On the public citation graph
How Experimental Errors Influence Drug Metabolism and Pharmacokinetic QSAR/QSPR Models
0 signed · 79 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known work and none has been retracted — and get a dated CiteStamped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/10.1002/9783906390468.ch8openalex-snapshot
The Role of Plasma Protein Binding in Drug Discovery
inferredcites →https://doi.org/10.1002/jps.21470openalex-snapshot
Consideration of the Linear Concentration Increase of the Unbound Drug Fraction in Plasma
inferredcites →https://doi.org/10.1002/minf.201100107openalex-snapshot
Automated QSAR with a Hierarchy of Global and Local Models
inferredcites →https://doi.org/10.1002/rcm.4546openalex-snapshot
High‐throughput metabolic stability studies in drug discovery by orthogonal acceleration time‐of‐flight (OATOF) with analogue‐to‐digital signal capture (ADC)
inferredcites →https://doi.org/10.1016/j.chroma.2003.10.084openalex-snapshot
Application of high-performance liquid chromatography based measurements of lipophilicity to model biological distribution
inferredcites →https://doi.org/10.1016/j.chroma.2005.10.030openalex-snapshot
High-throughput screening of protein binding by equilibrium dialysis combined with liquid chromatography and mass spectrometry
inferredcites →https://doi.org/10.1016/j.ejps.2009.02.007openalex-snapshot
Application of a Dried-DMSO rapid throughput 24-h equilibrium solubility in advancing discovery candidates
inferredcites →https://doi.org/10.1016/j.jala.2010.10.002openalex-snapshot
A Highly Automated Assay for Determining the Aqueous Equilibrium Solubility of Drug Discovery Compounds
inferredcites →https://doi.org/10.1021/ci010363yopenalex-snapshot
Search for Predictive Generic Model of Aqueous Solubility Using Bayesian Neural Nets
inferredcites →https://doi.org/10.1021/ci7000633openalex-snapshot
Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties
inferredcites →https://doi.org/10.1021/ci700332kopenalex-snapshot
The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy)
inferredcites →https://doi.org/10.1021/ie980233nopenalex-snapshot
Predicting Cosolvency. 2. Correlation with Solvent Physicochemical Properties
inferredcites →https://doi.org/10.1021/jm0100990openalex-snapshot
ElogDoct: A Tool for Lipophilicity Determination in Drug Discovery. 2. Basic and Neutral Compounds
inferredcites →https://doi.org/10.1023/a:1025358319677openalex-snapshot
In silico ADME/Tox: why models fail
inferredcites →https://doi.org/10.1037/a0016973openalex-snapshot
An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests.
inferredcites →https://doi.org/10.1177/1087057110396372openalex-snapshot
A Method for Measuring the Lipophilicity of Compounds in Mixtures of 10
inferredcites →https://doi.org/10.1186/1758-2946-3-28openalex-snapshot
AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment
inferredcites →https://doi.org/10.1198/tech.2004.s248openalex-snapshot
Statistics and Chemometrics for Analytical Chemistry
inferredcites →https://doi.org/10.2174/092986709789578213openalex-snapshot
How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9783527818242.ch10openalex-snapshot
Computational Approaches for Target Inference
inferred← citeshttps://doi.org/10.1002/9783527840748.ch21openalex-snapshot
Advances in the Application of In Silico ADMET Models – An Industry Perspective
inferred← citeshttps://doi.org/10.1002/cmdc.201500424openalex-snapshot
How Consistent are Publicly Reported Cytotoxicity Data? Large‐Scale Statistical Analysis of the Concordance of Public Independent Cytotoxicity Measurements
inferred← citeshttps://doi.org/10.1002/minf.201900059openalex-snapshot
Investigation of Factors Affecting the Performance of in silico Volume Distribution QSAR Models for Human, Rat, Mouse, Dog & Monkey
inferred← citeshttps://doi.org/10.1002/minf.202100321openalex-snapshot
Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces
inferred← citeshttps://doi.org/10.1007/978-3-319-50042-3_4openalex-snapshot
In Silico ADME Techniques Used in Early-Phase Drug Discovery
inferred← citeshttps://doi.org/10.1007/978-981-95-2525-6_47openalex-snapshot
ADMET Prediction Based on Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/s10822-015-9836-5openalex-snapshot
Time dependent analysis of assay comparability: a novel approach to understand intra- and inter-site variability over time
inferred← citeshttps://doi.org/10.1007/s10822-015-9850-7openalex-snapshot
