New trends in (Q)SAR modeling with topological indices

Current Opinion in Drug Discovery and Development

Volumn 3, Issue 3, 2000, Pages 275-279

  Devillers, James ^a  

^a CTIS   (France)

Author keywords

Molecular descriptors; Molecular graphs; QSAR; SAR; Topological indices

Indexed keywords

ANALYTIC METHOD; ARTICLE; COVALENT BOND; DATA ANALYSIS; DRUG DESIGN; MATHEMATICAL MODEL; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

EID: 0033934394     PISSN: 13676733     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (33)

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2. Devillers J (Ed): Genetic Algorithms in Molecular Modeling. Academic Press, London, UK (1996). •• Currently the unique book available on the subject.
3. Handle M: Topological indices. In: The Encyclopedia of Computational Chemistry. Schleyer PP, Allinger ML, Clark T, Gasteiger J, Kollman PA, Schaefer HF, Schreiner PR (Eds), John Wiley & Sons, Chichester, UK (1998) 5:3018-3032.
4. Kier LB, Hall LH: Molecular Connectivity in Structure-activity Analysis. Wiley, London, UK (1986). •• This classical book provides all of the necessary information needed to calculate the molecular connectivity indices which remain the most widely used topological descriptors in QSAR and QSPR.
5. Kier LB, Hall LH: Molecular Structure Description: The Eectrotopological State. Academic Press, New York, USA (1999). •• This book, and its companion CD, allow one to easily understand and calculate the E-sate indices.
6. Balaban AT, Ivanciuc O: Historical development of topological indices. In: Topological Indices and Related Descriptors in QSAR and QSPR. Devillers J, Balaban AT (Eds), Gordon and Breach, The Netherlands (1999):21-57. •• Based on 151 references, this particularly well written article reviews the different topological indices which have been designed since the pioneering work of Harold Wienerin 1947.
7. Devillers J, Balaban AT (Eds): Topological Indices and Related Descriptors in QSAR and QSPR. Gordon and Breach, The Netherlands (1999). •• Numerous examples of calculations, fully analyzed case studies, algorithms, software and interesting sites on the Internet are detailed in this book.
8. Devillers J, Domine D: A noncongeneric model for predicting toxicity of organic molecules to Vibrio fischeri. SAR QSAR Environ Res (1999) 10:61-70.
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17. Devillers J, Bintein S, Domine D, Karcher W: A general QSAR model for predicting the toxicity of organic chemicals to luminescent bacteria (Microtoxtest). SAR QSAR Environ Res (1995) 4:29-38.
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22. Ajay A, Walters WP, Murcko MA: Can we learn to distinguish between 'drug-like' and 'nondrug-like' molecules? J Med Chem (1998) 41:3314-3324.
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26. Domine D, Guillon C, Devillers J, Lacroix R, Lacroix J, Doré JC: Nonlinear neural mapping analysis of the adverse effects of drugs. SAR QSAR Environ Res (1998) 8:109-120. • The N2M method (Kohonen self-organizing map + nonlinear mapping + minimum spanning tree) was applied to a large collection of data retrieved from the Vidai CD-ROM (Editions du Vidai, Paris, 1991) to derive a typology of molecules presenting possible adverse effects when driving vehicles. In addition, attempts were made to try to link the adverse effects of these molecules to therapeutic categories and structural information.
27. Rios-Santamarina I, Garcia-Domenech R, Galvez J, Cortijo J, Santamaria P, Morcillo E: New bronchodilators selected by molecular topology. Bioorg Med Chem J.eff (1998) 8:477-482. • The data are only partially provided by the authors, but a note indicates that an electronic copy of the different data sets can be obtained by contacting the senior author (Dr J Galvez, jorge.galvez@ use.es).
28. Hall LH, Kier LB: Molecular connectivity chi indices for database analysis and structure-property modeling. In: Topological Indices and Related Descriptors in QSAR and QSPR. Devillers J, Balaban AT (Eds), Gordon and Breach, The Netherlands (1999):307-360.
29. Basak SC, Gute BD, Grunwald GD: Assessment of the mutagenicity of aromatic amines from theoretical structural parameters: A hierarchical approach. SAR QSAR Environ Res (1999) 10:117-129. • This article clearly introduces a method applied to a limited set of 127 molecules, but other studies have been performed on larger sets of molecules, ie, > 500; presented by these authors in reference [7]. Note that topostructural indices are descriptors encoding information about the adjacency and distances of atoms (vertices) in molecular graphs irrespective of the chemical nature of the atoms involved in the bonding or factors, such as hybridization states of atoms and number of core/valence electrons in individual atoms. Topochemical indices are quantifying information regarding connectivity of atoms, as well as specific chemical properties of the atoms constituting a molecule. They are derived from molecular graphs where each vertex is properly weighted with selected chemical/physical properties.
30. Estrada E: On the topological sub-structural molecular design (TOSS-MODE) in QSPR/QSAR and drug design research. SAR QSAR Environ Res (2000) 11:55-73. •• This article clearly presents the calculation procedure for spectral moments, summarizes different case studies published elsewhere and provides information on the software designed by the author for a practical use. The software is free and can be obtained by contacting Dr £Estrada estrada66@yahoo.com.
31. Estrada E, Pena A, Garcia-Domenech R: Designing sedative/hypnotic compounds from a novel substructural graph-theoretical approach. J Comput Aided Mol Des (1998) 12:583-595. • An example of an application of the TOSS-MODE approach. Note that there is an error in this paper. Indeed, after discussion with Dr Estrada, it appears that 264 compounds were used to design the training set and testing set of 187 and 77 compounds, respectively (the incorrect sentence appears on page 586 at the top of the second column).
32. Estrada E, Un'arte E, Montera A, Teijeira M, Santana L, de Clercq E: A novel approach for the virtual screening and rational design of anticancer compounds. J Med Chem (2000) in press. • The 294 chemicals used to design testing and testing sets are provided as supplementary material.
33. Ivanciuc O: The neural network Mol Net prediction of alkane enthalpies. Anal Chim Ada (1999) 384:271-284.