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Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

https://doi.org/10.1021/ci049965i
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/1521-3838(200110)20:3<227::aid-qsar227>3.0.co;2-yopenalex-snapshot
Use of Support Vector Machine in Pattern Classification: Application to QSAR Studies
inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1002/jcc.10017openalex-snapshot
Support vector machines for predicting HIV protease cleavage sites in protein
inferredcites →https://doi.org/10.1006/jmbi.2001.4580openalex-snapshot
A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach1 1Edited by B. Holland
inferredcites →https://doi.org/10.1016/s0097-8485(01)00094-8openalex-snapshot
Drug design by machine learning: support vector machines for pharmaceutical data analysis
inferredcites →https://doi.org/10.1016/s0169-7439(99)00056-8openalex-snapshot
Local modelling with radial basis function networks
inferredcites →https://doi.org/10.1021/ac0263519openalex-snapshot
Prediction of the Effect of Mobile-Phase Salt Type on Protein Retention and Selectivity in Anion Exchange Systems
inferredcites →https://doi.org/10.1021/ci025580topenalex-snapshot
Prediction of Protein Retention Times in Anion-Exchange Chromatography Systems Using Support Vector Regression
inferredcites →https://doi.org/10.1021/ci025620topenalex-snapshot
Active Learning with Support Vector Machines in the Drug Discovery Process
inferredcites →https://doi.org/10.1021/ci0256438openalex-snapshot
Diagnosing Breast Cancer Based on Support Vector Machines
inferredcites →https://doi.org/10.1021/ci0340355openalex-snapshot
QSAR Study of Ethyl 2-[(3-Methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: An Inhibitor of AP-1 and NF-κB Mediated Gene Expression Based on Support Vector Machines
inferredcites →https://doi.org/10.1021/ci9903206openalex-snapshot
Structurally Diverse Quantitative Structure−Property Relationship Correlations of Technologically Relevant Physical Properties
inferredcites →https://doi.org/10.1021/cr020464aopenalex-snapshot
Cyclooxygenase (COX) Inhibitors: A Comparative QSAR Study
inferredcites →https://doi.org/10.1023/a:1012487302797openalex-snapshot
Gene Selection for Cancer Classification using Support Vector Machines
inferredcites →https://doi.org/10.1073/pnas.211566398openalex-snapshot
Multiclass cancer diagnosis using tumor gene expression signatures
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1080/10629360290023340openalex-snapshot
Fragment generation and support vector machines for inducing SARs
inferredcites →https://doi.org/10.1093/bioinformatics/16.10.906openalex-snapshot
Support vector machine classification and validation of cancer tissue samples using microarray expression data
inferredcites →https://doi.org/10.1201/9781482296945openalex-snapshot
Topological Indices and Related Descriptors in QSAR and QSPR
inferredcites →https://doi.org/10.2174/1568026023392922openalex-snapshot
The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9780470175668.ch3openalex-snapshot
Graph Theoretic Models in Chemistry and Molecular Biology
inferred← citeshttps://doi.org/10.1002/9781119483977.ch2openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/aisy.202000084openalex-snapshot
Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction
inferred← citeshttps://doi.org/10.1002/aocs.12421openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/btpr.2342openalex-snapshot
Accounting for host cell protein behavior in anion‐exchange chromatography
inferred← citeshttps://doi.org/10.1002/cem.2415openalex-snapshot
Novel algorithm to select basis functions in spline regression: applications in quantitative structure–activity relationship studies
inferred← citeshttps://doi.org/10.1002/cem.2508openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/cpe.6453openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/ffj.1917openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jcc.23886openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/maco.201307037openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201600153openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/minf.201900029openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200510142openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200610078openalex-snapshot
