.An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2-aminothiazole-4-carboxamide.Current Trends in Multidrug Optimization.The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability.Raman spectra exploring breast tissues: comparison of principal component analysis and support vector machine-recursive feature elimination.
P2860
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P2860
Drug design by machine learning: support vector machines for pharmaceutical data analysis.
description
2001 nî lūn-bûn
@nan
2001 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2001 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2001年の論文
@ja
2001年論文
@yue
2001年論文
@zh-hant
2001年論文
@zh-hk
2001年論文
@zh-mo
2001年論文
@zh-tw
2001年论文
@wuu
name
Drug design by machine learnin ...... pharmaceutical data analysis.
@ast
Drug design by machine learnin ...... pharmaceutical data analysis.
@en
type
label
Drug design by machine learnin ...... pharmaceutical data analysis.
@ast
Drug design by machine learnin ...... pharmaceutical data analysis.
@en
prefLabel
Drug design by machine learnin ...... pharmaceutical data analysis.
@ast
Drug design by machine learnin ...... pharmaceutical data analysis.
@en
P2093
P921
P1476
Drug design by machine learnin ...... pharmaceutical data analysis.
@en
P2093
P356
10.1016/S0097-8485(01)00094-8
P577
2001-12-01T00:00:00Z