On the public citation graph

Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines

0 signed · 72 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/etc.5620160514openalex-snapshot

Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow ( Pimephales promelas )

inferredcites →https://doi.org/10.1002/minf.201100142openalex-snapshot

Benchmarking Variable Selection in QSAR

inferredcites →https://doi.org/10.1007/978-0-387-98141-3openalex-snapshot

ggplot2: Elegant Graphics for Data Analysis

inferredcites →https://doi.org/10.1007/978-3-642-33412-2_17openalex-snapshot

Application of Conformal Prediction in QSAR

inferredcites →https://doi.org/10.1016/j.bmcl.2012.10.102openalex-snapshot

QSAR investigation of NaV1.7 active compounds using the SVM/Signature approach and the Bioclipse Modeling platform

inferredcites →https://doi.org/10.1016/j.compchemeng.2009.10.017openalex-snapshot

Computer-aided molecular design using the Signature molecular descriptor: Application to solvent selection

inferredcites →https://doi.org/10.1016/s0031-3203(96)00142-2openalex-snapshot

The use of the area under the ROC curve in the evaluation of machine learning algorithms

inferredcites →https://doi.org/10.1016/s0097-8485(01)00094-8openalex-snapshot

Drug design by machine learning: support vector machines for pharmaceutical data analysis

inferredcites →https://doi.org/10.1021/ar50020a002openalex-snapshot

Quantitative approach to biochemical structure-activity relationships

inferredcites →https://doi.org/10.1021/ci020345wopenalex-snapshot

The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies

inferredcites →https://doi.org/10.1021/ci020346oopenalex-snapshot

The Signature Molecular Descriptor. 2. Enumerating Molecules from Their Extended Valence Sequences

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/ci0341823openalex-snapshot

The Signature Molecular Descriptor. 4. Canonizing Molecules Using Extended Valence Sequences

inferredcites →https://doi.org/10.1021/ci200242copenalex-snapshot

Integrated Decision Support for Assessing Chemical Liabilities

inferredcites →https://doi.org/10.1021/ci4001376openalex-snapshot

Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms

inferredcites →https://doi.org/10.1021/ci5001168openalex-snapshot

Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination

inferredcites →https://doi.org/10.1021/ci500361uopenalex-snapshot

Ligand-Based Target Prediction with Signature Fingerprints

inferredcites →https://doi.org/10.1021/ci600332jopenalex-snapshot

Contemporary QSAR Classifiers Compared

inferredcites →https://doi.org/10.1021/ci900161gopenalex-snapshot

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

inferredcites →https://doi.org/10.1021/ci9002206openalex-snapshot

Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data

inferredcites →https://doi.org/10.1021/jm0101343openalex-snapshot

Three-Dimensional Quantitative Structure−Activity Relationships of Cyclo-oxygenase-2 (COX-2) Inhibitors: A Comparative Molecular Field Analysis

inferredcites →https://doi.org/10.1023/b:jcam.0000047814.85293.daopenalex-snapshot

Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors

inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-snapshot

A tutorial on support vector regression

inferredcites →https://doi.org/10.1080/019262301753385979openalex-snapshot

Review Article: Compendium of Chemical Carcinogens by Target Organ: Results of Chronic Bioassays in Rats, Mice, Hamsters, Dogs, and Monkeys

inferredcites →https://doi.org/10.1093/bioinformatics/btt325openalex-snapshot

WhichCyp: prediction of cytochromes P450 inhibition

inferredcites →https://doi.org/10.1093/toxsci/kfi161openalex-snapshot

Supplement to the Carcinogenic Potency Database (CPDB): Results of Animal Bioassays Published in the General Literature through 1997 and by the National Toxicology Program in 1997–1998

inferredcites →https://doi.org/10.1186/1471-2105-12-77openalex-snapshot

pROC: an open-source package for R and S+ to analyze and compare ROC curves

inferredcites →https://doi.org/10.1186/1471-2105-13-106openalex-snapshot

Natural product-likeness score revisited: an open-source, open-data implementation

inferredcites →https://doi.org/10.1186/1745-6215-12-106openalex-snapshot

Through the looking glass: understanding non-inferiority

inferredcites →https://doi.org/10.1289/ehp.99107s4527openalex-snapshot

Supplement to the Carcinogenic Potency Database (CPDB): results of animal bioassays published in the general literature in 1993 to 1994 and by the National Toxicology Program in 1995 to 1996.