Quantitative Structure–Activity Relationships Study on the Ah Receptor Binding Affinities of Polybrominated Diphenyl Ethers Using a Support Vector Machine
inferred← citeshttps://doi.org/10.1002/qsar.200630141openalex-snapshot
Exploration of Physicochemical Properties and Molecular Modeling Studies of Furanylamide Analogs as Antituberculosis Agents
inferred← citeshttps://doi.org/10.1002/qsar.200710077openalex-snapshot
Optimal QSAR Analysis of the Carcinogenic Activity of Aromatic and Heteroaromatic Amines
inferred← citeshttps://doi.org/10.1002/qsar.200710096openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/qsar.200860027openalex-snapshot
Study of Nematic Transition Temperatures in Themotropic Liquid Crystal Using Heuristic Method and Radial Basis Function Neural Networks and Support Vector Machine
inferred← citeshttps://doi.org/10.1002/qsar.200860065openalex-snapshot
Estimation of Soil Organic Carbon Normalized Sorption Coefficient (Koc) Using Least Squares‐Support Vector Machine
inferred← citeshttps://doi.org/10.1002/qsar.200960038openalex-snapshot
QSAR of Clerodane Diterpenoids with Cytotoxic and Antitumoral Activity Against V‐79 Cells
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_422openalex-snapshot
QSAR Modeling and QSAR Based Virtual Screening, Complexity and Challenges of Modern
inferred← citeshttps://doi.org/10.1007/978-0-387-68825-1_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-4020-9787-4_23openalex-snapshot
Aromatic Ketones Containing One Propionyl Group
inferred← citeshttps://doi.org/10.1007/978-1-4419-0197-2_1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-61779-012-6_2openalex-snapshot
Computer-Aided Drug Discovery and Development
inferred← citeshttps://doi.org/10.1007/978-3-031-28401-4_11openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-96364-3_3openalex-snapshot
Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling
inferred← citeshttps://doi.org/10.1007/978-3-540-36266-1_10openalex-snapshot
Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
inferred← citeshttps://doi.org/10.1007/978-981-13-5889-0_1openalex-snapshot
Contributions of Computer-Based Chemical Modeling Technologies on the Risk Assessment and the Environmental Fate Study of (Nano)pesticides
inferred← citeshttps://doi.org/10.1007/s00044-010-9518-8openalex-snapshot
QSAR analysis of B-ring-modified diaryl ether derivatives as a InhA inhibitors
inferred← citeshttps://doi.org/10.1007/s00044-011-9550-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00044-011-9863-2openalex-snapshot
QSAR study of some CCR5 antagonists as anti-HIV agents using radial basis function neural network and general regression neural network on the basis of principal components
inferred← citeshttps://doi.org/10.1007/s00044-012-0385-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00107-022-01794-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00214-025-03174-9openalex-snapshot
QSAR and machine learning-driven proposition of novel 1,3,4-oxadiazoles and structure-based studies of their antibacterial activities against Xanthomonas oryzae
inferred← citeshttps://doi.org/10.1007/s00216-005-0085-7openalex-snapshot
QSAR study on the Ah receptor-binding affinities of polyhalogenated dibenzo-p-dioxins using net atomic-charge descriptors and a radial basis neural network
inferred← citeshttps://doi.org/10.1007/s00521-013-1404-0openalex-snapshot
Generation of artificial neural networks models in anticancer study
inferred← citeshttps://doi.org/10.1007/s00521-016-2231-xopenalex-snapshot
Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran
inferred← citeshttps://doi.org/10.1007/s00521-022-08026-2openalex-snapshot
Compressive strength prediction of SLWC using RBFNN and LSSVM approaches
inferred← citeshttps://doi.org/10.1007/s00769-026-01701-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10668-021-01402-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10822-005-9003-5openalex-snapshot
Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines
inferred← citeshttps://doi.org/10.1007/s10822-017-0094-6openalex-snapshot
A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities
inferred← citeshttps://doi.org/10.1007/s10989-017-9661-xopenalex-snapshot
QSAR Study of Angiotensin I-Converting Enzyme Inhibitory Peptides Using SVHEHS Descriptor and OSC-SVM
inferred← citeshttps://doi.org/10.1007/s11030-006-9036-2openalex-snapshot
SVM approach for predicting LogP
inferred← citeshttps://doi.org/10.1007/s11030-010-9232-yopenalex-snapshot
Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling
inferred← citeshttps://doi.org/10.1007/s11030-013-9447-9openalex-snapshot
Classification of Plasmodium falciparum glucose-6-phosphate dehydrogenase inhibitors by support vector machine
inferred← citeshttps://doi.org/10.1007/s11095-005-6246-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11172-009-0076-5openalex-snapshot
Fragmental descriptors in (Q)SAR: prediction of the assignment of organic compounds to pharmacological groups using the support vector machine approach
inferred← citeshttps://doi.org/10.1007/s11224-020-01535-7openalex-snapshot
Molecular docking, linear and nonlinear QSAR studies on factor Xa inhibitors
inferred← citeshttps://doi.org/10.1007/s11356-019-04662-9openalex-snapshot
Neonicotinoid insecticide design: molecular docking, multiple chemometric approaches, and toxicity relationship with Cowpea aphids
inferred← citeshttps://doi.org/10.1007/s11426-008-0070-zopenalex-snapshot
QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network
inferred← citeshttps://doi.org/10.1007/s11434-007-0484-6openalex-snapshot
Back-propagation network improved by conjugate gradient based on genetic algorithm in QSAR study on endocrine disrupting chemicals
inferred← citeshttps://doi.org/10.1007/s13738-015-0761-2openalex-snapshot
QSPR/QSAR solely based on molecular surface electrostatic potentials for benzenoid hydrocarbons
inferred← citeshttps://doi.org/10.1007/s42107-023-00878-wopenalex-snapshot
Prediction of the self-healing properties of concrete modified with bacteria and fibers using machine learning
inferred← citeshttps://doi.org/10.1016/b978-0-12-396975-0.00003-6openalex-snapshot
Molecular-Level Modeling and Simulation in Process Safety
inferred← citeshttps://doi.org/10.1016/b978-0-443-29808-0.00014-5openalex-snapshot
Multiobjective optimization of biological and physical properties in drug discovery
inferred← citeshttps://doi.org/10.1016/b978-0-443-29808-0.00032-7openalex-snapshot
Applications of machine learning in quantitative structure-activity relationships (QSAR)
inferred← citeshttps://doi.org/10.1016/j.aca.2004.11.066openalex-snapshot
Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks
inferred← citeshttps://doi.org/10.1016/j.aca.2006.11.037openalex-snapshot
Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties
inferred← citeshttps://doi.org/10.1016/j.aca.2007.07.016openalex-snapshot
Quantitative structure–retention relationships for organic pollutants in biopartitioning micellar chromatography
inferred← citeshttps://doi.org/10.1016/j.aca.2008.01.003openalex-snapshot
Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression
inferred← citeshttps://doi.org/10.1016/j.aca.2008.02.037openalex-snapshot
Quantitative structure–property relationship study for estimation of quantitative calibration factors of some organic compounds in gas chromatography
inferred← citeshttps://doi.org/10.1016/j.aca.2009.02.006openalex-snapshot
Application of experimental design and radial basis function neural network to the separation and determination of active components in traditional Chinese medicines by capillary electrophoresis
inferred← citeshttps://doi.org/10.1016/j.aca.2015.01.050openalex-snapshot
Development of models for prediction of the antioxidant activity of derivatives of natural compounds
inferred← citeshttps://doi.org/10.1016/j.bmc.2007.12.055openalex-snapshot
A genetic algorithm optimized fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease
inferred← citeshttps://doi.org/10.1016/j.bmcl.2006.06.046openalex-snapshot
Correlation of antibacterial activity of some N-[5-(2-furanyl)-2-methyl-4-oxo-4H-thieno[2,3-d]pyrimidin-3-yl]-carboxamide and 3-substituted-5-(2-furanyl)-2-methyl-3H-thieno[2,3-d]pyrimidin-4-ones with topological indices using Hansch analysis
inferred← citeshttps://doi.org/10.1016/j.bmcl.2007.08.037openalex-snapshot
Quantitative structure–activity relationship studies for prediction of antimicrobial activity of synthesized 2,4-hexadienoic acid derivatives
inferred← citeshttps://doi.org/10.1016/j.bmcl.2012.12.085openalex-snapshot
Relationship between antimold activity and molecular structure of cinnamaldehyde analogues
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.06.016openalex-snapshot
Chemometrics tools in QSAR/QSPR studies: A historical perspective
inferred← citeshttps://doi.org/10.1016/j.chemolab.2016.10.016openalex-snapshot
