On the public citation graph
Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks
0 signed · 164 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9780470125830openalex-snapshot
Reviews in Computational Chemistry
inferredcites →https://doi.org/10.1002/qsar.19950140503openalex-snapshot
QSAR Study of the Toxicity of Nitrobenzenes to Tetrahymena pyriformis
inferredcites →https://doi.org/10.1002/qsar.19960150103openalex-snapshot
Using Artificial Neural Networks to Predict Biological Activity from Simple Molecular Structural Considerations
inferredcites →https://doi.org/10.1007/978-1-4612-0745-0openalex-snapshot
Bayesian Learning for Neural Networks
inferredcites →https://doi.org/10.1016/0006-2952(92)90472-uopenalex-snapshot
Substituted imidazo[1,2-b]pyridazines
inferredcites →https://doi.org/10.1016/0223-5234(96)85873-9openalex-snapshot
Syntheses, pharmacological evaluation and molecular modelling of substituted 6-alkoxyimidazo[1,2-b]pyridazines as new ligands for the benzodiazepine receptor
inferredcites →https://doi.org/10.1016/s0165-9936(98)00097-1openalex-snapshot
Kohonen artificial neural networks as a tool for wavelength selection in multicomponent spectrofluorimetric PLS modelling: application to phenol, o-cresol, m-cresol and p-cresol mixtures
inferredcites →https://doi.org/10.1016/s0166-1280(97)00205-4openalex-snapshot
GA strategy for variable selection in QSAR studies: GAPLS and D-optimal designs for predictive QSAR model
inferredcites →https://doi.org/10.1016/s0169-7439(96)00062-7openalex-snapshot
Random correlation in variable selection for multivariate calibration with a genetic algorithm
inferredcites →https://doi.org/10.1016/s0305-0548(96)00088-3openalex-snapshot
Determining the saliency of input variables in neural network classifiers
inferredcites →https://doi.org/10.1021/ci950204copenalex-snapshot
Neural Network Studies. 2. Variable Selection
inferredcites →https://doi.org/10.1021/ci980033mopenalex-snapshot
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
inferredcites →https://doi.org/10.1021/ci980325nopenalex-snapshot
Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture
inferredcites →https://doi.org/10.1021/ci980405ropenalex-snapshot
Development and Validation of a Novel Variable Selection Technique with Application to Multidimensional Quantitative Structure−Activity Relationship Studies
inferredcites →https://doi.org/10.1021/ja01062a036openalex-snapshot
Deoxyribonucleoside-3′,5′ Cyclic Phosphates. Synthesis and Acid-Catalyzed and Enzymic Hydrolysis
inferredcites →https://doi.org/10.1021/jm00063a017openalex-snapshot
Structure-activity relationship studies at benzodiazepine receptor (BZR): a comparison of the substituent effects of pyrazoloquinolinone analogs
inferredcites →https://doi.org/10.1021/jm00079a002openalex-snapshot
Muscarinic activity of the thiolactone, lactam, lactol, and thiolactol analogs of pilocarpine and a hypothetical model for the binding of agonists to the M1 receptor
inferredcites →https://doi.org/10.1021/jm00085a016openalex-snapshot
Synthesis and muscarinic activities of quinuclidin-3-yltriazole and -tetrazole derivatives
inferredcites →https://doi.org/10.1021/jm00085a017openalex-snapshot
Muscarinic receptor subtype specificity of (N,N-dialkylamino)alkyl 2-cyclohexyl-2-phenylpropionates: cylexphenes (cyclohexyl-substituted aprophen analogs)
inferredcites →https://doi.org/10.1021/jm00090a019openalex-snapshot
Novel functional M1 selective muscarinic agonists. Synthesis and structure-activity relationships of 3-(1,2,5-thiadiazolyl)-1,2,5,6-tetrahydro-1-methylpyridines
inferredcites →https://doi.org/10.1021/jm00090a020openalex-snapshot
Nucleic acid related compounds. 74. Synthesis and biological activity of 2'(and 3')-deoxy-2'(and 3')-methylenenucleoside analogs that function as mechanism-based inhibitors of S-adenosyl-L-homocysteine hydrolase and/or ribonucleotide reductase
inferredcites →https://doi.org/10.1021/jm00106a033openalex-snapshot
Muscarinic cholinergic agonists and antagonists of the 3-(3-alkyl-1,2,4-oxadiazol-5-yl)-1,2,5,6-tetrahydropyridine type. Synthesis and structure-activity relationships
inferredcites →https://doi.org/10.1021/jm00113a009openalex-snapshot
Comparison of azabicyclic esters and oxadiazoles as ligands for the muscarinic receptor
