Using motion planning to rank ligand binding affinity

Hsin Yi Yeh, Shawna Thomas, Nancy M. Amato

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

Abstract

In the drug discovery process, pharmaceutical companies have to screen many drug (or ligand) candidates to find the most promising ones for trial. This process is very costly and attention as turned to computational approaches to predict binding affinity to the desired target protein. In this work, we develop a computational tool for ranking ligand binding affinity that uniformly samples ligand conformations over the target protein's surface and analyzes the resulting set to compute an affinity ranking. Experiments on one tar-get protein shows that our method is able to correctly rank different ligands for the target protein as determined by ex-perimental data. Our method is a promising technique and potential cost-saving tool for pharmaceutical companies to narrow the search for good drug candidate. Copyright is held by the author/owner(s).

Original languageEnglish (US)
Title of host publicationBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages529-530
Number of pages2
ISBN (Electronic)9781450338530
DOIs
StatePublished - Sep 9 2015
Externally publishedYes
Event6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States
Duration: Sep 9 2015Sep 12 2015

Publication series

NameBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015
CountryUnited States
CityAtlanta
Period9/9/159/12/15

Keywords

  • Ligand binding affinity
  • Motion planning

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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