Interpretable linear and nonlinear quantitative structure-selectivity relationship (QSSR) modeling of a biomimetic catalytic system by particle swarm optimization based sparse regression
inferred← citeshttps://doi.org/10.1016/j.chemolab.2017.02.006openalex-snapshot
Constraint score for semi-supervised feature selection in ligand-and receptor-based QSAR on serine/threonine-protein kinase PLK3 inhibitors
inferred← citeshttps://doi.org/10.1016/j.chemolab.2018.11.004openalex-snapshot
A novel robust NL-PLS regression methodology
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2008.03.026openalex-snapshot
Comparative structure–toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2012.10.065openalex-snapshot
Prediction of the baseline toxicity of non-polar narcotic chemical mixtures by QSAR approach
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2017.01.014openalex-snapshot
A novel model to predict gas–phase hydroxyl radical oxidation kinetics of polychlorinated compounds
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2022.136977openalex-snapshot
Antibiotics degradation by advanced oxidation process (AOPs): Recent advances in ecotoxicity and antibiotic-resistance genes induction of degradation products
inferred← citeshttps://doi.org/10.1016/j.commatsci.2005.11.003openalex-snapshot
QSPR study of permeability coefficients through low-density polyethylene based on radial basis function neural networks and the heuristic method
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2011.03.003openalex-snapshot
Predicting protein–protein interactions using graph invariants and a neural network
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2011.12.001openalex-snapshot
Prediction of the mutual diffusion coefficient for controlled drug delivery devices
inferred← citeshttps://doi.org/10.1016/j.cscm.2025.e04360openalex-snapshot
A novel predictive model for the compressive strength development in class G cement slurries at different temperatures, retarder, and salt concentrations
inferred← citeshttps://doi.org/10.1016/j.dyepig.2009.07.013openalex-snapshot
Quantitative structure–electrochemistry relationship for variously-substituted 9, 10-anthraquinones using both an heuristic method and a radial basis function neural network
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2008.09.002openalex-snapshot
Rapid toxicity prediction of organic chemicals to Chlorella vulgaris using quantitative structure–activity relationships methods
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2023.115495openalex-snapshot
In silico simulations and molecular descriptors to predict in vitro transactivation potencies of Baikal seal estrogen receptors by environmental contaminants
inferred← citeshttps://doi.org/10.1016/j.ejmech.2007.06.022openalex-snapshot
Exploration of physicochemical properties and molecular modelling studies of 2-sulfonyl-phenyl-3-phenyl-indole analogs as cyclooxygenase-2 inhibitors
inferred← citeshttps://doi.org/10.1016/j.ejmech.2007.09.004openalex-snapshot
Quantitative structure–activity relationship studies of a series of non-benzodiazepine structural ligands binding to benzodiazepine receptor
inferred← citeshttps://doi.org/10.1016/j.ejmech.2008.10.021openalex-snapshot
QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.12.028openalex-snapshot
MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives
inferred← citeshttps://doi.org/10.1016/j.ejps.2006.04.002openalex-snapshot
Boosting support vector regression in QSAR studies of bioactivities of chemical compounds
inferred← citeshttps://doi.org/10.1016/j.ejps.2012.11.012openalex-snapshot
QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity
inferred← citeshttps://doi.org/10.1016/j.emcon.2015.05.003openalex-snapshot
A comparative QSAR study on the estrogenic activities of persistent organic pollutants by PLS and SVM
inferred← citeshttps://doi.org/10.1016/j.enconman.2012.07.014openalex-snapshot
Molecular modeling of the standard state heat of formation
inferred← citeshttps://doi.org/10.1016/j.engappai.2018.03.009openalex-snapshot
Prediction of the transition temperature of bent-core liquid crystals using fuzzy “digital thermometer” model based on artificial neural networks
inferred← citeshttps://doi.org/10.1016/j.envpol.2023.122620openalex-snapshot
Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition
inferred← citeshttps://doi.org/10.1016/j.fct.2017.04.010openalex-snapshot
