A neuro-fuzzy approach to virtual screening in molecular bioinformatics

Authors:
Jürgen Paetz;Gisbert Schneider
Affiliations:
Molecular Design Laboratory, Fachbereich Chemische und Pharmazeutische Wissenschaften, Institut für Organische Chemie und Chemische Biologie, J.W. Goethe-Universität Frankfurt am Main, M ...;Molecular Design Laboratory, Fachbereich Chemische und Pharmazeutische Wissenschaften, Institut für Organische Chemie und Chemische Biologie, J.W. Goethe-Universität Frankfurt am Main, M ...
Venue:
Fuzzy Sets and Systems
Year:
2005

Citing 5
Cited 1

Virtual Screening for Bioactive Molecules

Virtual Screening for Bioactive Molecules
Metric Rule Generation with Septic Shock Patient Data

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Large-scale virtual screening for discovering leads in the postgenomic era

IBM Systems Journal - Deep computing for the life sciences
Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions

Artificial Intelligence in Medicine

Optimization study with ligand-design interval rules

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.20

Visualization

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.