A neuro-fuzzy approach to virtual screening in molecular bioinformatics

https://doi.org/10.1016/j.fss.2004.10.015Get rights and content

Abstract

Molecular bioinformatics is a transdisciplinary working area. One hot topic is the design of drugs using computers and intelligent algorithms. This is known as in silico approach. We use a new in silico approach for separating active ligand molecules from inactive ones for different drug targets. This kind of retrospective virtual screening is performed by using encoded molecule data and a neuro-fuzzy methodology for classification, feature selection, and rule generation. We generate rules in a retrospective screening process that identify regions, where clearly more active compounds can be found compared to their a priori probability. We show that our approach is superior to a common descriptor-based standard technique.

References (27)

  • H.-J. Böhm et al.

    Virtual Screening for Bioactive Molecules

    (2000)
  • L. Bruno-Blanch, J. Galvez, R. Garcia-Domenech, Topological virtual screening: a way to find new anticonvulsant drugs...
  • E. Byvatov et al.

    Comparison of support vector machine and artificial neural network systems for drug/nondrug classification

    J. Chem. Inform. Comput. Sci.

    (2003)
  • Cited by (10)

    • Functional characterization of unknown protein sequences using Neuro-Fuzzy based machine learning approach and sequence augmented feature

      2022, Expert Systems with Applications
      Citation Excerpt :

      Protein secondary structure classification was performed by (Hering et al., 2003) using an artificial neural network with fuzzy approach. NFA had been applied for the virtual screening of the molecular (Paetz & Schneider, 2005). Neuro-Fuzzy and random forest classifiers were applied by (Barenboim, Masso, Vaisman, & Jamison, 2008) for the statistical geometry-based prediction of nonsynonymous SNP functional effects. (

    • Computational intelligence methods for docking scores

      2009, Current Computer-Aided Drug Design
    • The particle swarm interval rule optimizer with an application to drug design data

      2006, 2006 IEEE Congress on Evolutionary Computation, CEC 2006
    • Optimization study with ligand-design interval rules

      2006, Journal of Intelligent and Fuzzy Systems
    View all citing articles on Scopus
    View full text