A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells
inferred← citeshttps://doi.org/10.1016/j.fluid.2010.07.014openalex-snapshot
Predictive correlations based on large experimental datasets: Critical constants for pure compounds
inferred← citeshttps://doi.org/10.1016/j.fuel.2018.08.136openalex-snapshot
Rigorous prognostication of permeability of heterogeneous carbonate oil reservoirs: Smart modeling and correlation development
inferred← citeshttps://doi.org/10.1016/j.ijggc.2016.12.009openalex-snapshot
Rigorous modeling of CO 2 equilibrium absorption in ionic liquids
inferred← citeshttps://doi.org/10.1016/j.ijrefrig.2019.03.012openalex-snapshot
WITHDRAWN: Accurate prediction of surface tension of refrigerants
inferred← citeshttps://doi.org/10.1016/j.jcis.2009.04.002openalex-snapshot
Prediction of hydrophile–lipophile balance values of anionic surfactants using a quantitative structure–property relationship
inferred← citeshttps://doi.org/10.1016/j.jclepro.2025.146309openalex-snapshot
A critical review on molecular modeling approaches for green solvent screening in the extractive removal of aromatic sulfur-containing, aromatic nitrogen-containing, and aromatic hydrocarbon compounds from fossil fuels
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2014.06.018openalex-snapshot
Toxicity of ionic liquids: Database and prediction via quantitative structure–activity relationship method
inferred← citeshttps://doi.org/10.1016/j.jmgm.2010.03.005openalex-snapshot
Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition
inferred← citeshttps://doi.org/10.1016/j.jmgm.2010.06.002openalex-snapshot
QSAR modeling of peptide biological activity by coupling support vector machine with particle swarm optimization algorithm and genetic algorithm
inferred← citeshttps://doi.org/10.1016/j.jmgm.2010.10.001openalex-snapshot
Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: Study of cyclin dependent kinase 4 inhibitors
inferred← citeshttps://doi.org/10.1016/j.jmgm.2016.05.006openalex-snapshot
Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach
inferred← citeshttps://doi.org/10.1016/j.jscs.2011.12.002openalex-snapshot
QSAR analysis of furanone derivatives as potential COX-2 inhibitors: kNN MFA approach
inferred← citeshttps://doi.org/10.1016/j.microc.2020.105236openalex-snapshot
Assessment of blood–brain barrier permeability using micellar electrokinetic chromatography and P_VSA-like descriptors
inferred← citeshttps://doi.org/10.1016/j.molliq.2013.04.021openalex-snapshot
QSPR study on the viscosity of bis(trifluoromethylsulfonyl)imide-based ionic liquids
inferred← citeshttps://doi.org/10.1016/j.molliq.2016.04.090openalex-snapshot
Experimental and modeling studies on adsorption of a nonionic surfactant on sandstone minerals in enhanced oil recovery process with surfactant flooding
inferred← citeshttps://doi.org/10.1016/j.molliq.2016.10.049openalex-snapshot
An accurate RBF-NN model for estimation of viscosity of nanofluids
inferred← citeshttps://doi.org/10.1016/j.molliq.2017.01.098openalex-snapshot
On the prediction of Watson characterization factor of hydrocarbons
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2023.107577openalex-snapshot
GATBoost: Mining graph attention networks-based important substructures of polymers for a better property prediction
inferred← citeshttps://doi.org/10.1016/j.partic.2014.12.001openalex-snapshot
(Q)SAR modelling of nanomaterial toxicity: A critical review
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2018.04.033openalex-snapshot
Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework
inferred← citeshttps://doi.org/10.1016/j.tcs.2005.08.026openalex-snapshot
From Hopfield nets to recursive networks to graph machines: Numerical machine learning for structured data
inferred← citeshttps://doi.org/10.1016/j.vibspec.2010.07.007openalex-snapshot
Quantitative structure–property relationship study for the prediction of characteristic infrared absorption of carbonyl group of commonly used carbonyl compounds
inferred← citeshttps://doi.org/10.1016/s0092-8674(18)90002-4openalex-snapshot
ROBUSTNESS IN EXPERIMENTAL DESIGN: A STUDY ON THE RELIABILITY OF SELECTION APPROACHES
inferred← citeshttps://doi.org/10.1016/s1005-9040(08)60114-2openalex-snapshot
QSAR Studies on PCDD/Fs by Kernel PLS
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00073openalex-snapshot
Deep Dive into Machine Learning Models for Protein Engineering