inferredcites →https://doi.org/10.1021/jm00117a029openalex-snapshot
Synthesis of novel 3-substituted .beta.-carbolines as benzodiazepine receptor ligands: probing the benzodiazepine receptor pharmacophore
inferredcites →https://doi.org/10.1021/jm00117a030openalex-snapshot
Structure-activity relationships among benextramine-related tetraamine disulfides. Chain length effect on .alpha.-adrenoreceptor blocking activity
inferredcites →https://doi.org/10.1021/jm00165a028openalex-snapshot
Structural requirements for agonist actions at the benzodiazepine receptor: studies with analogs of 6-(benzyloxy)-4-(methoxymethyl)-.beta.-carboline-3-carboxylic acid ethyl ester
inferredcites →https://doi.org/10.1021/jm00165a029openalex-snapshot
Synthesis and structure-activity relationship of C5-substituted analogs of (.+-.)-10,11-dihydro-5H-dibenzo[a,d]cyclohepten-5, 10-imine [(.+-.)-desmethyl-MK801]: ligands for the NMDA receptor-coupled phencyclidine binding site
inferredcites →https://doi.org/10.1021/jm00166a009openalex-snapshot
Synthesis and .alpha.2-adrenoceptor effects of substituted catecholimidazoline and catecholimidazole analogs in human platelets
inferredcites →https://doi.org/10.1021/jm00346a002openalex-snapshot
2-Arylpyrazolo[4,3-c]quinolin-3-ones: a novel agonist, a partial agonist and an antagonist of benzodiazepines
inferredcites →https://doi.org/10.1021/jm980697nopenalex-snapshot
Robust QSAR Models Using Bayesian Regularized Neural Networks
inferredcites →https://doi.org/10.1021/tx9900627openalex-snapshot
A Quantitative Structure−Activity Relationships Model for the Acute Toxicity of Substituted Benzenes to Tetrahymena pyriformis Using Bayesian-Regularized Neural Networks
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
Neural Networks for Pattern Recognition
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/0471266949.bmc002openalex-snapshot
Recent Trends in Quantitative Structure‐Activity Relationships
inferred← citeshttps://doi.org/10.1002/bit.20961openalex-snapshot
Retrospective optimization of time‐dependent fermentation control strategies using time‐independent historical data
inferred← citeshttps://doi.org/10.1002/cphc.202500700openalex-snapshot
Complexation of Ca X 2 With n NH 3 ( X = H, F; n = 1–3): Insight From Quantum Chemical and Vibrational Mode Analysis
inferred← citeshttps://doi.org/10.1002/jcc.21190openalex-snapshot
Prediction of human cytochrome P450 2B6‐substrate interactions using hierarchical support vector regression approach
inferred← citeshttps://doi.org/10.1002/jcc.24764openalex-snapshot
Deep learning for computational chemistry
inferred← citeshttps://doi.org/10.1002/qsar.200390007openalex-snapshot
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
inferred← citeshttps://doi.org/10.1002/qsar.200430921openalex-snapshot
Mutagenicity of Aromatic and Heteroaromatic Amines and Related Compounds: A QSAR Investigation
inferred← citeshttps://doi.org/10.1002/qsar.200610093openalex-snapshot
ADMET Property Prediction: The State of the Art and Current Challenges
inferred← citeshttps://doi.org/10.1002/qsar.200810173openalex-snapshot
Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods
inferred← citeshttps://doi.org/10.1007/978-1-4939-7899-1_5openalex-snapshot
Machine Learning Methods in Computational Toxicology
inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_3openalex-snapshot
Bayesian Regularization of Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-319-70166-0_10openalex-snapshot
Quantitative Structure-Activity Modelling of Toxic Compounds
inferred← citeshttps://doi.org/10.1007/s10822-004-5319-9openalex-snapshot
Locally Linear Embedding for dimensionality reduction in QSAR
inferred← citeshttps://doi.org/10.1007/s10822-005-3321-5openalex-snapshot
An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network
inferred← citeshttps://doi.org/10.1007/s10822-018-0106-1openalex-snapshot
Sparse QSAR modelling methods for therapeutic and regenerative medicine
inferred← citeshttps://doi.org/10.1007/s11030-006-8697-1openalex-snapshot
Molecular similarity and diversity in chemoinformatics: From theory to applications
inferred← citeshttps://doi.org/10.1007/s11426-008-0076-6openalex-snapshot
Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds
inferred← citeshttps://doi.org/10.1007/s12039-021-01995-2openalex-snapshot
Artificial intelligence: machine learning for chemical sciences
inferred← citeshttps://doi.org/10.1007/s42250-024-00976-5openalex-snapshot
Revolution of Artificial Intelligence in Computational Chemistry Breakthroughs
inferred← citeshttps://doi.org/10.1016/b978-0-12-801238-3.05329-0openalex-snapshot
Structure-Activity Relationships: Theory, Uses and Limitations☆
inferred← citeshttps://doi.org/10.1016/b978-0-12-824156-1.00014-5openalex-snapshot
QSAR approach for combating cancer cells
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/b978-008055232-3.60006-6openalex-snapshot
Structure-Activity Relationships
inferred← citeshttps://doi.org/10.1016/j.arabjc.2011.03.006openalex-snapshot
A quantitative structure–activity relationship study on HIV-1 integrase inhibitors using genetic algorithm, artificial neural networks and different statistical methods
inferred← citeshttps://doi.org/10.1016/j.bmc.2004.12.028openalex-snapshot
3D QSAR Markov model for drug-induced eosinophilia—theoretical prediction and preliminary experimental assay of the antimicrobial drug G1
inferred← citeshttps://doi.org/10.1016/j.bulm.2003.12.003openalex-snapshot
Simple stochastic fingerprints towards mathematical modelling in biology and medicine. 1. The treatment of coccidiosis
inferred← citeshttps://doi.org/10.1016/j.cam.2022.114819openalex-snapshot
On the use of sparse Bayesian learning-based polynomial chaos expansion for global reliability sensitivity analysis
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.2016.01.007openalex-snapshot
Type (I, II) errors variable selection in quantitative structure activity relationships
inferred← citeshttps://doi.org/10.1016/j.chemolab.2025.105544openalex-snapshot
Advancing QSAR models in drug discovery for best practices, theoretical foundations, and applications in targeting nuclear factor-κB inhibitors- A bright future in pharmaceutical chemistry
inferred← citeshttps://doi.org/10.1016/j.cis.2022.102639openalex-snapshot
Drug-dendrimer complexes and conjugates: Detailed furtherance through theory and experiments
inferred← citeshttps://doi.org/10.1016/j.cjac.2023.100330openalex-snapshot
Evaluation of dimedone-derived compounds as inhibitors against human colon cancer: Insights from 2D-QSAR, ADMET prediction, Osiris, Molinspiration, and molecular modeling
inferred← citeshttps://doi.org/10.1016/j.cjche.2018.08.015openalex-snapshot
A review of extractive distillation from an azeotropic phenomenon for dynamic control
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2023.102337openalex-snapshot
Prediction of algal bloom using a combination of sparse modeling and a machine learning algorithm: Automatic relevance determination and support vector machine
inferred← citeshttps://doi.org/10.1016/j.ejmech.2006.12.017openalex-snapshot
Robust fuzzy mappings for QSAR studies
inferred← citeshttps://doi.org/10.1016/j.envsoft.2008.05.002openalex-snapshot
Fuzzy filtering for robust bioconcentration factor modelling
inferred← citeshttps://doi.org/10.1016/j.envsoft.2025.106592openalex-snapshot
Testing machine learning algorithms as post-processing tools for hydro-meteorological modelling over a small river basin
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2005.08.003openalex-snapshot
Predicting permeability coefficient in ADMET evaluation by using different membranes-interaction QSAR
inferred← citeshttps://doi.org/10.1016/j.jcp.2020.109539openalex-snapshot
Global sensitivity analysis: A Bayesian learning based polynomial chaos approach
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2021.125688openalex-snapshot
Use of a large dataset to develop new models for estimating the sorption of active pharmaceutical ingredients in soils and sediments
inferred← citeshttps://doi.org/10.1016/j.jmgm.2004.03.010openalex-snapshot
Modelling blood–brain barrier partitioning using Bayesian neural nets
inferred← citeshttps://doi.org/10.1016/j.jmgm.2006.06.005openalex-snapshot
Statistical external validation and consensus modeling: A QSPR case study for Koc prediction
inferred← citeshttps://doi.org/10.1016/j.measurement.2020.108654openalex-snapshot
Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification
inferred← citeshttps://doi.org/10.1016/j.micromeso.2011.06.011openalex-snapshot
