On the public citation graph

Benchmarking of QSAR Models for Blood-Brain Barrier Permeation

0 signed · 123 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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 this work says

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

inferredcites →https://doi.org/10.1002/1521-3838(200205)21:1<23::aid-qsar23>3.0.co;2-eopenalex-snapshot
Fragmental Methods in the Design of New Compounds. Applications of The Advanced Algorithm Builder
inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1002/bit.21214openalex-snapshot
MODEL—molecular descriptor lab: A web‐based server for computing structural and physicochemical features of compounds
inferredcites →https://doi.org/10.1002/cem.858openalex-snapshot
Monte Carlo cross‐validation for selecting a model and estimating the prediction error in multivariate calibration
inferredcites →https://doi.org/10.1002/jcc.540070419openalex-snapshot
Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity
inferredcites →https://doi.org/10.1002/jps.20081openalex-snapshot
TOPS‐MODE Approach for the Prediction of Blood–Brain Barrier Permeation
inferredcites →https://doi.org/10.1002/jps.20595openalex-snapshot
A data base for partition of volatile organic compounds and drugs from blood/plasma/serum to brain, and an LFER analysis of the data
inferredcites →https://doi.org/10.1002/prot.10556openalex-snapshot
Critical assessment of methods of protein structure prediction (CASP)-round V
inferredcites →https://doi.org/10.1002/qsar.19850040303openalex-snapshot
A Shape Index from Molecular Graphs
inferredcites →https://doi.org/10.1002/qsar.19850040304openalex-snapshot
A Method for Determining the Theoretical Carcinogenicity of Polycyclic Aromatic Hydrocarbons
inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/s00894-002-0101-1openalex-snapshot
ADME evaluation in drug discovery
inferredcites →https://doi.org/10.1007/s10822-005-8694-yopenalex-snapshot
Virtual Computational Chemistry Laboratory – Design and Description
inferredcites →https://doi.org/10.1016/j.bmc.2005.01.061openalex-snapshot
In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method
inferredcites →https://doi.org/10.1016/j.bmc.2006.03.012openalex-snapshot
Correlation of blood–brain penetration using structural descriptors
inferredcites →https://doi.org/10.1016/s0169-7439(00)00122-2openalex-snapshot
Monte Carlo cross validation
inferredcites →https://doi.org/10.1016/s0223-5234(01)01269-7openalex-snapshot
Correlation and prediction of a large blood–brain distribution data set—an LFER study
inferredcites →https://doi.org/10.1016/s0378-5173(00)00422-1openalex-snapshot
A simple model for the prediction of blood–brain partitioning
inferredcites →https://doi.org/10.1016/s0925-4439(02)00074-1openalex-snapshot
A simple model to predict blood–brain barrier permeation from 3D molecular fields
inferredcites →https://doi.org/10.1016/s1359-6446(03)02827-7openalex-snapshot
In silico prediction of blood–brain barrier permeation
inferredcites →https://doi.org/10.1021/ci00063a006openalex-snapshot
Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics
inferredcites →https://doi.org/10.1021/ci00063a007openalex-snapshot
Review of ring perception algorithms for chemical graphs
inferredcites →https://doi.org/10.1021/ci010127nopenalex-snapshot
Modeling Blood-Brain Barrier Partitioning Using the Electrotopological State
inferredcites →https://doi.org/10.1021/ci025584yopenalex-snapshot
The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo- and Bioinformatics
inferredcites →https://doi.org/10.1021/ci030304fopenalex-snapshot
A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption
inferredcites →https://doi.org/10.1021/ci034134iopenalex-snapshot
ADME Evaluation in Drug Discovery. 3. Modeling Blood-Brain Barrier Partitioning Using Simple Molecular Descriptors
inferredcites →https://doi.org/10.1021/ci0498057openalex-snapshot
Constructing Optimum Blood Brain Barrier QSAR Models Using a Combination of 4D-Molecular Similarity Measures and Cluster Analysis
inferredcites →https://doi.org/10.1021/ci050135uopenalex-snapshot
Effect of Selection of Molecular Descriptors on the Prediction of Blood−Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods
inferredcites →https://doi.org/10.1021/ci050303iopenalex-snapshot
In Silico Prediction of Blood Brain Barrier Permeability: An Artificial Neural Network Model
inferredcites →https://doi.org/10.1021/ci060064eopenalex-snapshot
Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
inferredcites →https://doi.org/10.1021/ci600385wopenalex-snapshot
Prediction of Blood-Βrain Partitioning and Human Serum Albumin Binding Based on COSMO-RS σ-Moments
inferredcites →https://doi.org/10.1021/ci980098popenalex-snapshot
A New Topological Index for QSPR of Alkanes
inferredcites →https://doi.org/10.1021/ci980339topenalex-snapshot
Estimation of Molecular Linear Free Energy Relation Descriptors Using a Group Contribution Approach
inferredcites →https://doi.org/10.1021/ja993663topenalex-snapshot
Prediction of Properties from Simulations: Free Energies of Solvation in Hexadecane, Octanol, and Water
inferredcites →https://doi.org/10.1021/jm000942eopenalex-snapshot
Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties
inferredcites →https://doi.org/10.1021/jm020155copenalex-snapshot
Do Structurally Similar Molecules Have Similar Biological Activity?
