Cited by 4,107Graph theory and applicationsComputational Drug Discovery MethodsAdvanced Chemical Physics Studies

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Handbook of Molecular Descriptors

Todeschini, Roberto, Consonni, Viviana·2000·Methods and Principles in Medicinal Chemistry

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Abstract

Users guide notations acronyms list of molecular descriptors. Appendices: counting and topological descriptors calculation of descriptors tables of molecular descriptor values.

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Similarity searching
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Background
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Background
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Background
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“There are a great number of ways to numerically characterize a compound; see Chapter 3 of Leach and Gillet (2003)14 or the encyclopedic book of Todeschini and Consonni (2000), hereafter TC.15,16 There are two typical styles of descriptors, bit strings of 0’s and 1’s and vectors of continuous…”
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“5 posed ( Todeschini and Consonni, 2002 ). These descriptors include in particular physicochemical properties of the molecules, such as its solubility or logP, descriptors derived from the 2D structure of the molecule, such as fragment counts or structural fingerprints, or descriptors extracted from the 3D structure (…”
Background
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Method
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Background
On the inverse sum indeg index
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Background
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“[15] R.”
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“These descriptors are inherently three-dimensional and a relatively new addition to the other 2D/3D descriptor collections used in predictive QSAR modeling (51, 52 ).”
Background
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“Molecular descriptors can be classi  ed into zero-dimensional (0D)- descriptors ( e.g. molecular weight), 1D-descriptors ( e.g. counts of certain molecular fragments) and 2D-descriptors ( e.g. molecular constitution in terms of atom types and their connectivity 23 ).”
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“The descriptor PCR is the ratio of multiple path counts to path counts [24].”
BackgroundMethodInfluential
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Randić index and information
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Hierarchical and programmable one-pot synthesis of oligosaccharides
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Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity
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“3D-MoRSE descriptors (3D Molecule Representation of Structures based on Electron diffraction) are derived from Infrared spectra simulation using a generalized scattering function [42].”
Method
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Zhang, L., Fourches, D., Sedykh, A., Zhu, H., Golbraikh, A., Ekins, S., Clark, J. A., Connelly, M., Sigal, M. S., Hodges, D., et al. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300421n · Cited by 101
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Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure–retention relationship studies
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QNA-based ‘Star Track’ QSAR approach
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“Many characteristics of chemical compounds can be calculated on the basis of structural formula 1 , 5 , 17–21 .”
Background
Hierarchic system of QSAR models (1D–4D) on the base of simplex representation of molecular structure
Kuz’min, V. Е., Artemenko, A. G., Polischuk, P. G., Muratov, E. N., Hromov, A. I., Liahovsky, A. V., Andronati, S. A., & Makan, S. Y. (2005) · Journal of Molecular Modeling · doi:10.1007/s00894-005-0237-x · Cited by 98
Combinatorial QSAR of Ambergris Fragrance Compounds
Kovatcheva, A., Golbraikh, A., Oloff, S., Xiao, Y.-D., Zheng, W., Wolschann, P., Buchbauer, G., & Tropsha, A. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034203t · Cited by 100
Similarity methods in chemoinformatics
Willett, P. (2009) · Annual Review of Information Science and Technology · doi:10.1002/aris.2009.1440430108 · Cited by 101
Ligand - based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks
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The Application of Modeling and Prediction to the Formation and Stability of Amorphous Solid Dispersions
DeBoyace, K., & Wildfong, P. L. D. (2017) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2017.03.029 · Cited by 101
On the interpretation and interpretability of quantitative structure–activity relationship models
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“A wide variety of molecular descriptors [ 102 ] is available for use in QSAR model development.”
Method
Quantitative structure–activity relationship prediction of blood-to-brain partitioning behavior using support vector machine
Golmohammadi, H., Dashtbozorgi, Z., & Acree, W. E. (2012) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2012.06.021 · Cited by 98