A quantitative structure activity relationship approach to probe the influence of the functionalization on the drug encapsulation of porous metal-organic frameworks
inferred← citeshttps://doi.org/10.1016/j.neunet.2005.09.002openalex-snapshot
Comparison of recent methods for inference of variable influence in neural networks
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2016.11.156openalex-snapshot
Quantitative structure-property relationships for predicting sorption of pharmaceuticals to sewage sludge during waste water treatment processes
inferred← citeshttps://doi.org/10.1016/j.strusafe.2020.102074openalex-snapshot
Structural reliability analysis by a Bayesian sparse polynomial chaos expansion
inferred← citeshttps://doi.org/10.1016/j.tiv.2016.12.013openalex-snapshot
In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach
inferred← citeshttps://doi.org/10.1016/s0003-2670(03)00349-0openalex-snapshot
Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis
inferred← citeshttps://doi.org/10.1016/s0143-7208(03)00100-1openalex-snapshot
Mutagenicity of aminoazo dyes and their reductive-cleavage metabolites: a QSAR/QPAR investigation
inferred← citeshttps://doi.org/10.1016/s0377-2217(01)00129-1openalex-snapshot
Bayesian neural network learning for repeat purchase modelling in direct marketing
inferred← citeshttps://doi.org/10.1016/s1359-6446(01)00173-8openalex-snapshot
Neural networks and genetic algorithms in drug design
inferred← citeshttps://doi.org/10.1016/s1359-6446(01)01990-0openalex-snapshot
The impact of informatics and computational chemistry on synthesis and screening
inferred← citeshttps://doi.org/10.1016/s1359-6446(01)02059-1openalex-snapshot
The broader applications of neural and genetic modelling methods
inferred← citeshttps://doi.org/10.1016/s1359-6446(02)02288-2openalex-snapshot
Predicting ADME properties in silico: methods and models
inferred← citeshttps://doi.org/10.1016/s1741-8364(04)02393-5openalex-snapshot
Bayesian neural nets for modeling in drug discovery
inferred← citeshttps://doi.org/10.1016/s1741-8364(04)02402-3openalex-snapshot
Using artificial neural networks to drive virtual screening of combinatorial libraries
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00261openalex-snapshot
Relevance Vector Machines: Sparse Classification Methods for QSAR
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00415openalex-snapshot
Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands
inferred← citeshttps://doi.org/10.1021/acs.jctc.8b00439openalex-snapshot
Improving Vibrational Mode Interpretation Using Bayesian Regression
inferred← citeshttps://doi.org/10.1021/acs.jpcc.5c08343openalex-snapshot
How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes
inferred← citeshttps://doi.org/10.1021/acsomega.2c07436openalex-snapshot
Novel Random Forest Ensemble Modeling Strategy Combined with Quantitative Structure–Property Relationship for Density Prediction of Energetic Materials
inferred← citeshttps://doi.org/10.1021/ci000459copenalex-snapshot
Quantitative Structure−Activity Relationship Studies Using Gaussian Processes
inferred← citeshttps://doi.org/10.1021/ci010363yopenalex-snapshot
Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets
inferred← citeshttps://doi.org/10.1021/ci0203702openalex-snapshot
On the Use of Neural Network Ensembles in QSAR and QSPR
inferred← citeshttps://doi.org/10.1021/ci034108kopenalex-snapshot
Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms
inferred← citeshttps://doi.org/10.1021/ci0341565openalex-snapshot
2D QSAR Consensus Prediction for High-Throughput Virtual Screening. An Application to COX-2 Inhibition Modeling and Screening of the NCI Database
inferred← citeshttps://doi.org/10.1021/ci049923uopenalex-snapshot
Validated QSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting into Training−Test Sets and Consensus Modeling
inferred← citeshttps://doi.org/10.1021/ci0504014openalex-snapshot
logD7.4 Modeling Using Bayesian Regularized Neural Networks. Assessment and Correction of the Errors of Prediction
inferred← citeshttps://doi.org/10.1021/ci100169popenalex-snapshot
Jackknife-Based Selection of Gram−Schmidt Orthogonalized Descriptors in QSAR
inferred← citeshttps://doi.org/10.1021/ci2002186openalex-snapshot
