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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Upper bounds for the Zagreb indices and the spectral radius of series‐parallel graphs
Zhou, B. (2006) · International Journal of Quantum Chemistry · doi:10.1002/qua.21223 · Cited by 37
Comparison of global and mode of action-based models for aquatic toxicity
Martin, T. M., Young, D. M., Lilavois, C. R., & Barron, M. G. (2015) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2015.1018939 · Cited by 38
“The most common descriptors selected include molecular fragment counts [40], autocorrelation [44], E-state [45] and constitutional [46].”
Background
New Upper Bounds for the First Zagreb Index
Hosamani, S. M., & Basavanagoud, B. (2015) · Cited by 37
Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis
Ren, Y., Zhou, L., Li, Y., Liu, P. Y., Zhao, B., & Liu, H. (2016) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2016.1229691 · Cited by 37
Computing Zagreb Indices of the Subdivision-Related Generalized Operations of Graphs
Liu, J., Javaid, M., & Awais, H. M. (2019) · IEEE Access · doi:10.1109/access.2019.2932002 · Cited by 37
Open problems on the exponential vertex-degree-based topological indices of graphs
Das, K. C., Elumalai, S., & Balachandran, S. (2021) · Discrete Applied Mathematics · doi:10.1016/j.dam.2021.01.018 · Cited by 37
Degree Kirchhoff Index of Unicyclic Graphs 1
Feng, L., Gutman, İ., & Yu, G. (2013) · Cited by 37
Development of QSAR models for prediction of fish bioconcentration factors using physicochemical properties and molecular descriptors with machine learning algorithms
Kobayashi, Y., & Yoshida, K. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101285 · Cited by 37
QSPR study of absorption maxima of organic dyes for dye-sensitized solar cells based on 3D descriptors
Xu, J., Zhang, H., Wang, L., Liang, G., Wang, L., Shen, X., & Xu, W. (2010) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2010.03.027 · Cited by 37
Aconitum and Delphinium alkaloids of curare-like activity. QSAR analysis and molecular docking of alkaloids into AChBP.
Turabekova, M., Rasulev, B., Dzhakhangirov, F., Leszczyńska, D., & Leszczynski, J. (2010) · European journal of medicinal chemistry · doi:10.1016/j.ejmech.2010.05.042 · Cited by 31
“In total 84 descriptors have been applied in our study including so called physicochemical, constitutional, geometrical, charge, functional groups, properties and quantum-chemical descriptors [33].”
Method
Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks
Mokhtari, F., & Hossein‐Zadeh, G. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.10.012 · Cited by 38
Markovian chemicals “in silico” design (MARCH-INSIDE), a promising approach for computer-aided molecular design III: 2.5D indices for the discovery of antibacterials
González‐Díaz, H., Torres-Gomez, L. A., Guevara, Y. G., et al. (2005) · Journal of Molecular Modeling · doi:10.1007/s00894-004-0228-3 · Cited by 37
“This method is based on the representation of molecular structure by certain numbers, the so-called molecular descriptors, which are thereafter connected with biological activity by regression techniques [2, 3].”
Method
On two types of geometric–arithmetic index
Zhou, B., Gutman, İ., Furtula, B., et al. (2009) · Chemical Physics Letters · doi:10.1016/j.cplett.2009.09.102 · Cited by 37
An overview of descriptors to capture protein properties – Tools and perspectives in the context of QSAR modeling
Emonts, J., & Buyel, J. F. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.05.022 · Cited by 37
“The descriptor concept is well established in chemometrics, where it is used to characterize small-molecule compounds with a mass of less than 5000 kDa [15–17].”
iSIM: instant similarity
López-Pérez, K., Kim, T. D., & Miranda‐Quintana, R. A. (2024) · Digital Discovery · doi:10.1039/d4dd00041b · Cited by 36
Anticonvulsant activity of artificial sweeteners: A structural link between sweet-taste receptor T1R3 and brain glutamate receptors
Talevi, A., Enrique, A., & Bruno‐Blanch, L. E. (2012) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2012.04.076 · Cited by 36
An efficient heuristic approach to detecting graph isomorphism based on combinations of highly discriminating invariants
Dehmer, M., Grabner, M., Mowshowitz, A., et al. (2012) · Advances in Computational Mathematics · doi:10.1007/s10444-012-9281-0 · Cited by 36
“In chemistry, so-called topological indices representing graph invariants have been investigated extensively [48].”
