Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Graph kernels for chemical informatics.

Liva Ralaivola1, Sanjay J Swamidass, Hiroto Saigo

  • 1School of Information and Computer Sciences, University of California, Irvine, CA 92697-3425, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|September 15, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Critical Period Microbiota Shape Brain Plasticity.

bioRxiv : the preprint server for biology·2026
Same author

Evaluating the Intelligence of large language models: A comparative study using verbal and visual IQ tests.

Computers in human behavior. Artificial humans·2026
Same author

The nBAF complex subunit CREST/SS18L1 regulates hippocampal memory processes via tyrosine 397 and histone acetyltransferase CBP.

Cell reports·2026
Same author

Circadian reprogramming by timed sodium intake reveals transcriptional pathways of daily salt handling in the colon.

Science advances·2026
Same author

Chronic intermittent hypoxia reshapes circadian metabolic architecture in a model of sleep apnea.

Science advances·2026
Same author

A Dataset of Plausible Proton Transfer Steps for Arrow-Pushing Mechanisms.

Scientific data·2026
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

New graph kernels improve machine learning for computational chemistry. These methods accurately predict chemical compound mutagenicity, toxicity, and anti-cancer activity, outperforming previous approaches.

Area of Science:

  • Computational chemistry
  • Chemical informatics
  • Machine learning on graphs

Background:

  • Large chemical compound repositories necessitate advanced machine learning.
  • Chemical structures are graph-based, requiring variable-size graph processing.

Purpose of the Study:

  • Introduce novel graph kernels for chemical informatics.
  • Evaluate kernel performance on mutagenicity, toxicity, and anti-cancer activity prediction.

Main Methods:

  • Developed Tanimoto, MinMax, and Hybrid graph kernels.
  • Utilized molecular fingerprints and labeled path counting via depth-first search.
  • Applied kernels to three public datasets for classification tasks.

Main Results:

Related Experiment Videos

  • Achieved high accuracy: 91.5% on Mutag, 65-67% on PTC, 72% on NCI.
  • Performance comparable or superior to existing literature methods.
  • Demonstrated effectiveness of graph kernels in chemical property prediction.

    • Conclusions:

    • The new graph kernels are effective for computational chemistry tasks.
    • These methods offer improved prediction accuracy for chemical compound properties.
    • Further exploration of 1D/3D molecular representations and kernel tradeoffs is warranted.