“A wide variety of descriptors have been proposed for QSAR analysis (Karelson et al., 1996; Todeschini and Consonni, 2000), such as topological descriptors, geometrical descriptors, electrostatic descriptors and quantum chemical descriptors.”
Development, Validation and Inspection of the Applicability Domain of QSPR Models for Physicochemical Properties of Polybrominated Diphenyl Ethers
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SHED: Shannon Entropy Descriptors from Topological Feature Distributions
Gregori‐Puigjané, E., & Mestres, J. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0600509 · Cited by 98
Unified QSAR and network‐based computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals
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Genetic programming for computational pharmacokinetics in drug discovery and development
Archetti, F., Lanzeni, S., Messina, E., & Vanneschi, L. (2007) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-007-9040-z · Cited by 104
“For a detailed discussion on molecular descriptors, see for instance [10].”
Background
A DFT-based quantum theoretic QSAR study of aromatic and heterocyclic sulfonamides as carbonic anhydrase inhibitors against isozyme, CA-II
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Surface and Colloid Chemistry
Birdi, K. S. (2009) · CRC Press eBooks · doi:10.1201/b10154 · Cited by 102
How Accurately Can We Predict the Melting Points of Drug-like Compounds?
Tetko, I. V., Sushko, Y., Novotarskyi, S., Patiny, L., Kondratov, I. S., Petrenko, A. E., Charochkina, L., & Asiri, A. M. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5005288 · Cited by 96
Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks
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Chemoinformatics—an introduction for computer scientists
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“However, there are many possible approaches to calculating informative molecular descriptors [Karelson 2000; Todeschini and Consonni 2000].”
Method
A Novel/Old Modification of the First Zagreb Index
Ali, A., & Trinajstić, N. (2018) · Molecular Informatics · doi:10.1002/minf.201800008 · Cited by 95
“[5,6] According to Todeschini and Consonni[6] “molecular descriptor is the final result of a logical and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into an useful number or the result of some standardized experiment”.”
BackgroundInfluential
Degree-based energies of graphs
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The spinosyns, spinosad, spinetoram, and synthetic spinosyn mimics ‐ discovery, exploration, and evolution of a natural product chemistry and the impact of computational tools
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On atom–bond connectivity index of connected graphs
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Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods
Yap, C. W., Li, Z., & Chen, Y. (2005) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2005.10.004 · Cited by 95
INCIDENCE ENERGY OF A GRAPH
Jooyandeh, M., Kiani, D., & Mirzakhah, M. (2009) · Cited by 94
Aqueous OH Radical Reaction Rate Constants for Organophosphorus Flame Retardants and Plasticizers: Experimental and Modeling Studies
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Exploring Quantitative Nanostructure-Activity Relationships (QNAR) Modeling as a Tool for Predicting Biological Effects of Manufactured Nanoparticles
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A novel QSAR model for predicting induction of apoptosis by 4-aryl-4H-chromenes
Afantitis, A., Melagraki, G., Sarimveis, H., Koutentis, P. A., Markopoulos, J., & Igglessi‐Markopoulou, O. (2006) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2006.05.061 · Cited by 92
Ligand-Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates
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Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity
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Generative Models for Automatic Chemical Design
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“Thousands of different descriptors are available for chemical prediction meth-ods [127].”
Background
Learning Invariant Representations of Molecules for Atomization Energy Prediction
Montavon, G., Hansen, K., Fazli, S., Biegler, F., Ziehe, A., Tkatchenko, A., von Lilienfeld, O. A., & Müller, K.-R. (2014) · Cited by 97

Authors: Todeschini, R., & Consonni, V.
ORCID: Todeschini, Roberto
Consonni, Viviana
Venue: Methods and Principles in Medicinal Chemistry
Publisher: Wiley
Published: 2000-09-22
ISSN: 1432-4636 (ISSN-L)
1865-0562
DOI: 10.1002/9783527613106
OpenAlex: W1545231783
Cited by: 4,107
References: 0
Citations by year: 2026: 55 · 2025: 93 · 2024: 145 · 2023: 149 · 2022: 143 · 2021: 201 · 2020: 183 · 2019: 177 · 2018: 163 · 2017: 176 · 2016: 191 · 2015: 171 · 2014: 170 · 2013: 188 · 2012: 231
FWCI: 32.85
Type: book
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Methods and principles in medicinal chemistry (publishedVersion)
BOA (University of Milano-Bicocca) (submittedVersion)
Topics: Physical Sciences › Mathematics › Discrete Mathematics and Combinatorics › Graph theory and applications
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords: molecular descriptors, topological indices, chemical graph theory

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