Influential
Development of validated QSPR models for impact sensitivity of nitroaliphatic compounds
Prana, V., Fayet, G., Rotureau, P., et al. (2012) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2012.07.036 · Cited by 36
“According to Todeschini and Consonni [35], the “molecular descri pto is the final result of a logic and mathematical procedure which transforms chemical inf ormation encoded within a symbolic representation of a molecule into a useful number o r the result of some standardized experiment” .”
Method
QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs
Li, J., & Gramatica, P. (2010) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2010.528254 · Cited by 36
“The list and meaning of the molecular descriptors is provided by the DRAGON package, and the calculation procedure is explained in detail, with related references, in the Handbook of Molecular Descriptors [29].”
Method
Feature selection method based on fuzzy entropy for regression in QSAR studies
Elmi, Z. M., Faez, K., Goodarzi, M., et al. (2009) · Molecular Physics · doi:10.1080/00268970903078559 · Cited by 36
“…Holistic Invariant Molecular descriptors), 3D-MoRSE (3DMolecular Representation of Structure based on Electron diffraction), molecular walk count, 2D-autocorrelation, aromaticity index, Randic molecular profile, radial distribution function, functional group, and atom-centred fragment classes [19].”
Fast Computer Search for Trees with Minimal ABC Index Based on Tree Degree Sequences
Lin, W., Chen, J., Gao, T., et al. (2014) · Cited by 36
M-polynomial and neighborhood M-polynomial methods for topological indices of porous graphene
Shanmukha, M. C., Usha, A., Shilpa, K. C., et al. (2021) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-021-02074-8 · Cited by 36
Energetic Materials
Leszczyński, J., Damavarapu, R., Steevens, J. A., et al. (2017) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-319-59208-4 · Cited by 36
QSAR Modeling of Acute Toxicity for Nitrobenzene Derivatives Towards Rats: Comparative Analysis by MLRA and Optimal Descriptors
Toropov, A. A., Rasulev, B., & Leszczyński, J. (2007) · QSAR & Combinatorial Science · doi:10.1002/qsar.200610135 · Cited by 36
“The meaning of these molecular descriptors and the calculation procedures are summarized elsewhere [39].”
BackgroundMethodInfluential
In Silico Prediction of Estrogen Receptor Subtype Binding Affinity and Selectivity Using Statistical Methods and Molecular Docking with 2-Arylnaphthalenes and 2-Arylquinolines
Wang, Z., Li, Y., Ai, C., & Wang, Y. (2010) · International Journal of Molecular Sciences · doi:10.3390/ijms11093434 · Cited by 36
QSAR Modeling of in Vitro Inhibition of Cytochrome P450 3A4*
Mao, B., Gozalbes, R., Barbosa, F., et al. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0600915 · Cited by 36
Identifying potential endocrine disruptors among industrial chemicals and their metabolites – development and evaluation of in silico tools
Rybacka, A., Rudén, C., Tetko, I. V., & Andersson, P. L. (2015) · Chemosphere · doi:10.1016/j.chemosphere.2015.07.036 · Cited by 36
Very-long-chain fatty acid sphingomyelin in nuclear lipid microdomains of hepatocytes and hepatoma cells: can the exchange from C24:0 to C16:0 affect signal proteins and vitamin D receptor?
Lazzarini, A., Macchiarulo, A., Floridi, A., Coletti, A., Cataldi, S., Codini, M., Lazzarini, R., Bartoccini, E., Cascianelli, G., Ambesi‐Impiombato, F. S., et al. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-04-0229 · Cited by 36
Chapter 8 Machine Learning in Computational Chemistry
Goldman, B. B., & Walters, W. P. (2006) · Annual reports in computational chemistry · doi:10.1016/s1574-1400(06)02008-1 · Cited by 37
Trees of extremal connectivity index
Liu, H., Lu, M., & Tian, F. (2005) · Discrete Applied Mathematics · doi:10.1016/j.dam.2004.10.009 · Cited by 36
“Mathematical descriptors of molecular structure, such as various topological indices, have been widely used in structure-property-activity studies (see [13,14,21]).”