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01489openalex-snapshot
XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00277openalex-snapshot
Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00410openalex-snapshot
Molecule Property Prediction Based on Spatial Graph Embedding
inferred← citeshttps://doi.org/10.1021/acs.jpcb.3c07304openalex-snapshot
E(n) Equivariant Graph Neural Network for Learning Interactional Properties of Molecules
inferred← citeshttps://doi.org/10.1021/acsomega.5c01839openalex-snapshot
Computational Analysis of Biological Control Agents for Fusarium Wilt Using M‑Polynomials
inferred← citeshttps://doi.org/10.1021/ci060132xopenalex-snapshot
A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models
inferred← citeshttps://doi.org/10.1021/ci100200uopenalex-snapshot
Computational Analysis of HIV-1 Protease Protein Binding Pockets
inferred← citeshttps://doi.org/10.1021/ci100308fopenalex-snapshot
StructRank: A New Approach for Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci4001376openalex-snapshot
Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms
inferred← citeshttps://doi.org/10.1021/ci700047xopenalex-snapshot
Radial Basis Function Network-Based Transform for a Nonlinear Support Vector Machine as Optimized by a Particle Swarm Optimization Algorithm with Application to QSAR Studies
inferred← citeshttps://doi.org/10.1021/ci800142dopenalex-snapshot
Comparative Study of Machine-Learning and Chemometric Tools for Analysis of In-Vivo High-Throughput Screening Data
inferred← citeshttps://doi.org/10.1021/jp052223nopenalex-snapshot
Accurate Quantitative Structure−Property Relationship Model To Predict the Solubility of C 60 in Various Solvents Based on a Novel Approach Using a Least-Squares Support Vector Machine
inferred← citeshttps://doi.org/10.1029/2011ms000070openalex-snapshot
Spatial extrapolation of light use efficiency model parameters to predict gross primary production
inferred← citeshttps://doi.org/10.1038/s41598-018-28105-6openalex-snapshot
Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling
inferred← citeshttps://doi.org/10.1038/s41598-022-15869-1openalex-snapshot
New insights into permeability determination by coupling Stoneley wave propagation and conventional petrophysical logs in carbonate oil reservoirs
inferred← citeshttps://doi.org/10.1038/s41598-024-58394-zopenalex-snapshot
Inferring molecular inhibition potency with AlphaFold predicted structures
inferred← citeshttps://doi.org/10.1039/b800148kopenalex-snapshot
Prediction of quantitative calibration factors of some organic compounds in gas chromatography
inferred← citeshttps://doi.org/10.1039/c0md00165aopenalex-snapshot
Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets
inferred← citeshttps://doi.org/10.1039/c4ra15478aopenalex-snapshot
In silico de novo design of novel NNRTIs: a bio-molecular modelling approach
inferred← citeshttps://doi.org/10.1039/c6ra07898bopenalex-snapshot
Computational approaches for the prediction of the selective uptake of magnetofluorescent nanoparticles into human cells
inferred← citeshttps://doi.org/10.1039/c8ra05430dopenalex-snapshot
Implementation of a dynamic intestinal gut-on-a-chip barrier model for transport studies of lipophilic dioxin congeners
inferred← citeshttps://doi.org/10.1039/d3py00395gopenalex-snapshot
A review on the application of molecular descriptors and machine learning in polymer design
inferred← citeshttps://doi.org/10.1049/iet-epa.2010.0103openalex-snapshot
Fuzzy information granulated particle swarm optimisation-support vector machine regression for the trend forecasting of dissolved gases in oil-filled transformers
inferred← citeshttps://doi.org/10.1051/matecconf/201815401003openalex-snapshot
Multiple linear regression for the analysis of the parameters used in dyes decolourisation by ozonation techniques
inferred← citeshttps://doi.org/10.1055/s-0043-108553openalex-snapshot
QSAR Study of Artemisinin Analogues as Antimalarial Drugs by Neural Network and Replacement Method
inferred← citeshttps://doi.org/10.1063/5.0185200openalex-snapshot
Multiple linear regression analysis of the degradation process of Congo red using ferrate
inferred← citeshttps://doi.org/10.1080/00268976.2024.2331620openalex-snapshot
Applications of conceptual density functional theory in reference to quantitative structure–activity / property relationship
