A neuro-fuzzy approach to virtual screening in molecular bioinformatics

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.

[1]  Peter Willett,et al.  Promoting Access to White Rose Research Papers Effectiveness of Graph-based and Fingerprint-based Similarity Measures for Virtual Screening of 2d Chemical Structure Databases , 2022 .

[2]  Schmid,et al.  "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. , 1999, Angewandte Chemie.

[3]  Edward N. Trifonov,et al.  Earliest pages of bioinformatics , 2000, Bioinform..

[4]  Jürgen Paetz.  Metric rule generation with septic shock patient data , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[5]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[6]  Konstantin V. Balakin,et al.  Structure-Based versus Property-Based Approaches in the Design of G-Protein-Coupled Receptor-Targeted Libraries , 2003, J. Chem. Inf. Comput. Sci..

[7]  Petra Schneider,et al.  Comparison of correlation vector methods for ligand-based similarity searching , 2003, J. Comput. Aided Mol. Des..

[8]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[9]  Kaixian Chen,et al.  Virtual screening on natural products for discovering active compounds and target information. , 2003, Current medicinal chemistry.

[10]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[11]  Jens Sadowski,et al.  Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..

[12]  Huafeng Xu,et al.  Retrospect and prospect of virtual screening in drug discovery. , 2002, Current topics in medicinal chemistry.

[13]  J. Gasteiger,et al.  Chemoinformatics: A Textbook , 2003 .

[14]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[15]  Gisbert Schneider,et al.  Virtual screening and fast automated docking methods. , 2002, Drug discovery today.

[16]  Tim D. J. Perkins,et al.  Large-scale virtual screening for discovering leads in the postgenomic era , 2001, IBM Syst. J..

[17]  Michael R. Berthold,et al.  Building precise classifiers with automatic rule extraction , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  G. Schneider,et al.  Virtual Screening for Bioactive Molecules , 2000 .

[19]  P. Brown.  Matrix metalloproteinase inhibitors in the treatment of cancer , 1997, Medical oncology.

[20]  A. Jeng,et al.  Chapter 15. Matrix metalloproteinase inhibitors for treatment of cancer , 2000 .

[21]  Jürgen Paetz.  Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions , 2003, Artif. Intell. Medicine.

[22]  Jürgen Bajorath,et al.  Recursive Median Partitioning for Virtual Screening of Large Databases , 2003, J. Chem. Inf. Comput. Sci..

[23]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[24]  Gisbert Schneider,et al.  Collection of bioactive reference compounds for focused library design , 2003 .

[25]  J. Gálvez,et al.  Topological virtual screening: a way to find new anticonvulsant drugs from chemical diversity. , 2003, Bioorganic & medicinal chemistry letters.

[26]  Ajay.  Predicting drug-likeness: why and how? , 2002, Current topics in medicinal chemistry.

[27]  Nathaniel A. Woody.  Chemoinformatics: a textbook, Johann Gasteiger and Thomas Engel (eds), Wiley-VCH, Weinheim, 2003, ISBN 3-527-30681-1 , 2004 .

[28]  Paul D Lyne,et al.  Structure-based virtual screening: an overview. , 2002, Drug discovery today.

[29]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .