On the public citation graph
Volume Learning Algorithm Artificial Neural Networks for 3D QSAR Studies
0 signed · 78 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known work and none has been retracted — and get a dated CiteStamped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/(sici)1098-1128(199905)19:3<249::aid-med4>3.0.co;2-0openalex-snapshot
Application of neural networks in structure-activity relationships
inferredcites →https://doi.org/10.1002/(sici)1099-128x(199703)11:2<141::aid-cem461>3.0.co;2-2openalex-snapshot
INLR, implicit non-linear latent variable regression
inferredcites →https://doi.org/10.1002/(sici)1099-128x(199905/08)13:3/4<461::aid-cem555>3.0.co;2-bopenalex-snapshot
A serial extension of multiblock PLS
inferredcites →https://doi.org/10.1002/1521-3838(200010)19:4<345::aid-qsar345>3.0.co;2-qopenalex-snapshot
GIFI-PLS: Modeling of Non-Linearities and Discontinuities in QSAR
inferredcites →https://doi.org/10.1007/bf00134176openalex-snapshot
The comparison of geometric and electronic properties of molecular surfaces by neural networks: Application to the analysis of corticosteroid-binding globulin activity of steroids
inferredcites →https://doi.org/10.1007/bf00342633openalex-snapshot
Cognitron: A self-organizing multilayered neural network
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/10.1016/0960-894x(95)00560-gopenalex-snapshot
C-Attached aminoalkylindoles: potent cannabinoid mimetics
inferredcites →https://doi.org/10.1016/b978-0-12-213815-7.x5000-6openalex-snapshot
Neural Networks in QSAR and Drug Design
inferredcites →https://doi.org/10.1016/s0223-5234(00)00108-2openalex-snapshot
Validation of protein-based alignment in 3D quantitative structure–activity relationships with CoMFA models
inferredcites →https://doi.org/10.1016/s0223-5234(99)80052-xopenalex-snapshot
Neural networks in drug discovery: have they lived up to their promise?
inferredcites →https://doi.org/10.1016/s0893-6080(97)00005-1openalex-snapshot
Efficient Partition of Learning Data Sets for Neural Network Training
inferredcites →https://doi.org/10.1021/ci00027a006openalex-snapshot
Neural network studies. 1. Comparison of overfitting and overtraining
inferredcites →https://doi.org/10.1021/ci950204copenalex-snapshot
Neural Network Studies. 2. Variable Selection
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/ci990314+openalex-snapshot
Use of Artificial Neural Networks in a QSAR Study of Anti-HIV Activity for a Large Group of HEPT Derivatives
inferredcites →https://doi.org/10.1021/ja00073a034openalex-snapshot
A combined application of two different neural network types for the prediction of chemical reactivity
inferredcites →https://doi.org/10.1021/ja00226a005openalex-snapshot
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
inferredcites →https://doi.org/10.1021/jm00016a013openalex-snapshot
Aminoalkylindoles: Structure-Activity Relationships of Novel Cannabinoid Mimetics
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/jm00079a016openalex-snapshot
Conformationally restrained analogs of pravadoline: nanomolar potent, enantioselective, (aminoalkyl)indole agonists of the cannabinoid receptor
inferredcites →https://doi.org/10.1021/jm00079a017openalex-snapshot
2.beta.-Substituted analogs of cocaine. Synthesis and inhibition of binding to the cocaine receptor
inferredcites →https://doi.org/10.1021/jm00145a002openalex-snapshot
A computational procedure for determining energetically favorable binding sites on biologically important macromolecules
inferredcites →https://doi.org/10.1021/jm00145a003openalex-snapshot
Comparative computer graphics and solution studies of the DNA interaction of substituted anthraquinones based on doxorubicin and mitoxantrone
inferredcites →https://doi.org/10.1073/pnas.79.8.2554openalex-snapshot
Neural networks and physical systems with emergent collective computational abilities.
inferredcites →https://doi.org/10.1201/9780429499661openalex-snapshot
Introduction to the Theory of Neural Computation
inferredcites →https://doi.org/10.1517/13543776.6.3.239openalex-snapshot
Review Biologicals & Immunologicals: Applications of artificial neural networks to quantitative structure-activity relationships
inferredcites →https://doi.org/10.2307/2003035openalex-snapshot
Mathematical Handbook for Scientists and Engineers
inferredcites →https://doi.org/10.7551/mitpress/5236.001.0001openalex-snapshot
Parallel Distributed Processing
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/10.1002/9780470571224.pse004openalex-snapshot
Molecular Similarity Methods and QSAR Models As Tools for Virtual Screening
inferred← citeshttps://doi.org/10.1002/ddr.20410openalex-snapshot
In silico structure‐activity‐relationship (SAR) models from machine learning: a review
inferred← citeshttps://doi.org/10.1002/minf.201300180openalex-snapshot
Global Performance and Trend of QSAR/QSPR Research: A Bibliometric Analysis
inferred← citeshttps://doi.org/10.1002/qsar.200330860openalex-snapshot
Status of HTS Data Mining Approaches
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/s00044-017-1828-7openalex-snapshot
An improvement on the prediction power of the 3D-QSAR CoMFA models using a hybrid of statistical and machine learning methods: a case study on γ‑secretase modulators of Alzheimer’s disease
inferred← citeshttps://doi.org/10.1007/s005210200013openalex-snapshot
Investigation of Structure – Biodegradability Relationships in Polychlorinated Biphenyls Using Self-Organising Maps