inferred← citeshttps://doi.org/10.1080/00387010.2017.1297956openalex-snapshot
Evaluation of spectral pretreatments, spectral range, and regression methods for quantitative spectroscopic analysis of soil organic carbon composition
inferred← citeshttps://doi.org/10.1080/07391102.2023.2196701openalex-snapshot
Machine learning and traditional QSAR modeling methods: a case study of known PXR activators
inferred← citeshttps://doi.org/10.1080/10629360600934044openalex-snapshot
Quantitative structure-property relationship studies on electrochemical degradation of substituted phenols using a support vector machine
inferred← citeshttps://doi.org/10.1080/10629360601054313openalex-snapshot
Predicting activities without computing descriptors: graph machines for QSAR
inferred← citeshttps://doi.org/10.1080/1062936x.2012.664562openalex-snapshot
Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors
inferred← citeshttps://doi.org/10.1080/1062936x.2015.1080186openalex-snapshot
Linear and non-linear modelling of the cytotoxicity of TiO2 and ZnO nanoparticles by empirical descriptors
inferred← citeshttps://doi.org/10.1080/1062936x.2016.1197310openalex-snapshot
Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches
inferred← citeshttps://doi.org/10.1080/1062936x.2018.1424030openalex-snapshot
QSAR modelling using combined simple competitive learning networks and RBF neural networks
inferred← citeshttps://doi.org/10.1080/10659360600562079openalex-snapshot
Quantitative structure-toxicity relationships (QSTRs): A comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis
inferred← citeshttps://doi.org/10.1080/10916466.2021.1902351openalex-snapshot
Accurate prediction of viscosity of mixed oils
inferred← citeshttps://doi.org/10.1088/2399-6528/abd3d8openalex-snapshot
Predicting toxicity by quantum machine learning
inferred← citeshttps://doi.org/10.1109/cibcb.2005.1594906openalex-snapshot
Neuro-fuzzy Prediction of Biological Activity and Rule Extraction for HIV-1 Protease Inhibitors
inferred← citeshttps://doi.org/10.1109/cibcb.2010.5510465openalex-snapshot
Classification of HIV-1 protease crystal structures using Random Forest, linear discriminant analysis and logistic regression
inferred← citeshttps://doi.org/10.1109/cibcb.2014.6845505openalex-snapshot
Bayesian ARTMAP prediction of biological activities for potential HIV-1 protease inhibitors using a small molecular dataset
inferred← citeshttps://doi.org/10.1109/iama.2011.6049004openalex-snapshot
A fuzzy based approach for prediction of biological activities of HIV -1 protease inhibitor compounds
inferred← citeshttps://doi.org/10.1109/icodsa55874.2022.9862820openalex-snapshot
QSAR Model for Prediction PTP1B Inhibitor as Anti-diabetes Mellitus using Simulated Annealing-Support Vector Machine
inferred← citeshttps://doi.org/10.1109/ijcnn.2006.1716622openalex-snapshot
An Integrated Soft Computing Approach for Predicting Biological Activity of Potential HIV-1 Protease Inhibitors
inferred← citeshttps://doi.org/10.1109/tcbb.2009.50openalex-snapshot
Fuzzy ARTMAP Prediction of Biological Activities for Potential HIV-1 Protease Inhibitors Using a Small Molecular Data Set
inferred← citeshttps://doi.org/10.1109/tmc.2025.3526185openalex-snapshot
MagicWrite: One-Dimensional Acoustic Tracking-Based Air Writing System
inferred← citeshttps://doi.org/10.1109/tsm.2013.2286498openalex-snapshot
Robust Relevance Vector Machine With Variational Inference for Improving Virtual Metrology Accuracy
inferred← citeshttps://doi.org/10.1109/ukci.2014.6930194openalex-snapshot
Using computational methods for the prediction of drug vehicles
inferred← citeshttps://doi.org/10.1111/cbdd.13511openalex-snapshot
Inhibition activity prediction for a dataset of candidates’ drug by combining fuzzy logic with MLR / ANN QSAR models
inferred← citeshttps://doi.org/10.1111/cbdd.13656openalex-snapshot
Bayesian machine learning to discover Bruton’s tyrosine kinase inhibitors
inferred← citeshttps://doi.org/10.1111/exsy.12675openalex-snapshot
Project performance prediction model linking agility and flexibility demands to project type
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2009.00935.xopenalex-snapshot
Computational Study of CCR5 Antagonist with Support Vector Machines and Three Dimensional Quantitative Structure Activity Relationship Methods
inferred← citeshttps://doi.org/10.1111/j.1747-0285.2012.01415.xopenalex-snapshot
