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Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Library Design: Reactant and Product-Based Approaches
inferred← citeshttps://doi.org/10.1016/b978-0-12-409547-2.02568-3openalex-snapshot
Library Design: Reactant and Product-Based Approaches
inferred← citeshttps://doi.org/10.1016/b978-0-12-417205-0.00015-8openalex-snapshot
Compound Properties and their Influence on Drug Quality
inferred← citeshttps://doi.org/10.1016/b978-0-12-822312-3.00011-4openalex-snapshot
Modern Tools and Techniques in Computer-Aided Drug Design
inferred← citeshttps://doi.org/10.1016/b978-0-323-85874-8.00007-7openalex-snapshot
New method of horizontal wellbore cleanout by supercritical carbon dioxide
inferred← citeshttps://doi.org/10.1016/b978-0-323-91819-0.00006-3openalex-snapshot
AI and machine learning in pharmaceutical formulation and manufacturing of personalized medicines
inferred← citeshttps://doi.org/10.1016/b978-0-323-99137-7.00010-1openalex-snapshot
Artificial intelligence (AI) and machine learning in the treatment of various diseases
inferred← citeshttps://doi.org/10.1016/b978-0-443-18638-7.00029-3openalex-snapshot
Quantitative structure-activity relationships (QSARs) in medicinal chemistry
inferred← citeshttps://doi.org/10.1016/b978-0-443-21870-5.00016-9openalex-snapshot
Machine learning–based solutions for brain tumor detection: Comparative study and limitations
inferred← citeshttps://doi.org/10.1016/b978-0-443-30010-3.00002-7openalex-snapshot
Basics of machine learning and artificial intelligence methods in toxicology and environmental health
inferred← citeshttps://doi.org/10.1016/b978-0-443-33032-2.00018-6openalex-snapshot
Leveraging artificial intelligence for drug development and drug discovery
inferred← citeshttps://doi.org/10.1016/b978-0-443-36474-7.00006-5openalex-snapshot
Application of artificial intelligence/machine learning in modeling chemical toxicity and ecotoxicity
inferred← citeshttps://doi.org/10.1016/bs.pmbts.2024.03.030openalex-snapshot
Application of artificial intelligence and machine learning in drug repurposing
inferred← citeshttps://doi.org/10.1016/j.aap.2015.05.018openalex-snapshot
Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects
inferred← citeshttps://doi.org/10.1016/j.aca.2004.12.085openalex-snapshot
Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine
inferred← citeshttps://doi.org/10.1016/j.actamat.2019.03.012openalex-snapshot
Machine-learning phase prediction of high-entropy alloys
inferred← citeshttps://doi.org/10.1016/j.addr.2015.01.009openalex-snapshot
The application of in silico drug-likeness predictions in pharmaceutical research
inferred← citeshttps://doi.org/10.1016/j.addr.2015.03.014openalex-snapshot
Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools
inferred← citeshttps://doi.org/10.1016/j.addr.2026.115855openalex-snapshot
Artificial intelligence-enabled personalisation of oral drug delivery: From data-driven design to on-demand manufacturing
inferred← citeshttps://doi.org/10.1016/j.aej.2024.08.074openalex-snapshot
Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.apt.2017.10.023openalex-snapshot
Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression
inferred← citeshttps://doi.org/10.1016/j.arr.2024.102651openalex-snapshot
Drug repositioning for Parkinson’s disease: An emphasis on artificial intelligence approaches
inferred← citeshttps://doi.org/10.1016/j.artmed.2011.11.003openalex-snapshot
Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers
inferred← citeshttps://doi.org/10.1016/j.artmed.2019.07.005openalex-snapshot
Accurate prediction of potential druggable proteins based on genetic algorithm and Bagging-SVM ensemble classifier
inferred← citeshttps://doi.org/10.1016/j.atmosres.2016.04.013openalex-snapshot
Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images
inferred← citeshttps://doi.org/10.1016/j.biteb.2025.102342openalex-snapshot
Optimizing potential of consortium AKUC-1 for cresol bioremediation: A statistical approach complemented with AI-based prediction
inferred← citeshttps://doi.org/10.1016/j.bmc.2007.08.057openalex-snapshot
A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine
inferred← citeshttps://doi.org/10.1016/j.brainres.2011.05.066openalex-snapshot
Quantitative prediction of acute ischemic tissue fate using support vector machine
inferred← citeshttps://doi.org/10.1016/j.bspc.2018.10.006openalex-snapshot
Algorithm for automatic EEG classification according to the epilepsy type: Benign focal childhood epilepsy and structural focal epilepsy
inferred← citeshttps://doi.org/10.1016/j.cbpa.2004.04.005openalex-snapshot
Applying data mining techniques to library design, lead generation and lead optimization
inferred← citeshttps://doi.org/10.1016/j.ces.2013.04.024openalex-snapshot
Experimental study on mass transfer and prediction using artificial neural network for CO2 absorption into aqueous DETA
inferred← citeshttps://doi.org/10.1016/j.chemolab.2008.02.004openalex-snapshot
Tailored scoring function of Trypsin–benzamidine complex using COMBINE descriptors and support vector regression
inferred← citeshttps://doi.org/10.1016/j.chemolab.2008.03.002openalex-snapshot
Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds
inferred← citeshttps://doi.org/10.1016/j.chemolab.2010.11.005openalex-snapshot
Prediction of association constants of cesium chelates based on Uniform Design Optimized Support Vector Machine
inferred← citeshttps://doi.org/10.1016/j.chemolab.2019.103850openalex-snapshot
Artificial intelligence facilitates drug design in the big data era
inferred← citeshttps://doi.org/10.1016/j.chemolab.2021.104309openalex-snapshot
Efficient lipophilicity prediction of molecules employing deep-learning models
inferred← citeshttps://doi.org/10.1016/j.chemolab.2022.104640openalex-snapshot
A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2005.09.053openalex-snapshot
Quantitative structure-activity relationship models for prediction of sensory irritants (logRD50) of volatile organic chemicals
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2017.04.015openalex-snapshot
Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses
inferred← citeshttps://doi.org/10.1016/j.chroma.2006.01.136openalex-snapshot
Quantitative structure–property relationships for pesticides in biopartitioning micellar chromatography
inferred← citeshttps://doi.org/10.1016/j.chroma.2006.11.026openalex-snapshot
Classification of protein adsorption and recovery at low salt conditions in hydrophobic interaction chromatographic systems
inferred← citeshttps://doi.org/10.1016/j.cie.2014.01.014openalex-snapshot
A weighted support vector machine method for control chart pattern recognition
inferred← citeshttps://doi.org/10.1016/j.cjca.2019.09.013openalex-snapshot
Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches
inferred← citeshttps://doi.org/10.1016/j.cmpb.2014.08.009openalex-snapshot
Drug/nondrug classification using Support Vector Machines with various feature selection strategies
inferred← citeshttps://doi.org/10.1016/j.cmpb.2017.02.004openalex-snapshot
Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies
inferred← citeshttps://doi.org/10.1016/j.commatsci.2021.110389openalex-snapshot
Improving phase prediction accuracy for high entropy alloys with Machine learning
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2015.05.015openalex-snapshot
Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2018.04.024openalex-snapshot
Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2020.103722openalex-snapshot
The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.109441openalex-snapshot
Kinematic classification of mandibular movements in patients with temporomandibular disorders based on PCA
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2016.04.035openalex-snapshot
Prediction of viscosity of imidazolium-based ionic liquids using MLR and SVM algorithms
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2019.04.006openalex-snapshot
Surrogate modeling of phase equilibrium calculations using adaptive sampling
inferred← citeshttps://doi.org/10.1016/j.corsci.2014.02.023openalex-snapshot
Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design
inferred← citeshttps://doi.org/10.1016/j.corsci.2015.06.003openalex-snapshot
The discussion of descriptors for the QSAR model and molecular dynamics simulation of benzimidazole derivatives as corrosion inhibitors
inferred← citeshttps://doi.org/10.1016/j.drudis.2006.06.006openalex-snapshot
Methods for mining HTS data
inferred← citeshttps://doi.org/10.1016/j.drudis.2014.10.012openalex-snapshot
Machine-learning approaches in drug discovery: methods and applications
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2009.06.005openalex-snapshot
Automated classification of bird and amphibian calls using machine learning: A comparison of methods
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.04.050openalex-snapshot
QSAR study of Akt/protein kinase B (PKB) inhibitors using support vector machine
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.09.006openalex-snapshot
Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.12.003openalex-snapshot
QSAR study on melanocortin-4 receptors by support vector machine
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.12.038openalex-snapshot
Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods
inferred← citeshttps://doi.org/10.1016/j.ejmech.2026.118996openalex-snapshot
Prediction of intrinsic clearance using an explainable learning framework integrating molecular fingerprints and graph representation
inferred← citeshttps://doi.org/10.1016/j.ejps.2012.06.021openalex-snapshot
Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine
inferred← citeshttps://doi.org/10.1016/j.electacta.2007.12.084openalex-snapshot
Prediction of selectivity coefficients of univalent anions for anion-selective electrode using support vector machine
inferred← citeshttps://doi.org/10.1016/j.enggeo.2025.108121openalex-snapshot
A comparison of metaheuristic optimizations with automated hyperparameter tuning methods in support vector machines algorithm for predicting soil water characteristic curve
inferred← citeshttps://doi.org/10.1016/j.envsoft.2016.06.020openalex-snapshot
Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong
inferred← citeshttps://doi.org/10.1016/j.eswa.2012.02.189openalex-snapshot
Parkinson’s Disease tremor classification – A comparison between Support Vector Machines and neural networks
inferred← citeshttps://doi.org/10.1016/j.eswa.2015.08.033openalex-snapshot
Training with synthesised data for disaggregated event classification at the water meter
inferred← citeshttps://doi.org/10.1016/j.eswa.2016.02.017openalex-snapshot
Financial time series pattern matching with extended UCR Suite and Support Vector Machine
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115525openalex-snapshot
EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction
inferred← citeshttps://doi.org/10.1016/j.flowmeasinst.2022.102293openalex-snapshot
Modeling subcritical multi-phase flow through surface chokes with new production parameters
inferred← citeshttps://doi.org/10.1016/j.fluid.2014.04.010openalex-snapshot
Prediction of the aqueous solubility of BaSO4 using pitzer ion interaction model and LSSVM algorithm
inferred← citeshttps://doi.org/10.1016/j.fluid.2014.04.023openalex-snapshot
Predicting the solubility of SrSO4 in Na–Ca–Mg–Sr–Cl–SO4–H2O system at elevated temperatures and pressures
inferred← citeshttps://doi.org/10.1016/j.fmre.2023.02.016openalex-snapshot
Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale
inferred← citeshttps://doi.org/10.1016/j.fss.2004.10.015openalex-snapshot
A neuro-fuzzy approach to virtual screening in molecular bioinformatics
inferred← citeshttps://doi.org/10.1016/j.ihj.2016.12.016openalex-snapshot
The effect of traditional Persian music on the cardiac functioning of young Iranian women
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2018.11.039openalex-snapshot
The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2019.04.002openalex-snapshot
Data analytics on raw material properties to accelerate pharmaceutical drug development
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