Background
Steric Control of Conductivity in Highly Conjugated Polythiophenes
Benincori, T., Consonni, V., Gramatica, P., et al. (2001) · Chemistry of Materials · doi:10.1021/cm0009118 · Cited by 36
Self-citation
International Journal of Chemistry and Pharmaceutical Sciences
Dhani, P. R. (2014) · ADVANCED SCIENCES INDEX · Cited by 36
Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors
Capuzzi, S. J., Sun, W., Muratov, E. N., Martínez-Romero, C., He, S., Zhu, W., Li, H., Tawa, G., Fisher, E. G., Xu, M., et al. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00035 · Cited by 35
AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection
Clyde, A., Liu, X., Brettin, T. S., Yoo, H., Partin, A., Babuji, Y., Blaiszik, B. J., Mohd-Yusof, J., Merzky, A., Turilli, M., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-28785-9 · Cited by 38
Quantitative Structure–Property Relationship Studies for Predicting Flash Points of Organic Compounds using Support Vector Machines
Pan, Y., Jiang, J., Wang, R., et al. (2008) · QSAR & Combinatorial Science · doi:10.1002/qsar.200810009 · Cited by 35
Quantitative Structure–Retention Relationships as Useful Tool to Characterize Chromatographic Systems and Their Potential to Simulate Biological Processes
Giaginis, C., & Tsantili‐Kakoulidou, A. (2012) · Chromatographia · doi:10.1007/s10337-012-2374-6 · Cited by 35
In silico applications of bioisosterism in contemporary medicinal chemistry practice
Papadatos, G., & Brown, N. (2013) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1148 · Cited by 37
“Given, however, the existence of thousands of descriptors, 25 this is not a trivial exercise.”
BackgroundMethod
Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices
Pérez-Bello, A., Munteanu, C. R., Ubeira, F. M., Magalhães, A. L., Uriarte, E., & González‐Díaz, H. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.09.035 · Cited by 35
“The result file contains the following embedded (super index ‘‘e’’) or non-embedded TIs (Todeschini and Consonni, 2002): Shannon Entropy of the n powered Markov matrices (SGyn): SGyðeÞn ¼ X i pi logðpiÞ (6) where pi are the ni elements of the p vector, resulted from the matrix multiplication of the…”
17β-carboxamide steroids – in vitro prediction of human skin permeability and retention using PAMPA technique
Dobričić, V., Marković, B. D., Nikolic, K. M., Savić, V., Vladimirov, S. M., & Cudina, O. A. (2013) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2013.10.017 · Cited by 35
Sharp bounds on Zagreb indices of cacti with k pendant vertices
Li, S., Yang, H., & Zhao, Q. (2012) · Filomat · doi:10.2298/fil1206189l · Cited by 35
“In 1975, Randić proposed a structural descriptor called branching index [57] that later became well-known Randić connectivity index, which is the most used molecular descriptor in QSPR and QSAR; see [20, 33, 34, 55, 63].”
Method
Chemical applications of graph theory
Burch, K. J. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813651-5.00008-5 · Cited by 35
Evaluation of Cross-Validation Strategies in Sequence-Based Binding Prediction Using Deep Learning
Rio, A. L.-D., Nonell‐Canals, A., Vidal, D., & Perera-Lluna, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00663 · Cited by 39
Extremal trees for the modified first Zagreb connection index with fixed number of segments or vertices of degree 2
Noureen, S., Bhatti, A. A., & Ali, A. (2019) · Journal of Taibah University for Science · doi:10.1080/16583655.2019.1699227 · Cited by 35
A Concise Review on the Significance of QSAR in Drug Design
Tandon, H., Chakraborty, T., & Suhag, V. (2019) · Chemical and Biomolecular Engineering · doi:10.11648/j.cbe.20190404.11 · Cited by 35
Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets
Abdelbaky, I., Tayara, H., & Chong, K. T. (2021) · Scientific Reports · doi:10.1038/s41598-020-80758-4 · Cited by 35
Perspectives and recent advances in quantitative structure-retention relationships for high performance liquid chromatography. How far are we?
Sagandykova, G. N., & Buszewski, B. (2021) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2021.116294 · Cited by 38
Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)‐based small molecule structure elucidation
Liu, Y., Vijlder, T. D., Bittremieux, W., Laukens, K., & Heyndrickx, W. (2021) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.9120 · Cited by 35
QSAR/QSPR Modeling: Introduction
Roy, K., Kar, S., & Das, R. N. (2015) · Springer briefs in molecular science · doi:10.1007/978-3-319-17281-1_1 · Cited by 35
Quantitative structure-activity relationships of antimicrobial fatty acids and derivatives against Staphylococcus aureus
Zhang, H., Zhang, L., Peng, L., et al. (2012) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b1100049 · Cited by 35
“The 96-well plates were incubated for 24 h at 37 °C, and the optical density at 595 nm (OD595) for 0 and 24 h of the culture was read on a Multiskan MK3 microplate reader (Multiskan MK3, Thermo Labsystems, Finland).”