Interpreting physicochemical experimental data sets
inferred← citeshttps://doi.org/10.1007/s10822-015-9871-2openalex-snapshot
The impact of data integrity on decision making in early lead discovery
inferred← citeshttps://doi.org/10.1007/s10822-022-00458-1openalex-snapshot
Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images
inferred← citeshttps://doi.org/10.1007/s11030-021-10237-zopenalex-snapshot
Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review
inferred← citeshttps://doi.org/10.1007/s11095-025-03922-3openalex-snapshot
Fraction-based Linear Extrapolation (FLEX) Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach
inferred← citeshttps://doi.org/10.1016/b978-0-12-801238-3.11532-6openalex-snapshot
In Silico ADME Modeling
inferred← citeshttps://doi.org/10.1016/b978-0-443-44415-9.00003-xopenalex-snapshot
AI for predicting pharmacokinetics and pharmacodynamics
inferred← citeshttps://doi.org/10.1016/j.chemolab.2024.105292openalex-snapshot
In silico prediction of metabolic stability for ester-containing molecules: Machine learning and quantum mechanical methods
inferred← citeshttps://doi.org/10.1016/j.dmpk.2021.100395openalex-snapshot
Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model
inferred← citeshttps://doi.org/10.1016/j.drudis.2015.09.010openalex-snapshot
A new paradigm for navigating compound property related drug attrition
inferred← citeshttps://doi.org/10.1016/j.drudis.2016.03.015openalex-snapshot
A comprehensive company database analysis of biological assay variability
inferred← citeshttps://doi.org/10.1016/j.drudis.2020.07.005openalex-snapshot
Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling
inferred← citeshttps://doi.org/10.1016/j.molstruc.2020.128459openalex-snapshot
A machine learning approach to discriminate MR1 binders: The importance of the phenol and carbonyl fragments
inferred← citeshttps://doi.org/10.1021/acs.chemrev.8b00728openalex-snapshot
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01344openalex-snapshot
Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c01214openalex-snapshot
The Impact of Experimental and Calculated Error on the Performance of Affinity Predictions
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00639openalex-snapshot
Denoising Drug Discovery Data for Improved Absorption, Distribution, Metabolism, Excretion, and Toxicity Property Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00101openalex-snapshot
Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00294openalex-snapshot
Data Quality in the Human and Environmental Health Sciences: Using Statistical Confidence Scoring to Improve QSAR/QSPR Modeling
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00229openalex-snapshot
QSAR Model of Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain: Incorporating P-glycoprotein Efflux as a Variable
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01878openalex-snapshot
Unimodal vs Multimodal Learning: A Systematic Evaluation of Fusion Strategies and Model Design for Molecular Property Prediction and Uncertainty Quantification
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00460openalex-snapshot
Prediction of Oral Bioavailability in Rats: Transferring Insights from in Vitro Correlations to (Deep) Machine Learning Models Using in Silico Model Outputs and Chemical Structure Parameters
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.0c00163openalex-snapshot
Chemists: AI Is Here; Unite To Get the Benefits
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c01707openalex-snapshot
Human-Focused Multiparameter Optimization Scores for Rank Ordering Compounds during Early Drug Discovery: Validation of PBPK Models Based on Clinical PK Data
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.7b00487openalex-snapshot
In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.7b00935openalex-snapshot
Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA)
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.1c00718openalex-snapshot
Machine Learning Models for Human In Vivo Pharmacokinetic Parameters with In-House Validation
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.2c00027openalex-snapshot
Prediction of In Vivo Pharmacokinetic Parameters and Time–Exposure Curves in Rats Using Machine Learning from the Chemical Structure
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.6b00836openalex-snapshot
Predicting Passive Permeability of Drug-like Molecules from Chemical Structure: Where Are We?
