Molecular Similarity Searching with Different Similarity Coefficients and Different Molecular Descriptors

In a chemical database searching, two principal components of a structural similarity measurement are the molecular representations to describe the molecules, and the similarity coefficients used to calculate the score of similarity between two molecules representations. The representation is usually a set of binary elements describing the presence or the absence of attributes of molecules. In literature, many approaches and methods have been developed to improve similarity searching for chemical database information.

[1]  Mohammed Mumtaz Al-Dabbagh,et al.  Adapting Document Similarity Measures for Ligand-Based Virtual Screening , 2016, Molecules.

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

[3]  Naomie Salim,et al.  LINGO-DOSM: LINGO for Descriptors of Outline Shape of Molecules , 2013, ACIIDS.

[4]  Mohammed Mumtaz Al-Dabbagh,et al.  A Quantum-Based Similarity Method in Virtual Screening , 2015, Molecules.

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

[6]  Jérôme Hert,et al.  Enhancing the Effectiveness of Fingerprint-Based Virtual Screening: Use of Turbo Similarity Searching and of Fragment Frequencies of Occurrence , 2009, PRIB.

[7]  M. Rawlins.  Cutting the cost of drug development? , 2004, Nature Reviews Drug Discovery.

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

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

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

[11]  Peter Willett,et al.  Measuring the degree of similarity between objects in text retrieval systems , 1993 .

[12]  Valerie J. Gillet,et al.  Analysis of Data Fusion Methods in Virtual Screening: Theoretical Model , 2006, J. Chem. Inf. Model..

[13]  Johann Gasteiger,et al.  The central role of chemoinformatics , 2006 .

[14]  Igor V. Pletnev,et al.  Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions , 2003, J. Chem. Inf. Comput. Sci..

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

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

[17]  Peter Willett,et al.  Evaluation of Similarity Measures for Searching the Dictionary of Natural Products Database , 2003, J. Chem. Inf. Comput. Sci..

[18]  Xin Chen,et al.  Performance of Similarity Measures in 2D Fragment-Based Similarity Searching: Comparison of Structural Descriptors and Similarity Coefficients , 2002, J. Chem. Inf. Comput. Sci..

[19]  Cícero Nogueira dos Santos,et al.  Boosting Docking-Based Virtual Screening with Deep Learning , 2016, J. Chem. Inf. Model..

[20]  Naomie Salim,et al.  Similarity‐Based Virtual Screening with a Bayesian Inference Network , 2009, ChemMedChem.

[21]  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.

[22]  Adrià Cereto-Massagué,et al.  Molecular fingerprint similarity search in virtual screening. , 2015, Methods.

[23]  Frank K Brown,et al.  Practical Approaches to Efficient Screening: Information-Rich Screening Protocol , 2004, Journal of biomolecular screening.