Discovery of Novel Bruton’s Tyrosine Kinase Inhibitors Using a Hybrid Protocol of Virtual Screening Approaches Based on SVM Model, Pharmacophore and Molecular Docking
inferred← citeshttps://doi.org/10.1134/s1068162020060163openalex-snapshot
Synthesis of New Furanone Derivatives with Potent Anticancer Activity
inferred← citeshttps://doi.org/10.11591/ijece.v8i2.pp1131-1139openalex-snapshot
Towards an accurate Ground-Level Ozone Prediction
inferred← citeshttps://doi.org/10.1177/0003702816681009openalex-snapshot
Differences and Relationships Between Normal and Atypical Ductal Hyperplasia, Ductal Carcinoma In Situ, and Invasive Ductal Carcinoma Tissues in the Breast Based on Raman Spectroscopy
inferred← citeshttps://doi.org/10.1186/1471-2105-7-182openalex-snapshot
Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models
inferred← citeshttps://doi.org/10.1186/s13321-018-0304-9openalex-snapshot
Evaluating parameters for ligand-based modeling with random forest on sparse data sets
inferred← citeshttps://doi.org/10.1246/bcsj.20140169openalex-snapshot
Developing a Support Vector Machine Based QSPR Model for Prediction of Atmospheric Lifetime of Some Halocarbons
inferred← citeshttps://doi.org/10.1360/csb2007-52-18-2116openalex-snapshot
遗传算法结合共轭梯度法改进BP算法人工神经网络用于环境雌激素的QSAR研究
inferred← citeshttps://doi.org/10.1371/journal.pone.0041698openalex-snapshot
Proteochemometric Modeling of the Bioactivity Spectra of HIV-1 Protease Inhibitors by Introducing Protein-Ligand Interaction Fingerprint
inferred← citeshttps://doi.org/10.1371/journal.pone.0122416openalex-snapshot
Proteochemometric Modeling of the Antigen-Antibody Interaction: New Fingerprints for Antigen, Antibody and Epitope-Paratope Interaction
inferred← citeshttps://doi.org/10.1371/journal.pone.0155419openalex-snapshot
Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules
inferred← citeshttps://doi.org/10.14279/depositonce-3309openalex-snapshot
Novel Machine Learning Methods for Computational Chemistry
inferred← citeshttps://doi.org/10.1517/17460441.2010.492827openalex-snapshot
Advances in computational methods to predict the biological activity of compounds
inferred← citeshttps://doi.org/10.17576/jsm-2018-4703-25openalex-snapshot
Parallel Based Support Vector Regression for Empirical Modeling of Nonlinear Chemical Process Systems
inferred← citeshttps://doi.org/10.18174/531714openalex-snapshot
Human gut-on-chip as an intestinal model to predict compound absorption and toxicity
inferred← citeshttps://doi.org/10.2118/170636-msopenalex-snapshot
Integration of Principal-Component-Analysis and Streamline Information for the History Matching of Channelized Reservoirs
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3133577/v1openalex-snapshot
Prediction of self-healing properties of concrete modified with bacteria and fibers using Machine Learning
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3225181/v1openalex-snapshot
Inferring molecular Inhibition potency with AlphaFold predicted structures
inferred← citeshttps://doi.org/10.2174/0113852728249020230921072236openalex-snapshot
State of the Art and Outlook of Data Science and Machine Learning in Organic Chemistry
inferred← citeshttps://doi.org/10.2174/0115680266280005231207105900openalex-snapshot
Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches
inferred← citeshttps://doi.org/10.2174/138955752014200626163614openalex-snapshot
Medicinal Chemistry and Computational Chemistry: Mutual Influence and Harmonization
inferred← citeshttps://doi.org/10.2174/1389557520666200204121806openalex-snapshot
Medicinal Chemistry Meets Electrochemistry: Redox Potential in the Role of Endpoint or Molecular Descriptor in QSAR/QSPR
inferred← citeshttps://doi.org/10.2174/1570180819666220420092723openalex-snapshot
A Machine Learning Language to Build a QSAR Model of Pyrazoline Derivative Inhibitors Targeting Mycobacterium tuberculosis Strain H37Rv
inferred← citeshttps://doi.org/10.2174/157340913804998838openalex-snapshot
QSAR Modeling for the Antimalarial Activity of 1,4-Naphthoquinonyl Derivatives as Potential Antimalarial Agents
inferred← citeshttps://doi.org/10.2174/9789815179934123010004openalex-snapshot
Quantitative Structure-activity Relationship (QSAR) in Studying the Biologically Active Molecules
inferred← citeshttps://doi.org/10.2174/9789815305180124010010openalex-snapshot
Role of Artificial Intelligence in Teaching and Learning Chemical Sciences
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