On the public citation graph

Applications of neural networks in chemistry. 1. Prediction of electrophilic aromatic substitution reactions

0 signed · 62 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.
Claim with ORCID
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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What points at this work

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

inferred← citeshttps://doi.org/10.1002/amo.860020404openalex-snapshot
Neural network fitting and prediction of polarisability tensors for aromatic hydrocarbons
inferred← citeshttps://doi.org/10.1002/amo.860040104openalex-snapshot
Neural network analysis of molecular hyperpolarisabilities
inferred← citeshttps://doi.org/10.1002/ange.19931050405openalex-snapshot
Neuronale Netze in der Chemie
inferred← citeshttps://doi.org/10.1002/anie.199305031openalex-snapshot
Neural Networks in Chemistry
inferred← citeshttps://doi.org/10.1002/chem.201404081openalex-snapshot
Artificial Neural Networks for Guest Chirality Classification through Supramolecular Interactions
inferred← citeshttps://doi.org/10.1002/cjoc.20000180412openalex-snapshot
Quantitative prediction of substituted products based on quantum‐chemical parameters and neural network method
inferred← citeshttps://doi.org/10.1002/etc.5620130520openalex-snapshot
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR TOXICITY OF PHENOLS USING REGRESSION ANALYSIS AND COMPUTATIONAL NEURAL NETWORKS
inferred← citeshttps://doi.org/10.1002/ijch.202100016openalex-snapshot
What's Left for a Computational Chemist To Do in the Age of Machine Learning?
inferred← citeshttps://doi.org/10.1002/nadc.19910390207openalex-snapshot
Organische Chemie 1990
inferred← citeshttps://doi.org/10.1002/qsar.19930120108openalex-snapshot
New Trends in Structure‐Biodegradability Relationships
inferred← citeshttps://doi.org/10.1002/qsar.19970160306openalex-snapshot
Calculation of Octanol/Water Partition Coefficients (logP) using Artificial Neural Networks and Connection Matrices
inferred← citeshttps://doi.org/10.1007/978-3-540-36213-5_10openalex-snapshot
A Novel Approach to QSPR/QSAR Based on Neural Networks for Structures
inferred← citeshttps://doi.org/10.1016/0003-2670(92)85193-aopenalex-snapshot
Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data
inferred← citeshttps://doi.org/10.1016/0003-2670(93)85264-kopenalex-snapshot
Non-linear modelling of chemical data by combinations of linear and neural net methods
inferred← citeshttps://doi.org/10.1016/0045-6535(91)90186-hopenalex-snapshot
Prediction of biodegradability of organic chemicals by an artificial neural network
inferred← citeshttps://doi.org/10.1016/0166-1280(91)85103-eopenalex-snapshot
Application of neural networks in chemistry. Prediction of product distribution of nitration in a series of monosubstituted benzenes
inferred← citeshttps://doi.org/10.1016/0169-7439(93)80042-gopenalex-snapshot
Interpretation of infrared spectra with modular neural-network systems
inferred← citeshttps://doi.org/10.1016/0169-7439(93)80052-jopenalex-snapshot
Backpropagation neural networks
inferred← citeshttps://doi.org/10.1016/0223-5234(91)90010-kopenalex-snapshot
Structure-odor relationships using neural networks
inferred← citeshttps://doi.org/10.1016/0898-5529(90)90049-eopenalex-snapshot
Neural network applications in synthetic organic chemistry: I. A hybrid system which performs retrosynthetic analysis
inferred← citeshttps://doi.org/10.1016/0898-5529(90)90050-iopenalex-snapshot
Applications for neural networks in chemistry. 2. A general connectivity representation for the prediction of regiochemistry
inferred← citeshttps://doi.org/10.1016/0926-860x(95)00171-9openalex-snapshot
Application of a neural network to the analysis of catalytic reactions Analysis of NO decomposition over Cu/ZSM-5 zeolite
inferred← citeshttps://doi.org/10.1016/b978-012213810-2/50012-8openalex-snapshot
Computer-Aided Molecular Design Using Neural Networks and Genetic Algorithms
inferred← citeshttps://doi.org/10.1016/j.cis.2020.102314openalex-snapshot
Toward molecular characterization of asphaltene from different origins under different conditions by means of FT-IR spectroscopy
inferred← citeshttps://doi.org/10.1016/j.eurpolymj.2006.09.014openalex-snapshot
An approach to predict the 13C NMR chemical shifts of acrylonitrile copolymers using artificial neural network
inferred← citeshttps://doi.org/10.1016/j.molliq.2021.118128openalex-snapshot
Wettability alteration by surface adsorption of asphaltene molecular in carbonate porous media
inferred← citeshttps://doi.org/10.1016/j.petrol.2019.02.032openalex-snapshot
Experimental and mathematical model evaluation of asphaltene fractionation based on adsorption in porous media: Part 1. calcite reservoir rock
inferred← citeshttps://doi.org/10.1016/s0003-2670(00)80865-xopenalex-snapshot
Neural networks: A new method for solving chemical problems or just a passing phase?
inferred← citeshttps://doi.org/10.1016/s0003-2670(98)00777-6openalex-snapshot
The neural network MolNet prediction of alkane enthalpies
inferred← citeshttps://doi.org/10.1016/s0166-1280(00)00366-3openalex-snapshot
Artificial neural network applied to the discrimination of antibacterial activity by topological methods
