On computational complexity of graph inference from counting

  • Authors:

  • Szilárd Zsolt Fazekas;Hiro Ito;Yasushi Okuno;Shinnosuke Seki;Kei Taneishi

  • Affiliations:

  • Nyíregyháza, Mathematics and Informatics Institute, Nyíregyháza, Hungary 4400;School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan 182-8585;Department of Systems Bioscience for Drug Discovery, Kyoto University, Kyoto, Japan 606-8501;Department of Information and Computer Science, Aalto University, Aalto, Finland 00076;Department of Systems Bioscience for Drug Discovery, Kyoto University, Kyoto, Japan 606-8501

  • Venue:
  • Natural Computing: an international journal
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

In de novo drug design, chemical compounds are quantitized as real-valued vectors called chemical descriptors, and an optimization algorithm runs on known drug-like chemical compounds in a database and outputs an optimal chemical descriptor. Since structural information is needed for chemical synthesis, we must infer chemical graphs from the obtained descriptor. This is formalized as a graph inference problem from a real-value vector. By generalizing subword history, which was originally introduced in formal language theory to extract numerical information of words and languages based on counting, we propose a comprehensive framework to investigate the computational complexity of chemical graph inference. We also propose a (pseudo-)polynomial-time algorithm for inferring graphs in a class of practical importance from spectrums.