Jointly Handling Potency and Toxicity of Antimicrobial Peptidomimetics by Simple Rules from Desirability Theory and Chemoinformatics
inferred← citeshttps://doi.org/10.1021/ci200409xopenalex-snapshot
Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
inferred← citeshttps://doi.org/10.1021/ci7000633openalex-snapshot
Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties
inferred← citeshttps://doi.org/10.1021/ci700231copenalex-snapshot
Modeling the Toxicity of Chemicals toTetrahymenapyriformisUsing Heuristic Multilinear Regression and Heuristic Back-Propagation Neural Networks
inferred← citeshttps://doi.org/10.1021/ci800290hopenalex-snapshot
Toward Novel Universal Descriptors: Charge Fingerprints
inferred← citeshttps://doi.org/10.1021/ci900377eopenalex-snapshot
Insolubility Classification with Accurate Prediction Probabilities Using a MetaClassifier
inferred← citeshttps://doi.org/10.1021/cr3004339openalex-snapshot
Descriptor Selection Methods in Quantitative Structure–Activity Relationship Studies: A Review Study
inferred← citeshttps://doi.org/10.1021/jm048959aopenalex-snapshot
A Rapid Computational Filter for Cytochrome P450 1A2 Inhibition Potential of Compound Libraries
inferred← citeshttps://doi.org/10.1023/a:1020869118689openalex-snapshot
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
inferred← citeshttps://doi.org/10.1023/a:1021372108686openalex-snapshot
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
inferred← citeshttps://doi.org/10.1023/a:1026132402754openalex-snapshot
ANVAS: Artificial Neural Variables Adaptation System for descriptor selection
inferred← citeshttps://doi.org/10.1039/c2mb05439fopenalex-snapshot
Robust, quantitative tools for modelling ex-vivo expansion of haematopoietic stem cells and progenitors
inferred← citeshttps://doi.org/10.1039/c3gc42540aopenalex-snapshot
Towards chromate-free corrosion inhibitors: structure–property models for organic alternatives
inferred← citeshttps://doi.org/10.1039/d0cp05509copenalex-snapshot
Predicting second virial coefficients of organic and inorganic compounds using Gaussian process regression
inferred← citeshttps://doi.org/10.1063/5.0057487openalex-snapshot
A comparative analysis of the ensemble methods for drug design
inferred← citeshttps://doi.org/10.1071/ch05202openalex-snapshot
Predictive Human Intestinal Absorption QSAR Models Using Bayesian Regularized Neural Networks
inferred← citeshttps://doi.org/10.1071/ch15172openalex-snapshot
Adrien Albert Award: How to Mine Chemistry Space for New Drugs and Biomedical Therapies
inferred← citeshttps://doi.org/10.1071/ch22138openalex-snapshot
Probing the properties of molecules and complex materials using machine learning
inferred← citeshttps://doi.org/10.1080/10629360108035385openalex-snapshot
Gaussian Process: An Efficient Technique to Solve Quantitative Structure-Property Relationship Problems
inferred← citeshttps://doi.org/10.1080/10629360701428854openalex-snapshot
Handling uncertainties in toxicity modelling using a fuzzy filter
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/10.1080/17460441.2016.1201262openalex-snapshot
A renaissance of neural networks in drug discovery
inferred← citeshttps://doi.org/10.1080/17460441.2018.1542428openalex-snapshot
Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR)
inferred← citeshttps://doi.org/10.1093/toxsci/kfq159openalex-snapshot
Predicting Mutagenicity of Aromatic Amines by Various Machine Learning Approaches
inferred← citeshttps://doi.org/10.1093/toxsci/kft209openalex-snapshot
Chemoinformatics Profiling of Ionic Liquids—Automatic and Chemically Interpretable Cytotoxicity Profiling, Virtual Screening, and Cytotoxicophore Identification
inferred← citeshttps://doi.org/10.1109/access.2020.3027828openalex-snapshot
Extension of pQSAR: Ensemble Model Generated by Random Forest and Partial Least Squares Regressions
inferred← citeshttps://doi.org/10.1109/coase.2007.4341713openalex-snapshot
Fuzzy Handling of Uncertainties in Modeling the Inhibition of Glycogen Synthase Kinase-3 by Paullones
inferred← citeshttps://doi.org/10.1109/coase.2008.4626437openalex-snapshot
A fuzzy system for modeling the structure-activity relationships in presence of uncertainties
inferred← citeshttps://doi.org/10.1109/ijcnn.2002.1007444openalex-snapshot
Who is afraid of the big bad ANN?