inferredcites →https://doi.org/10.1023/a:1012240703377openalex-snapshot
Prediction of blood-brain partitioning using Monte Carlo simulations of molecules in water
inferredcites →https://doi.org/10.1023/a:1015952613760openalex-snapshot
An Electrotopological-State Index for Atoms in Molecules
inferredcites →https://doi.org/10.1023/b:jcam.0000017372.32162.37openalex-snapshot
Computational models to predict blood–brain barrier permeation and CNS activity
inferredcites →https://doi.org/10.1038/nrd1032openalex-snapshot
ADMET in silico modelling: towards prediction paradise?
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1080/01621459.1993.10476299openalex-snapshot
Linear Model Selection by Cross-Validation
inferredcites →https://doi.org/10.7551/mitpress/4175.001.0001openalex-snapshot
Learning with Kernels

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9781394167258.ch6openalex-snapshot
Recent Advances in Drug Design With Machine Learning
inferred← citeshttps://doi.org/10.1002/9783527628766.biblioopenalex-snapshot
Bibliography
inferred← citeshttps://doi.org/10.1002/9783527645121.ch2openalex-snapshot
Developing Best Practices for Descriptor‐Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations
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/cmdc.201800533openalex-snapshot
In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods
inferred← citeshttps://doi.org/10.1002/jps.21745openalex-snapshot
In vivo, in vitro and in silico methods for small molecule transfer across the BBB
inferred← citeshttps://doi.org/10.1002/minf.201300009openalex-snapshot
Rough Set Theory as an Interpretable Method for Predicting the Inhibition of Cytochrome P450 1A2 and 2D6
inferred← citeshttps://doi.org/10.1002/minf.201400118openalex-snapshot
Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set
inferred← citeshttps://doi.org/10.1002/qsar.200810175openalex-snapshot
COSMO‐RS as a Predictive Tool for Lipophilicity
inferred← citeshttps://doi.org/10.1002/qsar.200960053openalex-snapshot
Multi‐Objective Feature Selection in QSAR Using a Machine Learning Approach
inferred← citeshttps://doi.org/10.1007/978-0-387-30440-3_135openalex-snapshot
Drug Design with Machine Learning
inferred← citeshttps://doi.org/10.1007/978-1-0716-0838-8_9openalex-snapshot
In Vitro Models of Central Nervous System Barriers for Blood-Brain Barrier Permeation Studies
inferred← citeshttps://doi.org/10.1007/978-1-4020-9783-6_10openalex-snapshot
In Silico Approaches for Predicting Adme Properties
inferred← citeshttps://doi.org/10.1007/978-3-030-33582-3_25openalex-snapshot
Methods to Improve Ranking Chemical Structures in Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1007/978-3-319-56850-8_9openalex-snapshot
QSAR/QSPR Modeling in the Design of Drug Candidates with Balanced Pharmacodynamic and Pharmacokinetic Properties
inferred← citeshttps://doi.org/10.1007/978-981-19-2535-1_33openalex-snapshot
Assessing Permeability Prediction of BBB in the Central Nervous System Using ML
inferred← citeshttps://doi.org/10.1007/s00044-011-9748-4openalex-snapshot
Quantitative structure-activity relationship study and directed synthesis of Thieno[2,3-d]pyrimidine-2,4-diones as monocarboxylate transporter 1 inhibitors
inferred← citeshttps://doi.org/10.1007/s00204-022-03252-yopenalex-snapshot
Prediction reliability of QSAR models: an overview of various validation tools
inferred← citeshttps://doi.org/10.1007/s00894-024-06188-5openalex-snapshot
Exploring blood–brain barrier passage using atomic weighted vector and machine learning
inferred← citeshttps://doi.org/10.1007/s10822-010-9401-1openalex-snapshot
Toward better QSAR/QSPR modeling: simultaneous outlier detection and variable selection using distribution of model features
inferred← citeshttps://doi.org/10.1007/s10822-011-9478-1openalex-snapshot
Qualitative prediction of blood–brain barrier permeability on a large and refined dataset
inferred← citeshttps://doi.org/10.1007/s10822-023-00525-1openalex-snapshot
A least-squares-fitting procedure for an efficient preclinical ranking of passive transport across the blood–brain barrier endothelium
inferred← citeshttps://doi.org/10.1007/s11030-014-9562-2openalex-snapshot
Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability
inferred← citeshttps://doi.org/10.1007/s11030-016-9715-6openalex-snapshot
