On the public citation graph
Machine Learning Models for Lipophilicity and Their Domain of Applicability
0 signed · 58 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known work and none has been retracted — and get a dated CiteStamped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/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1002/cmdc.200500047openalex-snapshot
An Accurate and Interpretable Bayesian Classification Model for Prediction of hERG Liability
inferredcites →https://doi.org/10.1002/cmdc.200700041openalex-snapshot
Predicting Lipophilicity of Drug‐Discovery Molecules using Gaussian Process Models
inferredcites →https://doi.org/10.1007/978-1-4899-3324-9openalex-snapshot
Density Estimation for Statistics and Data Analysis
inferredcites →https://doi.org/10.1007/s00894-002-0101-1openalex-snapshot
ADME evaluation in drug discovery
inferredcites →https://doi.org/10.1007/s10822-007-9125-zopenalex-snapshot
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
inferredcites →https://doi.org/10.1016/s1574-1400(09)00513-1openalex-snapshot
Annual Reports in Computational Chemistry
inferredcites →https://doi.org/10.1021/ci000459copenalex-snapshot
Quantitative Structure−Activity Relationship Studies Using Gaussian Processes
inferredcites →https://doi.org/10.1021/ci0202741openalex-snapshot
A Consensus Neural Network-Based Technique for Discriminating Soluble and Poorly Soluble Compounds
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/ci034255iopenalex-snapshot
Nonlinear Prediction of Quantitative Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/ci049737oopenalex-snapshot
Classifying ‘Drug-likeness' with Kernel-Based Learning Methods
inferredcites →https://doi.org/10.1021/ci049744copenalex-snapshot
Generalized Fragment-Substructure Based Property Prediction Method
inferredcites →https://doi.org/10.1021/ci0503210openalex-snapshot
In Silico Prediction of Buffer Solubility Based on Quantum-Mechanical and HQSAR- and Topology-Based Descriptors
inferredcites →https://doi.org/10.1021/ci0503762openalex-snapshot
Model Selection Based on Structural Similarity−Method Description and Application to Water Solubility Prediction
inferredcites →https://doi.org/10.1021/ci0504014openalex-snapshot
logD7.4 Modeling Using Bayesian Regularized Neural Networks. Assessment and Correction of the Errors of Prediction
inferredcites →https://doi.org/10.1021/ci600205gopenalex-snapshot
Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1109/72.914517openalex-snapshot
An introduction to kernel-based learning algorithms
inferredcites →https://doi.org/10.1111/j.2517-6161.1978.tb01643.xopenalex-snapshot
Curve Fitting and Optimal Design for Prediction
inferredcites →https://doi.org/10.1145/1143844.1143969openalex-snapshot
Two-dimensional solution path for support vector regression
inferredcites →https://doi.org/10.1177/026119290503300209openalex-snapshot
Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships
inferredcites →https://doi.org/10.1177/1087057105281048openalex-snapshot
Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Chemistry in Training and Test Sets
inferredcites →https://doi.org/10.1201/9781315140919openalex-snapshot
Density Estimation for Statistics and Data Analysis
inferredcites →https://doi.org/10.1289/txg.7125openalex-snapshot
Assessment of Prediction Confidence and Domain Extrapolation of Two Structure–Activity Relationship Models for Predicting Estrogen Receptor Binding Activity
inferredcites →https://doi.org/10.7551/mitpress/3206.001.0001openalex-snapshot
Gaussian Processes for Machine Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781394280735.ch25openalex-snapshot
Transform Drug Discovery and Development With Generative Artificial Intelligence
inferred← citeshttps://doi.org/10.1002/9783527627448.ch5openalex-snapshot
In Silico Tools and In Vitro HTS Approaches to Determine Lipophilicity During the Drug Discovery Process
inferred← citeshttps://doi.org/10.1002/9783527840748.ch13openalex-snapshot
Reliability and Applicability Assessment for Machine Learning Models
inferred← citeshttps://doi.org/10.1002/minf.201100070openalex-snapshot
From Hits to Leads: Challenges for the Next Phase of Machine Learning in Medicinal Chemistry
inferred← citeshttps://doi.org/10.1007/978-3-031-81728-1_11openalex-snapshot
Statistical Methods in QSAR
inferred← citeshttps://doi.org/10.1007/s10822-007-9125-zopenalex-snapshot
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
inferred← citeshttps://doi.org/10.1007/s10822-007-9160-9openalex-snapshot
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
inferred← citeshttps://doi.org/10.1016/b978-0-323-99134-6.00007-4openalex-snapshot
Convolutional neural networks: Basic concepts and applications in manufacturing
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2008.04.074openalex-snapshot
LogD: Lipophilicity for ionisable compounds
inferred← citeshttps://doi.org/10.1016/s0079-6468(10)49004-9openalex-snapshot
Computational Analysis of Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00305openalex-snapshot
Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00243openalex-snapshot
Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00295openalex-snapshot
Ligand-Based Virtual Screening in a Search for Novel Anti-HIV-1 Chemotypes
inferred← citeshttps://doi.org/10.1021/ci100308fopenalex-snapshot
StructRank: A New Approach for Ligand-Based Virtual Screening
inferred← citeshttps://doi.org/10.1021/ci700142copenalex-snapshot
A Probabilistic Approach to Classifying Metabolic Stability
inferred← citeshttps://doi.org/10.1021/ci800329ropenalex-snapshot
Atomic Local Neighborhood Flexibility Incorporation into a Structured Similarity Measure for QSAR
inferred← citeshttps://doi.org/10.1021/ci9000794openalex-snapshot
Bias-Correction of Regression Models: A Case Study on hERG Inhibition
inferred← citeshttps://doi.org/10.1039/d2dd00045hopenalex-snapshot
Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium
inferred← citeshttps://doi.org/10.1093/bib/bbab430openalex-snapshot
Artificial intelligence in drug discovery: applications and techniques
inferred← citeshttps://doi.org/10.1111/cbdd.14062openalex-snapshot
Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives
inferred← citeshttps://doi.org/10.1111/jphp.12863openalex-snapshot
Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression
inferred← citeshttps://doi.org/10.1134/s1070363210120248openalex-snapshot
Computational methods and software in computer-aided combinatorial library design
inferred← citeshttps://doi.org/10.1186/1758-2946-2-2openalex-snapshot
Estimation of the applicability domain of kernel-based machine learning models for virtual screening
inferred← citeshttps://doi.org/10.1208/s12248-012-9358-1openalex-snapshot
Paradigm Shift in Toxicity Testing and Modeling
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1003400openalex-snapshot
Machine Learning Estimates of Natural Product Conformational Energies
inferred← citeshttps://doi.org/10.14279/depositonce-3309openalex-snapshot
Novel Machine Learning Methods for Computational Chemistry
inferred← citeshttps://doi.org/10.1517/17425250902753261openalex-snapshot
The use of machine learning and nonlinear statistical tools for ADME prediction
inferred← citeshttps://doi.org/10.2174/156802611796391230openalex-snapshot
Chemogenomics Approaches for Receptor Deorphanization and Extensions of the Chemogenomics Concept to Phenotypic Space
inferred← citeshttps://doi.org/10.31487/j.cor.2021.01.02openalex-snapshot
AI – New Avenue for Drug Discovery and Optimization
inferred← citeshttps://doi.org/10.48550/arxiv.0912.1128openalex-snapshot
How to Explain Individual Classification Decisions
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.