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.6b01059openalex-snapshot
In Vitro Intrinsic Permeability: A Transporter-Independent Measure of Caco-2 Cell Permeability in Drug Design and Development
inferred← citeshttps://doi.org/10.1021/acs.oprd.6b00015openalex-snapshot
Toward a More Holistic Framework for Solvent Selection
inferred← citeshttps://doi.org/10.1021/acsenvironau.5c00224openalex-snapshot
Methodological Challenges in the Application of QSAR Models for Chemical Prioritization and Toxicity Assessment: A Case Study on Aryl Hydrocarbon Receptor Activity in Environmental Pollutant Mixtures
inferred← citeshttps://doi.org/10.1021/acsestwater.5c00850openalex-snapshot
PHYSPROPNET: A Benchmarking Study of Machine Learning Models for Physicochemical Property Prediction in Data-Limited Environmental Research
inferred← citeshttps://doi.org/10.1021/acsomega.4c02003openalex-snapshot
An Automated Robotic Interface for Assays: Facilitating Machine Learning in Drug Discovery by the Automation of Physicochemical Property Assays
inferred← citeshttps://doi.org/10.1021/acsomega.5c04861openalex-snapshot
Prediction of Permeability and Efflux Using Multitask Learning
inferred← citeshttps://doi.org/10.1021/acsomega.7b00274openalex-snapshot
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do
inferred← citeshttps://doi.org/10.1039/c5md00583copenalex-snapshot
Profiling the estimated plasma concentrations of 215 marketed oral drugs
inferred← citeshttps://doi.org/10.1039/c6md00362aopenalex-snapshot
Oral drug-likeness criteria in preclinical species
inferred← citeshttps://doi.org/10.1089/aivt.2018.0018openalex-snapshot
Building In Silico Models to Trigger Retesting: A Strategy on How to Use Predictive Models to Identify Potentially Incorrect In Vitro Intrinsic Clearance Results
inferred← citeshttps://doi.org/10.1111/cbdd.13819openalex-snapshot
Evaluation of the performance of various machine learning methods on the discrimination of the active compounds
inferred← citeshttps://doi.org/10.1186/s13321-021-00571-7openalex-snapshot
The effect of noise on the predictive limit of QSAR models
inferred← citeshttps://doi.org/10.1517/17460441.2015.1043885openalex-snapshot
In silicotools used for compound selection during target-based drug discovery and development
inferred← citeshttps://doi.org/10.2174/1874104501711010212openalex-snapshot
Ezqsar: An R Package for Developing QSAR Models Directly From Structures
inferred← citeshttps://doi.org/10.33483/jfpau.1327078openalex-snapshot
İLAÇ TASARIMINDA YAPAY ZEKÂ UYGULAMALARI
inferred← citeshttps://doi.org/10.3390/molecules20058270openalex-snapshot
Prediction of Placental Barrier Permeability: A Model Based on Partial Least Squares Variable Selection Procedure
inferred← citeshttps://doi.org/10.3390/molecules28020500openalex-snapshot
Contribution of Reliable Chromatographic Data in QSAR for Modelling Bisphenol Transport across the Human Placenta Barrier
inferred← citeshttps://doi.org/10.4155/fmc-2016-0161openalex-snapshot
The Integration of Computational Chemistry During Drug Discovery to Drive Decisions: are We There Yet?
inferred← citeshttps://doi.org/10.5599/admet.6.1.470openalex-snapshot
In silico ADME in drug design – enhancing the impact
inferred← citeshttps://doi.org/10.5772/intechopen.69642openalex-snapshot
Introductory Chapter: Some Quantitative Structure Activity Relationship Descriptor
inferred← citeshttps://doi.org/10.70675/b9dba6ccz7705z4da9zbc0czfb5f99be8d9bopenalex-snapshot
Evaluation des paramètres toxicocinétiques des analogues de bisphénols : développement d'approches de type Relation Quantitative Structure-Activité (QSAR)
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.