On the public citation graph
Comparison of Confirmed Inactive and Randomly Selected Compounds as Negative Training Examples in Support Vector Machine-Based Virtual Screening
0 signed · 59 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known work and none has been retracted — and get a dated CiteStamped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/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/s10822-006-9096-5openalex-snapshot
Evaluation of machine-learning methods for ligand-based virtual screening
inferredcites →https://doi.org/10.1016/j.jmgm.2007.12.002openalex-snapshot
A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor
inferredcites →https://doi.org/10.1016/j.neunet.2005.07.009openalex-snapshot
Graph kernels for chemical informatics
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/ci025620topenalex-snapshot
Active Learning with Support Vector Machines in the Drug Discovery Process
inferredcites →https://doi.org/10.1021/ci034207yopenalex-snapshot
Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier
inferredcites →https://doi.org/10.1021/ci049641uopenalex-snapshot
Virtual Screening of Molecular Databases Using a Support Vector Machine
inferredcites →https://doi.org/10.1021/ci050519kopenalex-snapshot
Assessing Different Classification Methods for Virtual Screening
inferredcites →https://doi.org/10.1021/ci060003gopenalex-snapshot
Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases
inferredcites →https://doi.org/10.1021/ci3001277openalex-snapshot
ZINC: A Free Tool to Discover Chemistry for Biology
inferredcites →https://doi.org/10.1021/ci7003253openalex-snapshot
Naïve Bayes Classification Using 2D Pharmacophore Feature Triplet Vectors
inferredcites →https://doi.org/10.1021/ci900419kopenalex-snapshot
Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation
inferredcites →https://doi.org/10.1021/ci970431+openalex-snapshot
Identification of Biological Activity Profiles Using Substructural Analysis and Genetic Algorithms
inferredcites →https://doi.org/10.1021/jm0608356openalex-snapshot
Benchmarking Sets for Molecular Docking
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1093/nar/gkl999openalex-snapshot
BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
inferredcites →https://doi.org/10.1145/130385.130401openalex-snapshot
A training algorithm for optimal margin classifiers
inferredcites →https://doi.org/10.1186/1758-2946-5-17openalex-snapshot
The influence of the inactives subset generation on the performance of machine learning methods
inferredcites →https://doi.org/10.5555/299094openalex-snapshot
Advances in kernel methods: support vector learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781119282594.ch5openalex-snapshot
Pharmacophore Models for Toxicology Prediction
inferred← citeshttps://doi.org/10.1002/minf.201700103openalex-snapshot
Identification of Bioactive Scaffolds Based on QSAR Models
inferred← citeshttps://doi.org/10.1002/minf.201700150openalex-snapshot
Multiple Machine Learning Based‐Chemoinformatics Models for Identification of Histone Acetyl Transferase Inhibitors
inferred← citeshttps://doi.org/10.1007/978-981-13-6920-9_11openalex-snapshot
Screening of Natural Antidiabetic Agents
inferred← citeshttps://doi.org/10.1007/s00011-017-1096-5openalex-snapshot
Nature is the best source of anti-inflammatory drugs: indexing natural products for their anti-inflammatory bioactivity
inferred← citeshttps://doi.org/10.1007/s11192-018-2865-9openalex-snapshot
The impact of imbalanced training data on machine learning for author name disambiguation
inferred← citeshttps://doi.org/10.1016/j.drudis.2016.10.009openalex-snapshot
Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)
inferred← citeshttps://doi.org/10.1016/j.jmgm.2016.10.021openalex-snapshot
In silico target prediction for elucidating the mode of action of herbicides including prospective validation
inferred← citeshttps://doi.org/10.1016/j.xcrp.2022.101113openalex-snapshot
Minimal data requirements for accurate compound activity prediction using machine learning methods of different complexity
inferred← citeshttps://doi.org/10.1016/j.ymben.2017.09.016openalex-snapshot
Predicting novel substrates for enzymes with minimal experimental effort with active learning
inferred← citeshttps://doi.org/10.1016/j.ymeth.2014.10.013openalex-snapshot
Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00469openalex-snapshot
Comparing Fingerprints for Ligand-Based Virtual Screening: A Fast and Scalable Approach for Unbiased Evaluation
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00476openalex-snapshot
Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein–Ligand Predictions
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00565openalex-snapshot
Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00407openalex-snapshot
When Trees Guide Molecules: Multiobjective Search in de Novo Drug Design
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00088openalex-snapshot
Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds
inferred← citeshttps://doi.org/10.1021/ci400480sopenalex-snapshot
Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation
inferred← citeshttps://doi.org/10.1021/ci500361uopenalex-snapshot
Ligand-Based Target Prediction with Signature Fingerprints
inferred← citeshttps://doi.org/10.1038/s41598-021-86616-1openalex-snapshot
Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein–protein interaction-oriented library and machine learning
inferred← citeshttps://doi.org/10.1039/9781788010139-00281openalex-snapshot
Application of Molecular Modelling to Speed-up the Lead Discovery Process
inferred← citeshttps://doi.org/10.1039/c7mb00118eopenalex-snapshot
Identification of human flap endonuclease 1 (FEN1) inhibitors using a machine learning based consensus virtual screening
inferred← citeshttps://doi.org/10.1080/07391102.2019.1654925openalex-snapshot
Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation
inferred← citeshttps://doi.org/10.1080/07391102.2022.2117738openalex-snapshot
Machine learning-based predictive modeling, virtual screening and biological evaluation studies for identification of potential inhibitors of MMP-13
inferred← citeshttps://doi.org/10.1080/17460441.2019.1549033openalex-snapshot
Advances with support vector machines for novel drug discovery
inferred← citeshttps://doi.org/10.1186/1758-2946-5-42openalex-snapshot
Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets
inferred← citeshttps://doi.org/10.1186/1758-2946-6-32openalex-snapshot
The influence of negative training set size on machine learning-based virtual screening
inferred← citeshttps://doi.org/10.1186/s13321-025-00999-1openalex-snapshot
InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem
inferred← citeshttps://doi.org/10.1186/s13321-025-01107-zopenalex-snapshot
Efficient decoy selection to improve virtual screening using machine learning models
inferred← citeshttps://doi.org/10.1371/journal.pone.0109340openalex-snapshot
Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH4R Antagonism
inferred← citeshttps://doi.org/10.1371/journal.pone.0175410openalex-snapshot
The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening
inferred← citeshttps://doi.org/10.1371/journal.pone.0187925openalex-snapshot
Nature is the best source of anticancer drugs: Indexing natural products for their anticancer bioactivity
inferred← citeshttps://doi.org/10.3389/fchem.2018.00133openalex-snapshot
How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors
inferred← citeshttps://doi.org/10.3390/molecules201219880openalex-snapshot
Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases
inferred← citeshttps://doi.org/10.3390/molecules21111554openalex-snapshot
Three-Dimensional Biologically Relevant Spectrum (BRS-3D): Shape Similarity Profile Based on PDB Ligands as Molecular Descriptors
inferred← citeshttps://doi.org/10.3390/molecules22091563openalex-snapshot
Indexing Natural Products for Their Potential Anti-Diabetic Activity: Filtering and Mapping Discriminative Physicochemical Properties
inferred← citeshttps://doi.org/10.64898/2026.07.04.736485openalex-snapshot
BoltzMol-1: Towards Reliable Virtual Screening for Fast and Cost-Effective Hit Discovery
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.