On the public citation graph
A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds
0 signed · 132 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known work and none has been retracted — and get a dated CiteStamped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/chin.199302282openalex-snapshot
ChemInform Abstract: Application of Neural Networks: Quantitative Structure‐Activity Relationships of the Derivatives of 2,4‐Diamino‐5‐(substituted‐benzyl) pyrimidines as DHFR Inhibitors.
inferredcites →https://doi.org/10.1002/ps.2780360212openalex-snapshot
The Use of Artificial Neural Networks in QSAR
inferredcites →https://doi.org/10.1002/ps.2780450211openalex-snapshot
Relating biological activity to chemical structure using neural networks
inferredcites →https://doi.org/10.1002/qsar.19910100103openalex-snapshot
An Application of Unsupervised Neural Network Methodology Kohonen Topology‐Preserving Mapping to QSAR Analysis
inferredcites →https://doi.org/10.1007/bf00337157openalex-snapshot
A hierarchical neural network model for associative memory
inferredcites →https://doi.org/10.1007/bf00344251openalex-snapshot
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
inferredcites →https://doi.org/10.1007/bf00363973openalex-snapshot
A neural network model for selective attention in visual pattern recognition
inferredcites →https://doi.org/10.1007/bf01192574openalex-snapshot
An application of neural networks in chemistry. Prediction of13C NMR chemical shifts
inferredcites →https://doi.org/10.1007/bf02174529openalex-snapshot
New approaches to QSAR: Neural networks and machine learning
inferredcites →https://doi.org/10.1007/bfb0051317openalex-snapshot
An algebraic model of constitutional chemistry as a basis for chemical computer programs
inferredcites →https://doi.org/10.1016/0003-2670(93)85300-9openalex-snapshot
Prediction of gas chromatographic retention index data by neural networks
inferredcites →https://doi.org/10.1016/0004-3702(89)90049-0openalex-snapshot
Connectionist learning procedures
inferredcites →https://doi.org/10.1016/0031-3203(82)90024-3openalex-snapshot
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
inferredcites →https://doi.org/10.1016/0097-8485(94)85008-9openalex-snapshot
Using molecular representations in combination with neural networks. A case study: Prediction of the HPLC retention index
inferredcites →https://doi.org/10.1016/0223-5234(91)90010-kopenalex-snapshot
Structure-odor relationships using neural networks
inferredcites →https://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
inferredcites →https://doi.org/10.1016/b978-0-08-051055-2.50029-8openalex-snapshot
CONNECTIONIST LEARNING PROCEDURES11This chapter appeared in Volume 40 of Artificial Intelligence in 1989, reprinted with permission of North-Holland Publishing. It is a revised version of Technical Report CMU-CS-87-115, which has the same title and was prepared in June 1987 while the author was at…
inferredcites →https://doi.org/10.1016/c2013-0-03750-4openalex-snapshot
Mathematical Analysis and its Applications
inferredcites →https://doi.org/10.1016/s0040-4020(01)88485-4openalex-snapshot
Neural network studies 2. Use of a neural net to estimate oxidation energies for substituted dihydropyridines and related heterocycles
inferredcites →https://doi.org/10.1021/ac00028a011openalex-snapshot
Counter-propagation neural networks in the modeling and prediction of Kovats indexes for substituted phenols
inferredcites →https://doi.org/10.1021/ac00028a012openalex-snapshot
pH gradient capillary zone electrophoresis using a solvent program delivery system
inferredcites →https://doi.org/10.1021/ci00009a015openalex-snapshot
Neural networks as nonlinear structure-activity relationship analyzers. Useful functions of the partial derivative method in multilayer neural networks
inferredcites →https://doi.org/10.1021/ci00010a022openalex-snapshot
Computational neural networks as model-free mapping devices
inferredcites →https://doi.org/10.1021/ci00014a009openalex-snapshot
Predicting phosphorus NMR shifts using neural networks
inferredcites →https://doi.org/10.1021/ci00015a012openalex-snapshot
Evaluation of neural networks based on radial basis functions and their application to the prediction of boiling points from structural parameters
inferredcites →https://doi.org/10.1021/ci00020a024openalex-snapshot
Applications of High-Resolution Self-Organizing Maps to Retrosynthetic and QSAR Analysis
inferredcites →https://doi.org/10.1021/ci00021a016openalex-snapshot
Correlation between Structure and Normal Boiling Points of Haloalkanes C1-C4 Using Neural Networks
inferredcites →https://doi.org/10.1021/ci00024a001openalex-snapshot
ChemNet: A Novel Neural Network Based Method for Graph/Property Mapping
inferredcites →https://doi.org/10.1021/ci00025a021openalex-snapshot
On the Basis of Invariants of Labeled Molecular Graphs
inferredcites →https://doi.org/10.1021/ci00027a017openalex-snapshot
Quantitative Structure-Activity Relationships in Carboquinones and Benzodiazepines Using Counter-Propagation Neural Networks
inferredcites →https://doi.org/10.1021/ci00027a018openalex-snapshot
Prediction of Chromatographic Retention Values (RM) and Partition Coefficients (log Poct) Using a Combination of Semiempirical Self-Consistent Reaction Field Calculations and Neural Networks
inferredcites →https://doi.org/10.1021/ci00068a020openalex-snapshot
Applications of neural networks in chemistry. 1. Prediction of electrophilic aromatic substitution reactions
inferredcites →https://doi.org/10.1021/cr00024a001openalex-snapshot
Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition
inferredcites →https://doi.org/10.1021/cr00098a007openalex-snapshot
QSAR studies on drugs acting at the central nervous system
inferredcites →https://doi.org/10.1021/ed064p575openalex-snapshot
Molecular structure: Property relationships
inferredcites →https://doi.org/10.1021/ja00025a009openalex-snapshot
Neural network studies. 1. Estimation of the aqueous solubility of organic compounds
inferredcites →https://doi.org/10.1021/ja00025a010openalex-snapshot
Computational studies on FK506: conformational search and molecular dynamics simulation in water
inferredcites →https://doi.org/10.1021/ja00179a044openalex-snapshot
Additivity methods in molecular polarizability
inferredcites →https://doi.org/10.1021/ja00221a015openalex-snapshot
Molecular modeling of the physical properties of alkanes
inferredcites →https://doi.org/10.1021/ja01636a111openalex-snapshot
Selected Values of Physical and Thermodynamic Properties of Hydrocarbons and Related Compounds.
inferredcites →https://doi.org/10.1021/jm00042a005openalex-snapshot
HIV-1 Reverse Transcriptase Inhibitor Design Using Artificial Neural Networks
inferredcites →https://doi.org/10.1021/jm00048a012openalex-snapshot
Analysis of linear and nonlinear QSAR data using neural networks
inferredcites →https://doi.org/10.1021/jm00059a003openalex-snapshot
Applications of neural networks in structure-activity relationships of a small number of molecules
inferredcites →https://doi.org/10.1021/jm00075a011openalex-snapshot
A Unified Framework for Using Neural Networks To Build QSARs
inferredcites →https://doi.org/10.1021/jm00095a016openalex-snapshot
Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors
inferredcites →https://doi.org/10.1021/jm00113a022openalex-snapshot
Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors
inferredcites →https://doi.org/10.1021/jm00165a005openalex-snapshot
Antioxidant-based inhibitors of leukotriene biosynthesis. The discovery of 6-[1-[2-(hydroxymethyl)phenyl]-1-propen-3-yl]-2,3-dihydro-5-benzofuranol, a potent topical antiinflammatory agent
inferredcites →https://doi.org/10.1021/jm00171a037openalex-snapshot
Neural networks applied to pharmaceutical problems. III. Neural networks applied to quantitative structure-activity relationship (QSAR) analysis
inferredcites →https://doi.org/10.1021/jm00171a038openalex-snapshot
Cyclization-activated prodrugs. Basic esters of 5-bromo-2'-deoxyuridine
inferredcites →https://doi.org/10.1063/1.47788openalex-snapshot
Anti AIDS drug design with the help of neural networks
inferredcites →https://doi.org/10.1080/10629369308028823openalex-snapshot
How to See Characteristics of Structural Parameters in QSAR Analysis: Descriptor Mapping Using Neural Networks
inferredcites →https://doi.org/10.1080/10629369308028824openalex-snapshot
Predicting the Impact Sensitivity of Explosive Molecules Using Neuromimetic Networks
inferredcites →https://doi.org/10.1080/10629369308028825openalex-snapshot
Application of Neural Networks in the QSAR Analysis of Percent Effect Biological Data: Comparison with Adaptive Least Squares and Nonlinear Regression Analysis
inferredcites →https://doi.org/10.1080/10629369308028828openalex-snapshot
Neural Network Classification of Mutagens Using Structural Fragment Data
inferredcites →https://doi.org/10.1080/10629369308028829openalex-snapshot
Estimating Pesticide Field Half-lives from a Backpropagation Neural Network
inferredcites →https://doi.org/10.1113/jphysiol.1962.sp006837openalex-snapshot
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
inferredcites →https://doi.org/10.1126/science.1411538openalex-snapshot
Neural Computing in Cancer Drug Development: Predicting Mechanism of Action
inferredcites →https://doi.org/10.1126/science.258.5081.451openalex-snapshot
Age and Duration of Weathering by 40 K- 40 Ar and 40 Ar/ 39 Ar Analysis of Potassium-Manganese Oxides
inferredcites →https://doi.org/10.1248/cpb.37.2558openalex-snapshot
NEURAL NETWORKS APPLIED TO PEARMACEUTICAL PROBLEMS. I. METHOD AND APPLICATION TO DECISION MAKING
inferredcites →https://doi.org/10.1248/cpb.40.2962openalex-snapshot
Comparison of the Functional-Link Net and the Generalized Delta Rule Net in Quantitative Structure-Activity Relationship Studies.