The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood–brain barrier (BBB) permeability
inferred← citeshttps://doi.org/10.1007/s11030-018-9866-8openalex-snapshot
ADME properties evaluation in drug discovery: in silico prediction of blood–brain partitioning
inferred← citeshttps://doi.org/10.1007/s11094-012-0748-6openalex-snapshot
Quantitative interaction between the structures of organic compounds and their abilities to penetrate the blood-brain barrier
inferred← citeshttps://doi.org/10.1007/s11094-017-1553-zopenalex-snapshot
A Ranged Series of Drug Molecule Fragments Defining Their Neuroavailability
inferred← citeshttps://doi.org/10.1007/s11095-008-9584-5openalex-snapshot
New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules
inferred← citeshttps://doi.org/10.1016/b978-0-444-64165-6.03018-4openalex-snapshot
High-Performance GRID Computing in Chemoinformatics
inferred← citeshttps://doi.org/10.1016/b978-044452701-1.00005-3openalex-snapshot
High-Performance GRID Computing in Chemoinformatics
inferred← citeshttps://doi.org/10.1016/j.addr.2015.01.001openalex-snapshot
Advances in computationally modeling human oral bioavailability
inferred← citeshttps://doi.org/10.1016/j.chemolab.2010.06.008openalex-snapshot
Automatic feature subset selection for decision tree-based ensemble methods in the prediction of bioactivity
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2021.130330openalex-snapshot
Development of blood brain barrier permeation prediction models for organic and inorganic biocidal active substances
inferred← citeshttps://doi.org/10.1016/j.cherd.2026.06.034openalex-snapshot
AI-driven prediction of drug-likeness for peptides: An evaluation of newly created MLP and k-NN models
inferred← citeshttps://doi.org/10.1016/j.cmpb.2017.02.004openalex-snapshot
Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies
inferred← citeshttps://doi.org/10.1016/j.drudis.2014.10.012openalex-snapshot
Machine-learning approaches in drug discovery: methods and applications
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117287openalex-snapshot
Deep Convolutional Neural Network to improve the performances of screening process in LBVS
inferred← citeshttps://doi.org/10.1016/j.ijpharm.2010.05.037openalex-snapshot
Application of PAMPA-models to predict BBB permeability including efflux ratio, plasma protein binding and physicochemical parameters
inferred← citeshttps://doi.org/10.1016/j.jconrel.2022.01.011openalex-snapshot
In vitro blood brain barrier models: An overview
inferred← citeshttps://doi.org/10.1016/j.jmgm.2010.03.010openalex-snapshot
Prediction of passive blood–brain partitioning: Straightforward and effective classification models based on in silico derived physicochemical descriptors
inferred← citeshttps://doi.org/10.1016/j.jmgm.2010.06.006openalex-snapshot
Prediction of blood–brain partitioning: A model based on molecular electronegativity distance vector descriptors
inferred← citeshttps://doi.org/10.1016/j.tranon.2021.101087openalex-snapshot
Bioengineered microfluidic blood-brain barrier models in oncology research
inferred← citeshttps://doi.org/10.1016/j.vascn.2017.04.014openalex-snapshot
QSAR model for blood-brain barrier permeation
inferred← citeshttps://doi.org/10.1016/s1574-1400(09)00505-2openalex-snapshot
Chapter 5 Recent Advances on in silico ADME Modeling
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.0c00343openalex-snapshot
Prediction of the Blood–Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00951openalex-snapshot
BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00590openalex-snapshot
Predicting the Brain-To-Plasma Unbound Partition Coefficient of Compounds via Formula-Guided Network
inferred← citeshttps://doi.org/10.1021/bk-2016-1222.ch014openalex-snapshot
Steps Toward a Virtual Rat: Predictive Absorption, Distribution, Metabolism, and Toxicity Models
inferred← citeshttps://doi.org/10.1021/ci700283sopenalex-snapshot
Statistical Confidence for Variable Selection in QSAR Models via Monte Carlo Cross-Validation
inferred← citeshttps://doi.org/10.1021/ci800209kopenalex-snapshot
Robust Cross-Validation of Linear Regression QSAR Models
inferred← citeshttps://doi.org/10.1038/s41597-021-01069-5openalex-snapshot
A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors
inferred← citeshttps://doi.org/10.1039/9781849732093-00531openalex-snapshot
Toxicokinetic Considerations in Predicting Toxicity
inferred← citeshttps://doi.org/10.1080/1062936x.2012.729224openalex-snapshot
In-silico prediction of blood–brain barrier permeability
inferred← citeshttps://doi.org/10.1080/1062936x.2014.994562openalex-snapshot
Qualitative and quantitative structure–activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals
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/bib/bbac357openalex-snapshot
A merged molecular representation deep learning method for blood–brain barrier permeability prediction
inferred← citeshttps://doi.org/10.1109/access.2020.3047852openalex-snapshot
Prediction of Blood-Brain Barrier Permeability of Compounds by Fusing Resampling Strategies and eXtreme Gradient Boosting
inferred← citeshttps://doi.org/10.1134/s199075081302008xopenalex-snapshot
Computer modeling of blood brain barrier permeability for physiologically active compounds
inferred← citeshttps://doi.org/10.1155/2015/292683openalex-snapshot
A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction
inferred← citeshttps://doi.org/10.1208/s12248-021-00675-wopenalex-snapshot
In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition
inferred← citeshttps://doi.org/10.1371/journal.pone.0023852openalex-snapshot
Identification of Novel Functional Inhibitors of Acid Sphingomyelinase
inferred← citeshttps://doi.org/10.1517/17460441.2011.627324openalex-snapshot
The discovery of new anesthetics by targeting GABAAreceptors
inferred← citeshttps://doi.org/10.1590/s1984-82502010000400016openalex-snapshot
A 2D-QSPR approach to predict blood-brain barrier penetration of drugs acting on the central nervous system
inferred← citeshttps://doi.org/10.18097/pbmc20146002161openalex-snapshot
Computer modeling of blood brain barrier permeability of physiologically active compounds
inferred← citeshttps://doi.org/10.2147/dddt.s218708openalex-snapshot
In-vitro blood-brain barrier models for drug screening and permeation studies: an overview
inferred← citeshttps://doi.org/10.2174/0115680266331755241008061915openalex-snapshot
ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel
inferred← citeshttps://doi.org/10.2174/156802610790232224openalex-snapshot
Two-Dimensional (2D) In Silico Models for Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME/T) in Drug Discovery
inferred← citeshttps://doi.org/10.2174/1573409916666200131114018openalex-snapshot
Computer Assisted Models for Blood Brain Barrier Permeation of 1, 5-Benzodiazepines
inferred← citeshttps://doi.org/10.2174/1875036201307010049openalex-snapshot
Artificial Neural Network in Drug Delivery and Pharmaceutical Research
inferred← citeshttps://doi.org/10.22159/ijap.2026v18i1.55744openalex-snapshot
THE AI REVOLUTION IN PHARMACEUTICALS: INNOVATIONS, CHALLENGES, AND FUTURE PROSPECTS – AN OVERVIEW
inferred← citeshttps://doi.org/10.2788/94537openalex-snapshot
Review of QSAR Models and Software Tools for predicting Biokinetic Properties
inferred← citeshttps://doi.org/10.3390/ijms21072534openalex-snapshot
In Silico Studies on Triterpenoid Saponins Permeation through the Blood–Brain Barrier Combined with Postmortem Research on the Brain Tissues of Mice Affected by Astragaloside IV Administration
inferred← citeshttps://doi.org/10.3390/ijms222010995openalex-snapshot
Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage
inferred← citeshttps://doi.org/10.3390/ijms241311152openalex-snapshot
Molecular Design of Magnetic Resonance Imaging Agents Binding to Amyloid Deposits
inferred← citeshttps://doi.org/10.3390/jfb17080377openalex-snapshot
Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review
inferred← citeshttps://doi.org/10.3390/molecules25030487openalex-snapshot
Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases
inferred← citeshttps://doi.org/10.3390/molecules28031342openalex-snapshot
Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework
inferred← citeshttps://doi.org/10.3390/ph17050620openalex-snapshot
The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods
inferred← citeshttps://doi.org/10.59350/rkkty-a3w29openalex-snapshot
CDK Literature #3
inferred← citeshttps://doi.org/10.59350/zac0g-6g014openalex-snapshot
CDK Literature #3
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.