inferred← citeshttps://doi.org/10.1111/j.1541-0420.2011.01576.xopenalex-snapshot
Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data
inferred← citeshttps://doi.org/10.1111/j.2042-7158.1958.tb10331.xopenalex-snapshot
STRUCTURE—ACTIVITY RELATIONSHIPS
inferred← citeshttps://doi.org/10.1186/s12859-019-3135-4openalex-snapshot
Comprehensive ensemble in QSAR prediction for drug discovery
inferred← citeshttps://doi.org/10.1248/cpb.54.1162openalex-snapshot
Assisting the Diagnosis of Thyroid Diseases with Bayesian-Type and SOM-Type Neural Networks Making Use of Routine Test Data
inferred← citeshttps://doi.org/10.1360/zb2008-38-6-461openalex-snapshot
面向毒害有机物生态风险评价的(Q)SAR技术: 进展与展望
inferred← citeshttps://doi.org/10.1385/mb:27:2:139openalex-snapshot
Neural Networks as Robust Tools in Drug Lead Discovery and Development
inferred← citeshttps://doi.org/10.1517/17425250902753261openalex-snapshot
The use of machine learning and nonlinear statistical tools for ADME prediction
inferred← citeshttps://doi.org/10.1517/17460441.2011.586689openalex-snapshot
Using artificial neural networks to predict cell-penetrating compounds
inferred← citeshttps://doi.org/10.21203/rs.3.rs-949300/v1openalex-snapshot
Optimal Division of Molecules Into Training And Test Sets With A New Tool To Predict Pharmacophore In 3D-QSAR
inferred← citeshttps://doi.org/10.2174/092986712802884259openalex-snapshot
Machine Learning Techniques and Drug Design
inferred← citeshttps://doi.org/10.2174/138161207780765981openalex-snapshot
An Introduction to Recursive Neural Networks and Kernel Methods for Cheminformatics
inferred← citeshttps://doi.org/10.2174/157340911793743547openalex-snapshot
Recent Advances in Ligand-Based Drug Design: Relevance and Utility of the Conformationally Sampled Pharmacophore Approach
inferred← citeshttps://doi.org/10.2174/978160805379711201010194openalex-snapshot
Overview of QSAR Modelling in Rational Drug Design
inferred← citeshttps://doi.org/10.2477/jccj.1.23openalex-snapshot
Prediction of Carcinogenicity of Chlorine-containing Organic Compounds by Neural Network.
inferred← citeshttps://doi.org/10.2477/jccj.4.89openalex-snapshot
Neural Network Prediction of Carcinogenicity of Diverse Organic Compounds
inferred← citeshttps://doi.org/10.26021/3210openalex-snapshot
Development and Implementation of an Online Kraft Black Liquor Viscosity Soft Sensor
inferred← citeshttps://doi.org/10.2964/jsik.16.3_63openalex-snapshot
Toxicity Information and Its QSAR Prediction of Chemical Substances
inferred← citeshttps://doi.org/10.3233/ida-194477openalex-snapshot
Balanced training/test set sampling for proper evaluation of classification models
inferred← citeshttps://doi.org/10.3390/genes10110906openalex-snapshot
In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning
inferred← citeshttps://doi.org/10.3390/ijms20133170openalex-snapshot
In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach
inferred← citeshttps://doi.org/10.3390/ijms22094435openalex-snapshot
Deep Learning in Virtual Screening: Recent Applications and Developments
inferred← citeshttps://doi.org/10.3390/molecules23071820openalex-snapshot
Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme
inferred← citeshttps://doi.org/10.3390/ph15111383openalex-snapshot
Quantitative Structure–Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective
inferred← citeshttps://doi.org/10.4018/978-1-60960-860-6.ch008openalex-snapshot
Advances in Relevant Descriptor Selection
inferred← citeshttps://doi.org/10.4018/ijqspr.2017070108openalex-snapshot
Quantitative Structure–Activity Relationship Studies of Anticancer Activity for Isatin (1H-indole-2,3-dione) Derivatives Based on Density Functional Theory
inferred← citeshttps://doi.org/10.48550/arxiv.1406.1231openalex-snapshot
Multi-task Neural Networks for QSAR Predictions
inferred← citeshttps://doi.org/10.5120/8076-1476openalex-snapshot
Role of the Computational Intelligence in Drugs Discovery and Design: Introduction, Techniques and Software
inferred← citeshttps://doi.org/10.70675/2cdea8a9zeb86z4a40z922czc3c6e7779f96openalex-snapshot
Uncertainty quantification and propagation in rapid dynamics : application to crash problem
inferred← citeshttps://doi.org/10.70675/5f9f3534z76cez4c04z8be6z7920d2646fefopenalex-snapshot
Novel Quadruplex ligands : in silico and in vitro approaches
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.