inferredcites →https://doi.org/10.5860/choice.29-2151openalex-snapshot
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9780470125939.ch2openalex-snapshot
Artificial Neural Networks and Their use in Chemistry
inferred← citeshttps://doi.org/10.1002/bkcs.12267openalex-snapshot
Effects of Pooling Operations on Prediction of Ligand Rotation‐Dependent Protein–Ligand Binding in 3D Graph Convolutional Network
inferred← citeshttps://doi.org/10.1002/chin.199742301openalex-snapshot
ChemInform Abstract: A Neural Device for Searching Direct Correlations Between Structures and Properties of Chemical Compounds.
inferred← citeshttps://doi.org/10.1002/chin.200129300openalex-snapshot
New Neural Networks for Structure—Property Models
inferred← citeshttps://doi.org/10.1002/cmdc.202100418openalex-snapshot
Application of Deep Neural Network Models in Drug Discovery Programs
inferred← citeshttps://doi.org/10.1002/minf.201800108openalex-snapshot
A Survey of Multi‐task Learning Methods in Chemoinformatics
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_134openalex-snapshot
Drug Design with Artificial Neural Networks
inferred← citeshttps://doi.org/10.1007/978-1-60327-101-1_8openalex-snapshot
Neural Networks in Building QSAR Models
inferred← citeshttps://doi.org/10.1007/978-3-031-47665-5_9openalex-snapshot
Edge Based Architecture for Total Energy Regression Models for Computational Materials Science
inferred← citeshttps://doi.org/10.1007/s10947-005-0037-2openalex-snapshot
Fragment descriptors in QSPR: Application to magnetic susceptibility calculations
inferred← citeshttps://doi.org/10.1007/s10994-021-05966-zopenalex-snapshot
Incorporating symbolic domain knowledge into graph neural networks
inferred← citeshttps://doi.org/10.1007/s11094-017-1595-2openalex-snapshot
Application of Molecular Topological Descriptors for Clustering a Database of Isothiourea Derivatives in Studying Structure – Activity Relationships
inferred← citeshttps://doi.org/10.1007/s11167-005-0439-0openalex-snapshot
A Study of the Affinity of Dyes for Cellulose Fiber within the Framework of a Fragment Approach in QSPR
inferred← citeshttps://doi.org/10.1007/s11172-016-1310-6openalex-snapshot
Online resources for the prediction of biological activity of organic compounds
inferred← citeshttps://doi.org/10.1007/s11704-024-40490-yopenalex-snapshot
A survey on deep learning-based algorithms for the traveling salesman problem
inferred← citeshttps://doi.org/10.1007/s12293-024-00423-5openalex-snapshot
ResGAT: Residual Graph Attention Networks for molecular property prediction
inferred← citeshttps://doi.org/10.1007/s42452-025-07811-7openalex-snapshot
p-GIN: a graph isomorphism network based on p-laplacian operator to enhance molecular property prediction
inferred← citeshttps://doi.org/10.1007/springerreference_60306openalex-snapshot
Drug Design with Artificial Neural Networks
inferred← citeshttps://doi.org/10.1016/b978-0-323-90392-9.00008-2openalex-snapshot
Innovations in dyes and chemoinformatics approach
inferred← citeshttps://doi.org/10.1016/j.drudis.2018.05.010openalex-snapshot
Machine learning in chemoinformatics and drug discovery
inferred← citeshttps://doi.org/10.1016/j.ipm.2023.103333openalex-snapshot
PO-GNN: Position-observant inductive graph neural networks for position-based prediction
inferred← citeshttps://doi.org/10.1016/j.molliq.2022.118616openalex-snapshot
Benchmarking machine learning methods for modeling physical properties of ionic liquids
inferred← citeshttps://doi.org/10.1016/j.tifs.2021.05.031openalex-snapshot
Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects
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/s0079-6107(98)00026-1openalex-snapshot
Artificial neural networks for computer-based molecular design
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01099openalex-snapshot
HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01220openalex-snapshot
VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01728openalex-snapshot
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00587openalex-snapshot
Graph Convolutional Neural Networks as “General-Purpose” Property Predictors: The Universality and Limits of Applicability
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00713openalex-snapshot
Image Based Liver Toxicity Prediction
inferred← citeshttps://doi.org/10.1021/acscentsci.7b00512openalex-snapshot
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1021/ci0001637openalex-snapshot
QSAR for Boiling Points of “Small” Sulfides. Are the “High-Quality Structure-Property-Activity Regressions” the Real High Quality QSAR Models?
