Ligand-based virtual screening with co-regularised support vector regression

Katrin Ullrich, Michael Kamp, Thomas Gartner, Martin Vogt, Stefan Wrobel

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

3 Citations (Scopus)

Abstract

We consider the problem of ligand affinity prediction as a regression task, typically with few labelled examples, many unlabelled instances, and multiple views on the data. In chemoinformatics, the prediction of binding affinities for protein ligands is an important but also challenging task. As protein-ligand bonds trigger biochemical reactions, their characterisation is a crucial step in the process of drug discovery and design. However, the practical determination of ligand affinities is very expensive, whereas unlabelled compounds are available in abundance. Additionally, many different vectorial representations for compounds (molecular fingerprints) exist that cover different sets of features. To this task we propose to apply a co-regularisation approach, which extracts information from unlabelled examples by ensuring that individual models trained on different fingerprints make similar predictions. We extend support vector regression similarly to the existing co-regularised least squares regression (CoRLSR) and obtain a co-regularised support vector regression (CoSVR). We empirically evaluate the performance of CoSVR on various protein-ligand datasets. We show that CoSVR outperforms CoRLSR as well as existing state-of-The-Art approaches that do not take unlabelled molecules into account. Additionally, we provide a theoretical bound on the Rademacher complexity for CoSVR.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages261-268
Number of pages8
ISBN (Electronic)9781509054725, 9781509059102
ISBN (Print)9781509059119
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Conference on Data Mining Workshops 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016
Conference number: 16th
https://icdm2016.eurecat.cat/

Conference

ConferenceIEEE International Conference on Data Mining Workshops 2016
Abbreviated titleICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1615/12/16
Internet address

Keywords

  • Co-regularisation
  • Kernel methods
  • Ligand affinity prediction
  • Multi-view
  • Support vector regression

Cite this

Ullrich, K., Kamp, M., Gartner, T., Vogt, M., & Wrobel, S. (2016). Ligand-based virtual screening with co-regularised support vector regression. In C. Domeniconi, & F. Gullo (Eds.), Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 (pp. 261-268). [7836675] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDMW.2016.0044