inferredcites →https://doi.org/10.1371/journal.pone.0066566openalex-snapshot

A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms

inferredcites →https://doi.org/10.2174/138161206777585274openalex-snapshot

Recent Developments of the Chemistry Development Kit (CDK) - An Open-Source Java Library for Chemo- and Bioinformatics

inferredcites →https://doi.org/10.2174/156802612804910287openalex-snapshot

Open Source Drug Discovery with Bioclipse

inferredcites →https://doi.org/10.2174/1570163043484789openalex-snapshot

Assessment of the Health Effects of Chemicals in Humans: I. QSAR Estimation of the Maximum Recommended Therapeutic Dose (MRTD) and No Effect Level (NOEL) of Organic Chemicals Based on Clinical Trial Data1

inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot

R: A Language and Environment for Statistical Computing

What points at this work

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

inferred← citeshttps://doi.org/10.1002/btpr.2693openalex-snapshot

Identifying new clotting factor XIa inhibitors in virtual high‐throughput screens using PCA‐GA‐SVM models and signature

inferred← citeshttps://doi.org/10.1016/b978-0-12-801238-3.11532-6openalex-snapshot

In Silico ADME Modeling

inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2020.10.006openalex-snapshot

Automated classification of stems and leaves of potted plants based on point cloud data

inferred← citeshttps://doi.org/10.1016/j.ces.2016.02.037openalex-snapshot

Developing an in silico pipeline for faster drug candidate discovery: Virtual high throughput screening with the Signature molecular descriptor using support vector machine models

inferred← citeshttps://doi.org/10.1016/j.chemolab.2015.12.002openalex-snapshot

Unraveling the origin of splice switching activity of hemoglobin β-globin gene modulators via QSAR modeling

inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2017.05.007openalex-snapshot

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models

inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2018.12.006openalex-snapshot

Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation

inferred← citeshttps://doi.org/10.1016/j.ejmech.2017.08.056openalex-snapshot

Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models

inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101753openalex-snapshot

Machine learning for mapping glacier surface facies in Svalbard

inferred← citeshttps://doi.org/10.1016/j.xphs.2022.05.010openalex-snapshot

In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology

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.0c01294openalex-snapshot

GGL-Tox: Geometric Graph Learning for Toxicity Prediction

inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00293openalex-snapshot

Machine Learning Strategies When Transitioning between Biological Assays

inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01088openalex-snapshot

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches

inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00633openalex-snapshot

LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets

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/ci500580yopenalex-snapshot

Scaling Predictive Modeling in Drug Development with Cloud Computing

inferred← citeshttps://doi.org/10.1080/00498254.2021.2011471openalex-snapshot

In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models

inferred← citeshttps://doi.org/10.1080/00498254.2022.2049397openalex-snapshot

In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology

inferred← citeshttps://doi.org/10.1080/00498254.2024.2352598openalex-snapshot

Perspectives on the use of machine learning for ADME prediction at AstraZeneca

inferred← citeshttps://doi.org/10.1111/cbdd.13457openalex-snapshot

Identifying de‐NEDDylation inhibitors: Virtual high‐throughput screens targeting SENP8

inferred← citeshttps://doi.org/10.1177/02611929221148447openalex-snapshot

In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

inferred← citeshttps://doi.org/10.1186/s13321-016-0151-5openalex-snapshot

Large-scale ligand-based predictive modelling using support vector machines

inferred← citeshttps://doi.org/10.1186/s13321-016-0179-6openalex-snapshot

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles

inferred← citeshttps://doi.org/10.1186/s13321-017-0220-4openalex-snapshot

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

inferred← citeshttps://doi.org/10.1186/s13321-017-0226-yopenalex-snapshot

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

inferred← citeshttps://doi.org/10.1186/s13321-018-0265-zopenalex-snapshot

Efficient iterative virtual screening with Apache Spark and conformal prediction

inferred← citeshttps://doi.org/10.1186/s13321-018-0271-1openalex-snapshot

A confidence predictor for logD using conformal regression and a support-vector machine

inferred← citeshttps://doi.org/10.1186/s13321-018-0304-9openalex-snapshot

Evaluating parameters for ligand-based modeling with random forest on sparse data sets

inferred← citeshttps://doi.org/10.1186/s13321-020-00464-1openalex-snapshot

Predicting target profiles with confidence as a service using docking scores

inferred← citeshttps://doi.org/10.1186/s13321-021-00511-5openalex-snapshot

Assessing the calibration in toxicological in vitro models with conformal prediction

inferred← citeshttps://doi.org/10.1186/s13321-024-00870-9openalex-snapshot

CPSign: conformal prediction for cheminformatics modeling

inferred← citeshttps://doi.org/10.3390/biom8020024openalex-snapshot

Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s

inferred← citeshttps://doi.org/10.3390/molecules26092572openalex-snapshot

Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology

inferred← citeshttps://doi.org/10.6084/m9.figshare.13101273openalex-snapshot

Additional file 1 of Predicting target profiles with confidence as a service using docking scores

inferred← citeshttps://doi.org/10.6084/m9.figshare.14516380openalex-snapshot

Additional file 1 of Assessing the calibration in toxicological in vitro models with conformal prediction

inferred← citeshttps://doi.org/10.6084/m9.figshare.26740850openalex-snapshot

Additional file 1 of CPSign: conformal prediction for cheminformatics modeling

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.