Data fusion approaches in ligand-based virtual screening: Recent developments overview

Virtual screening has been widely used in drug discovery, and it has become one of the most wealthy and active topic areas in Chemoinformatics. Virtual screening (VS) plays a major role in drug discovery process, for the process of drug discovery is costly, Virtual screening has been used to reduce this cost, recently, there are many different virtual screening methods that have been suggested and applied on chemical databases. This paper aims to discuss theoretically the VS approaches, and searching methods, and demonstrates the recent approaches of VS. It’s meanly focus and discuss the issue of using data fusion and how it increases the screening performance level, and demonstrate the different types of fusions that are applied in VS, discussing and exploring the enhancements and effectiveness that happen with applying the different types of applied fusion techniques, and discuss future trends of virtual screening.

[1]  John M. Barnard,et al.  Substructure searching methods: Old and new , 1993, J. Chem. Inf. Comput. Sci..

[2]  P Willett,et al.  Similarity-based approaches to virtual screening. , 2003, Biochemical Society transactions.

[3]  Peter Willett,et al.  Enhancing the Effectiveness of Ligand‐Based Virtual Screening Using Data Fusion , 2006 .

[4]  Malgorzata N. Drwal,et al.  Combination of ligand- and structure-based methods in virtual screening. , 2013, Drug discovery today. Technologies.

[5]  Naomie Salim,et al.  Condorcet and borda count fusion method for ligand-based virtual screening , 2014, Journal of Cheminformatics.

[6]  R. Glen,et al.  Molecular similarity: a key technique in molecular informatics. , 2004, Organic & biomolecular chemistry.

[7]  Joachim M. Buhmann,et al.  Fusion of Similarity Data in Clustering , 2005, NIPS.

[8]  Naomie Salim,et al.  Combination of Fingerprint-Based Similarity Coefficients Using Data Fusion , 2003, J. Chem. Inf. Comput. Sci..

[9]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[10]  Peter Willett,et al.  Analysis of Data Fusion Methods in Virtual Screening: Similarity and Group Fusion , 2006, J. Chem. Inf. Model..

[11]  Peter Willett,et al.  Combination Rules for Group Fusion in Similarity‐Based Virtual Screening , 2010, Molecular informatics.

[12]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[13]  Stefan Kramer,et al.  Improving structural similarity based virtual screening using background knowledge , 2013, Journal of Cheminformatics.

[14]  P. Willett,et al.  Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information. , 2005, Journal of medicinal chemistry.

[15]  P Willett,et al.  Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2D fragment bit-strings. , 2002, Combinatorial chemistry & high throughput screening.

[16]  Paolo Massimo Buscema,et al.  Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets , 2012, J. Chem. Inf. Model..

[17]  Peter Willett,et al.  Combination of Similarity Rankings Using Data Fusion , 2013, J. Chem. Inf. Model..

[18]  Robert P. Sheridan,et al.  Comparison of Topological, Shape, and Docking Methods in Virtual Screening , 2007, J. Chem. Inf. Model..

[19]  Peter Willett,et al.  Similarity Searching in Databases of Chemical Structures , 2007 .

[20]  Andreas Bender,et al.  Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance , 2004, J. Chem. Inf. Model..

[21]  Jenny Chen,et al.  A Machine Learning Approach to Weighting Schemes in the Data Fusion of Similarity Coefficients , 2009, J. Chem. Inf. Model..

[22]  D Horvath,et al.  A virtual screening approach applied to the search for trypanothione reductase inhibitors. , 1997, Journal of medicinal chemistry.

[23]  P. Willett,et al.  Combination of molecular similarity measures using data fusion , 2000 .

[24]  John M. Barnard,et al.  Chemical Similarity Searching , 1998, J. Chem. Inf. Comput. Sci..

[25]  Peter Willett,et al.  Enhancing the Effectiveness of Virtual Screening by Fusing Nearest Neighbor Lists: A Comparison of Similarity Coefficients , 2004, J. Chem. Inf. Model..

[26]  Horacio Emilio Pérez Sánchez,et al.  Improvement of Virtual Screening Predictions using Computational Intelligence Methods , 2013 .

[27]  E. A. Fox,et al.  Combining the Evidence of Multiple Query Representations for Information Retrieval , 1995, Inf. Process. Manag..

[28]  Peter Willett,et al.  Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data , 1994, J. Chem. Inf. Comput. Sci..

[29]  Peter Willett,et al.  Similarity-based virtual screening using 2D fingerprints. , 2006, Drug discovery today.