inferred← citeshttps://doi.org/10.1016/s0922-3487(09)70013-9openalex-snapshot
8: Genetic Algorithms and Neural Networks
inferred← citeshttps://doi.org/10.1021/ac00020a022openalex-snapshot
Nonlinear multivariate calibration using principal components regression and artificial neural networks
inferred← citeshttps://doi.org/10.1021/ac00020a023openalex-snapshot
Fourier transform infrared spectroscopic method for the quantitative trace analysis of transition-metal carbonyl-labeled bioligands
inferred← citeshttps://doi.org/10.1021/ac00028a011openalex-snapshot
Counter-propagation neural networks in the modeling and prediction of Kovats indexes for substituted phenols
inferred← citeshttps://doi.org/10.1021/ac00028a012openalex-snapshot
pH gradient capillary zone electrophoresis using a solvent program delivery system
inferred← citeshttps://doi.org/10.1021/ac00034a015openalex-snapshot
Prediction of carbon-13 nuclear magnetic resonance chemical shifts by artificial neural networks
inferred← citeshttps://doi.org/10.1021/ac00034a016openalex-snapshot
Reduction of polyatomic ion interferences in inductively coupled plasma mass spectrometry by cryogenic desolvation
inferred← citeshttps://doi.org/10.1021/acs.accounts.8b00087openalex-snapshot
Machine Learning in Computer-Aided Synthesis Planning
inferred← citeshttps://doi.org/10.1021/ci00010a023openalex-snapshot
Application of recurrent neural networks in chemistry. Prediction and classification of carbon-13 NMR chemical shifts in a series of monosubstituted benzenes
inferred← citeshttps://doi.org/10.1021/ci940128yopenalex-snapshot
A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds
inferred← citeshttps://doi.org/10.1021/ci950102mopenalex-snapshot
Optimization of Functional Group Prediction from Infrared Spectra Using Neural Networks
inferred← citeshttps://doi.org/10.1021/ci9700384openalex-snapshot
Using Artificial Neural Networks To Classify the Activity of Capsaicin and Its Analogues
inferred← citeshttps://doi.org/10.1021/ci9903399openalex-snapshot
Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure−Activity Relationship Studies of Benzodiazepines
inferred← citeshttps://doi.org/10.1021/je1001973openalex-snapshot
Prediction of Ethene + Oct-1-ene Copolymerization Ideal Conditions Using Artificial Neuron Networks
inferred← citeshttps://doi.org/10.1021/jm980448zopenalex-snapshot
Artificial Neural Network Applied to Prediction of Fluorquinolone Antibacterial Activity by Topological Methods
inferred← citeshttps://doi.org/10.1021/jo100310vopenalex-snapshot
Validation of a Computational Model for Predicting the Site for Electrophilic Substitution in Aromatic Systems
inferred← citeshttps://doi.org/10.1021/jp409623jopenalex-snapshot
Electrophilic Aromatic Substitution: Enthalpies of Hydrogenation of the Ring Determine Reactivities of C6H5X. The Direction of the C6H5–X Bond Dipole Determines Orientation of the Substitution
inferred← citeshttps://doi.org/10.1023/a:1008368105614openalex-snapshot
Application of Cascade Correlation Networks for Structures to Chemistry
inferred← citeshttps://doi.org/10.1023/a:1018966222807openalex-snapshot
Introduction to Backpropagation Neural Network Computation
inferred← citeshttps://doi.org/10.1039/c2rp20010dopenalex-snapshot
Modes of reasoning in self-initiated study groups in chemistry
inferred← citeshttps://doi.org/10.1039/d5sc00541hopenalex-snapshot
Computational tools for the prediction of site- and regioselectivity of organic reactions
inferred← citeshttps://doi.org/10.1039/ft9949000097openalex-snapshot
Use of a neural network to determine the boiling point of alkanes
inferred← citeshttps://doi.org/10.1039/ft9949002015openalex-snapshot
Use of a neural network to determine the normal boiling points of acyclic ethers, peroxides, acetals and their sulfur analogues
inferred← citeshttps://doi.org/10.1039/p29910001755openalex-snapshot
Identifying functional groups in IR spectra using an artificial neural network
inferred← citeshttps://doi.org/10.1080/10629369308028830openalex-snapshot
Adapting the Structure of a Neural Network to Extract Chemical Information. Application to Structure-Odour Relationships
inferred← citeshttps://doi.org/10.1080/10629369508032982openalex-snapshot
On the Use of Backpropagation Neural Networks in Modeling Environmental Degradation
inferred← citeshttps://doi.org/10.1080/10629369708033251openalex-snapshot
QSARs for Toxicity of DDT-Type Analogs Using Neural Network
inferred← citeshttps://doi.org/10.1109/ijcnn.1998.682247openalex-snapshot
Quantitative structure-activity relationships of Benzodiazepines by recursive cascade correlation
inferred← citeshttps://doi.org/10.1111/j.2042-7158.1996.tb05978.xopenalex-snapshot
Neural Network Computer Simulation of Medical Aerosols
inferred← citeshttps://doi.org/10.2533/chimia.2023.22openalex-snapshot
Putting Chemical Knowledge to Work in Machine Learning for Reactivity
inferred← citeshttps://doi.org/10.3109/03639049409038390openalex-snapshot
Application of Neural Computing in Pharmaceutical Product Development: Computer Aided Formulation Design
inferred← citeshttps://doi.org/10.3762/bjoc.16.137openalex-snapshot
Models of necessity
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.