inferred← citeshttps://doi.org/10.1021/ci200409xopenalex-snapshot
Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
inferred← citeshttps://doi.org/10.1021/ci9901340openalex-snapshot
Modeling of Ion Complexation and Extraction Using Substructural Molecular Fragments
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/10.1023/a:1024736719258openalex-snapshot
Fragmental descriptors in QSPR: application to molecular polarizability calculations
inferred← citeshttps://doi.org/10.1023/b:rjac.0000022438.70174.16openalex-snapshot
Calculation of the Enthalpy of Sublimation by the QSPR Method with the Use of a Fragment Approach
inferred← citeshttps://doi.org/10.1023/b:rucb.0000009629.38661.4copenalex-snapshot
Fragmental descriptors in QSPR: flash point calculations
inferred← citeshttps://doi.org/10.1023/b:rujo.0000043708.34952.eeopenalex-snapshot
QSPR: Application to Heat Capacity Calculation
inferred← citeshttps://doi.org/10.1038/s41598-020-78169-6openalex-snapshot
Layer-wise relevance propagation of InteractionNet explains protein–ligand interactions at the atom level
inferred← citeshttps://doi.org/10.1039/c7sc02664aopenalex-snapshot
MoleculeNet: a benchmark for molecular machine learning
inferred← citeshttps://doi.org/10.1039/d4dd00246fopenalex-snapshot
Embedding material graphs using the electron-ion potential: application to material fracture
inferred← citeshttps://doi.org/10.1080/10590500802135578openalex-snapshot
Directions in QSAR Modeling for Regulatory Uses in OECD Member Countries, EU and in Russia
inferred← citeshttps://doi.org/10.1080/10629360008033235openalex-snapshot
Polynomial Neural Network for Linear and Non-linear Model Selection in Quantitative-Structure Activity Relationship Studies on the Internet
inferred← citeshttps://doi.org/10.1080/17460441.2016.1201262openalex-snapshot
A renaissance of neural networks in drug discovery
inferred← citeshttps://doi.org/10.1080/17460441.2020.1745183openalex-snapshot
The power of deep learning to ligand-based novel drug discovery
inferred← citeshttps://doi.org/10.1134/s0036024407010037openalex-snapshot
Fragment descriptors in the QSPR method: Their use for calculating the enthalpies of vaporization of organic substances
inferred← citeshttps://doi.org/10.1137/1.9781611976700.38openalex-snapshot
Random Features Strengthen Graph Neural Networks
inferred← citeshttps://doi.org/10.1145/3605573.3605577openalex-snapshot
FastDimeNet++: Training DimeNet++ in 22 minutes
inferred← citeshttps://doi.org/10.1517/13543776.8.3.249openalex-snapshot
Applications of soft computing in drug design
inferred← citeshttps://doi.org/10.1609/aaai.v36i9.21262openalex-snapshot
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers
inferred← citeshttps://doi.org/10.1609/aaai.v37i9.26318openalex-snapshot
Substructure Aware Graph Neural Networks
inferred← citeshttps://doi.org/10.1897/01-363openalex-snapshot
Quantitative structure-property relationships for prediction of boiling point, vapor pressure, and melting point
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3169151/v1openalex-snapshot
VGAE-MCTS: A New Molecular Generative Model Combining Variational Graph Auto-Encoder and Monte Carlo Tree Search
inferred← citeshttps://doi.org/10.26686/wgtn.25044362openalex-snapshot
De novo Molecular Design using Deep Learning
inferred← citeshttps://doi.org/10.3103/s0027131407050112openalex-snapshot
Calculation of the stability of β-cyclodextrin complexes of organic compounds using the QSPR approach
inferred← citeshttps://doi.org/10.3390/colorants1030017openalex-snapshot
Chemoinformatics Analysis of the Colour Fastness Properties of Acid and Direct Dyes in Textile Coloration
inferred← citeshttps://doi.org/10.3390/informatics10010008openalex-snapshot
Motif-Based Graph Representation Learning with Application to Chemical Molecules
inferred← citeshttps://doi.org/10.4230/oasics.dx.2026.1openalex-snapshot
A Realization-Theoretic Foundation for Fault Diagnosis, with Diagnosability Equivalence Predictions
inferred← citeshttps://doi.org/10.48550/arxiv.1912.13007openalex-snapshot
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces
inferred← citeshttps://doi.org/10.48550/arxiv.2003.04078openalex-snapshot
A Survey on The Expressive Power of Graph Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.2005.06316openalex-snapshot
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks
inferred← citeshttps://doi.org/10.48550/arxiv.2110.00577openalex-snapshot
Reconstruction for Powerful Graph Representations
inferred← citeshttps://doi.org/10.48550/arxiv.2110.02910openalex-snapshot
Equivariant Subgraph Aggregation Networks
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.