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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“Concerning the above descriptors, they encode the atomic or molecular properties, overall molecular connectivity, molecular geometry, and their size and shape [40].”
Background
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“At present, many types of molecular descriptors such as topological indices and quantum chemical parameters have been proposed to describe the structural features of molecules (Karelson, 2000; Devillers and Balaban, 1999; Todeschini and Consonni, 2000).”
Background
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Background
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Background
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“To date, over 5000 molecular descriptors have been developed [38].”
Background
Explainable Deep Relational Networks for Predicting Compound–Protein Affinities and Contacts
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Method
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Background
QSAR modeling of acute toxicity by balance of correlations
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Method
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Augmented Zagreb Index of Connected Graphs
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Computing First General Zagreb Index of Operations on Graphs
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Method
Synthesis, antibacterial evaluation and QSAR studies of 7-[4-(5-aryl-1,3,4-oxadiazole-2-yl)piperazinyl] quinolone derivatives
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Sum-connectivity index of molecular trees
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Prediction of the in vitro intrinsic clearance determined in suspensions of human hepatocytes by using artificial neural networks
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Rice, B. M., Byrd, E. F. C., & Mattson, W. D. (2007) · Springer eBooks · doi:10.1007/430_2006_053 · Cited by 57
“An extensive compilation and description of over two thousand molecular descriptors are provided by Todeschini and Consonni [20].”
Background
Quantitative structure–activity relationship studies of HIV-1 integrase inhibition. 1. GETAWAY descriptors
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Classification Tree Models for the Prediction of Blood−Brain Barrier Passage of Drugs
Deconinck, E., Zhang, M. H., Coomans, D. H., et al. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050518s · Cited by 57
The Zagreb topological indices for a type of Benzenoid systems jagged-rectangle
Gao, W., & Farahani, M. R. (2017) · Journal of Interdisciplinary Mathematics · doi:10.1080/09720502.2016.1232037 · Cited by 56
“Here our notations are standard and mainly taken from [34-36].”
Background
QSAR and QSPR Studies of a Highly Structured Physicochemical Domain
Nicolotti, O., & Carotti, A. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050293l · Cited by 56
Structure−Activity Models for Contact Sensitization
Fedorowicz, A., Singh, H., Soderholm, S. C., et al. (2005) · Chemical Research in Toxicology · doi:10.1021/tx0497806 · Cited by 56
On the largest eigenvalue of the distance matrix of a connected graph
Zhou, B., & Trinajstić, N. (2007) · Chemical Physics Letters · doi:10.1016/j.cplett.2007.09.048 · Cited by 56
QSPR model of Henry’s law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach
Modarresi, H., Modarress, H., & Dearden, J. C. (2006) · Chemosphere · doi:10.1016/j.chemosphere.2006.09.049 · Cited by 56
QSPR prediction of flash point of esters by means of GFA and ANFIS
Khajeh, A., & Modarress, H. (2010) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2010.03.060 · Cited by 56
Statistical Design of Pools Using Optimal Coverage and Minimal Collision
Remlinger, K. S., Hughes‐Oliver, J. M., Young, S. S., et al. (2006) · Technometrics · doi:10.1198/004017005000000481 · Cited by 56
Development of a model for predicting reaction rate constants of organic chemicals with ozone at different temperatures
Li, X., Zhao, W., Li, J., et al. (2013) · Chemosphere · doi:10.1016/j.chemosphere.2013.03.040 · Cited by 56
Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays
Casañola‐Martín, G. M., Marrero‐Ponce, Y., Khan, M. T. H., Ather, A., Khan, K. M., Torrens, F., & Rotondo, R. (2007) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2007.01.026 · Cited by 56
Random Forest classification based on star graph topological indices for antioxidant proteins
Fernández-Blanco, E., Aguiar‐Pulido, V., Munteanu, C. R., & Dorado, J. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.10.006 · Cited by 56
In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future
Kar, S., Roy, J. K., & Leszczyński, J. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0025-z · Cited by 56
Prediction on the mutagenicity of nitroaromatic compounds using quantum chemistry descriptors based QSAR and machine learning derived classification methods
Hao, Y., Sun, G., Fan, T., Sun, X., Liu, Y., Zhang, N., Zhao, L., Zhong, R., & Peng, Y. (2019) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2019.109822 · Cited by 58
In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse
Hao, Y., Sun, G., Fan, T., Tang, X., Zhang, J., Liu, Y., Zhang, N., Zhao, L., Zhong, R., & Peng, Y. (2020) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2020.122981 · Cited by 56
Relation between hyper-Wiener and Wiener index
Gutman, İ. (2002) · Chemical Physics Letters · doi:10.1016/s0009-2614(02)01343-x · Cited by 56
Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine
Luan, F., Xue, C., Zhang, R., et al. (2005) · Analytica Chimica Acta · doi:10.1016/j.aca.2004.12.085 · Cited by 56
Application of ab initio theory for the prediction of acidity constants of some 1-hydroxy-9,10-anthraquinone derivatives using genetic neural network
Hemmateenejad, B., Safarpour, M. A., & Taghavi, F. (2003) · Journal of Molecular Structure THEOCHEM · doi:10.1016/s0166-1280(03)00418-4 · Cited by 56
“A wide variety of descriptors have been reported for using in QSPR analysis [7–10].”
Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor
Wicker, J. G. P., & Cooper, R. I. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00565 · Cited by 59
Physical pooling functions in graph neural networks for molecular property prediction
Schweidtmann, A. M., Rittig, J. G., Weber, J. M., Grohe, M., Dahmen, M., Leonhard, K. O., & Mitsos, A. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108202 · Cited by 56
“Commonly, for organic molecules the hydrogen (H) atoms are omitted and replaced by the hydrogen count as a node feature [41]; this results in reducing the complexity of molecular graphs and therefore reducing data demand.”
Background
Effect of the electronic and physicochemical parameters on the carcinogenesis activity of some sulfa drugs using QSAR analysis based on genetic-MLR and genetic-PLS
Deeb, O., Hemmateenejad, B., Jaber, A., Garduño‐Juárez, R., & Miri, R. (2007) · Chemosphere · doi:10.1016/j.chemosphere.2006.12.098 · Cited by 54
“A wide variety of descriptors including constitutional, topological and quantum chemical have been reported for using in QSAR analysis (Todescini and Consonni, 2000).”
Method
A self‐adaptive genetic algorithm‐artificial neural network algorithm with leave‐one‐out cross validation for descriptor selection in QSAR study
Wu, J., Mei, J., Wen, S., et al. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21471 · Cited by 55
QSAR prediction of inhibition of aldose reductase for flavonoids
Mercader, A. G., Duchowicz, P. R., Fernández, F. M., Castro, E. A., Bennardi, D. O., Autino, J. C., & Romanelli, G. P. (2008) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2008.06.004 · Cited by 55
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“[47] R. Todeschini, V. Consonni, Handbook of Molecular Descriptors, Wiley-WCH, Weinheim, 2000.”
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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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