inferred← citeshttps://doi.org/10.1007/s10822-008-9195-6openalex-snapshot
Quantitative Series Enrichment Analysis (QSEA): a novel procedure for 3D-QSAR analysis
inferred← citeshttps://doi.org/10.1007/s10822-013-9656-4openalex-snapshot
The continuous molecular fields approach to building 3D-QSAR models
inferred← citeshttps://doi.org/10.1016/b978-0-12-801505-6.00007-7openalex-snapshot
Validation of QSAR Models
inferred← citeshttps://doi.org/10.1016/j.bmc.2005.05.011openalex-snapshot
Synthesis of novel 5-aryl-2-thio-1,3,4-oxadiazoles and the study of their structure–anti-mycobacterial activities
inferred← citeshttps://doi.org/10.1016/j.bmc.2011.09.038openalex-snapshot
The structure–antituberculosis activity relationships study in a series of 5-aryl-2-thio-1,3,4-oxadiazole derivatives
inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.10.005openalex-snapshot
Efficient variable selection batch pruning algorithm for artificial neural networks
inferred← citeshttps://doi.org/10.1016/j.ejmech.2009.09.008openalex-snapshot
Application of desirability-based multi(bi)-objective optimization in the design of selective arylpiperazine derivates for the 5-HT1A serotonin receptor
inferred← citeshttps://doi.org/10.1016/j.jmgm.2004.03.003openalex-snapshot
Generation of QSAR sets with a self-organizing map
inferred← citeshttps://doi.org/10.1016/j.jmgm.2007.03.005openalex-snapshot
Volume learning algorithm significantly improved PLS model for predicting the estrogenic activity of xenoestrogens
inferred← citeshttps://doi.org/10.1016/j.jmgm.2015.06.006openalex-snapshot
The study of dual COX-2/5-LOX inhibitors by using electronic-topological approach based on data on the ligand–receptor interactions
inferred← citeshttps://doi.org/10.1016/j.polymdegradstab.2025.111733openalex-snapshot
Degradation mechanism and modified degradation model of high-degradation epoxy materials with different functional group vitrimers
inferred← citeshttps://doi.org/10.1021/ci010379oopenalex-snapshot
Neural Network Studies. 4. Introduction to Associative Neural Networks
inferred← citeshttps://doi.org/10.1021/ci030340eopenalex-snapshot
Artificial Neural Networks and Linear Discriminant Analysis: A Valuable Combination in the Selection of New Antibacterial Compounds
inferred← citeshttps://doi.org/10.1021/ci200411sopenalex-snapshot
A 3D-QSAR-Driven Approach to Binding Mode and Affinity Prediction
inferred← citeshttps://doi.org/10.1021/jm020194oopenalex-snapshot
Topomer CoMFA: A Design Methodology for Rapid Lead Optimization
inferred← citeshttps://doi.org/10.1021/jm0502743openalex-snapshot
Synthesis and Structure−Activity Relationship of a Novel Series of Aminoalkylindoles with Potential for Imaging the Neuronal Cannabinoid Receptor by Positron Emission Tomography
inferred← citeshttps://doi.org/10.1021/jm0505157openalex-snapshot
3D-QSAR Studies on Cannabinoid CB1 Receptor Agonists: G-Protein Activation as Biological Data
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/10.1080/10406638.2019.1692878openalex-snapshot
Molecular Docking of a Bio Material [1-{[(Z)-Cyclopentylidene] Amino}-3-Phenylthiourea] by First Principles Study
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/10.1080/1062936x.2021.1883107openalex-snapshot
Cross-validation strategies in QSPR modelling of chemical reactions
inferred← citeshttps://doi.org/10.1080/17460441.2016.1201262openalex-snapshot
A renaissance of neural networks in drug discovery
inferred← citeshttps://doi.org/10.1109/cibcb.2011.5948478openalex-snapshot
Comparative analysis of machine learning techniques for the prediction of logP
inferred← citeshttps://doi.org/10.1109/cibcb.2012.6217246openalex-snapshot
GPU-accelerated machine learning techniques enable QSAR modeling of large HTS data
inferred← citeshttps://doi.org/10.1109/icdmw.2006.51openalex-snapshot
Context-Aware Visual Exploration of Molecular Datab
inferred← citeshttps://doi.org/10.1109/ijcnn.2007.4371399openalex-snapshot
Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds
inferred← citeshttps://doi.org/10.1155/2010/693031openalex-snapshot
ETM-ANN Approach Application for Thiobenzamide and Quinolizidine Derivatives
inferred← citeshttps://doi.org/10.1201/9780203010822.ch1openalex-snapshot
General Introduction to QSAR
inferred← citeshttps://doi.org/10.1248/cpb.52.751openalex-snapshot
Application of a Self-Organizing Map to Quantitative Structure-Activity Relationship Analysis of Carboquinone and Benzodiazepine
inferred← citeshttps://doi.org/10.1248/cpb.53.1570openalex-snapshot
An Analysis of Thyroid Function Diagnosis Using Bayesian-Type and SOM-Type Neural Networks
inferred← citeshttps://doi.org/10.1517/17460441.2.12.1567openalex-snapshot
On some aspects of validation of predictive quantitative structure–activity relationship models
inferred← citeshttps://doi.org/10.1897/01-198openalex-snapshot
Structure-activity relationship approaches and applications
inferred← citeshttps://doi.org/10.2174/0113895575400393251007093333openalex-snapshot
Research Hotspots and Trends of Artificial Intelligence in Drug Discovery: A Review and Bibliometric Analysis
inferred← citeshttps://doi.org/10.3233/ida-194477openalex-snapshot
Balanced training/test set sampling for proper evaluation of classification models
inferred← citeshttps://doi.org/10.70675/2b63c353z8184z4bbbzbfbcz9ce124edd263openalex-snapshot
New methods of multiscale chemical space analysis : visualization of structure-activity relationships and structural pattern extraction
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.