QSAR study of neuraminidase inhibitors based on heuristic method and radial basis function network
Lü, W., Chen, Y., Ma, W., et al. (2007) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2007.04.011 · Cited by 35
QSAR approach to POPs screening for atmospheric persistence
Gramatica, P., Consolaro, F., & Pozzi, S. (2001) · Chemosphere · doi:10.1016/s0045-6535(00)00418-5 · Cited by 35
Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis
Habibi‐Yangjeh, A., & Danandeh-Jenagharad, M. (2009) · Monatshefte für Chemie - Chemical Monthly · doi:10.1007/s00706-009-0185-8 · Cited by 35
“3D MoRSE descriptors (3D molecule representation of structures based on electron diffraction) are derived from infrared spectra simulation using a generalized scattering function [ 28 ].”
BackgroundMethod
Ligand discovery and virtual screening using the program LIDAEUS
Taylor, P., Blackburn, E. A., Sheng, Y., Harding, S. A., Hsin, K., Kan, D., Shave, S., & Walkinshaw, M. D. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707532 · Cited by 36
“Using N, O, S, P polar contributions (Todeschini and Consonni, 2005).”
BackgroundMethod
Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening
Melagraki, G., Afantitis, A., Sarimveis, H., Koutentis, P. A., Markopoulos, J., & Igglessi‐Markopoulou, O. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9112-4 · Cited by 35
“PSAr has proven to be a very useful parameter for QSAR studies [ 17 ].”
BackgroundMethodInfluential
Some QSAR Studies for a Group of Sulfonamide Schiff Base as Carbonic Anhydrase CA II Inhibitors
Eroğlu, E. (2008) · International Journal of Molecular Sciences · doi:10.3390/ijms9020181 · Cited by 35
Aconitum and Delphinium sp. alkaloids as antagonist modulators of voltage-gated Na+ channels
Turabekova, M. A., Rasulev, B., Levkovich, M. G., et al. (2007) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2007.10.003 · Cited by 35
“…MR; molecular weight, MW; lipophilicity, Mlog P etc.) or quantum-chemical characteristics (energy of HOMO, energy of LUMO, HOMO and LUMO energy gap (Todeschini and Consonni, 2000) calculated for both ground and protonated compounds applying AM1 Hamiltonian) correlates best with toxicity and…”
BackgroundMethod
Topological modeling of lipophilicity, diuretic activity, and carbonic inhibition activity of benzene sulfonamides: a molecular connectivity approach
Jaiswal, M., Khadikar, P. V., & Supuran, C. T. (2004) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2004.08.051 · Cited by 35
A QSAR Toxicity Study of a Series of Alkaloids with the Lycoctonine Skeleton
Turabekova, M. A., & Rasulev, B. (2004) · Molecules · doi:10.3390/91201194 · Cited by 35
Modeling with Molecular Pseudoconnectivity Descriptors. A Useful Extension of the Intrinsic I-State Concept
Pogliani, L. (2000) · The Journal of Physical Chemistry A · doi:10.1021/jp001191v · Cited by 35
A TOPS-MODE approach to predict adenosine kinase inhibition
González, M. P., & Moldes, M. D. C. T. (2004) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2004.04.040 · Cited by 35
ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model
Novotarskyi, S., Abdelaziz, A., Sushko, Y., Körner, R., Vogt, J., & Tetko, I. V. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5b00481 · Cited by 34
Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives
Duarte, J. C., da Rocha, R. D., & Borges, I. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05339j · Cited by 33
A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition
Mirkhani, S. A., Gharagheizi, F., & Sattari, M. (2011) · Chemosphere · doi:10.1016/j.chemosphere.2011.11.021 · Cited by 34
“The molecular descriptor is the final result of a logic and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment (Todeschini and Consonni, 2000).”
Background
QSAR study on melanocortin-4 receptors by support vector machine
Pourbasheer, E., Riahi, S., Ganjali, M. R., & Norouzi, P. (2009) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.12.003 · Cited by 34
SMILES‐Based QSAR Models for the Calcium Channel‐Antagonistic Effect of 1,4‐Dihydropyridines
Veselinović, A. M., Milosavljević, J. B., Toropov, A. A., & Nikolić, G. M. (2012) · Archiv der Pharmazie · doi:10.1002/ardp.201200373 · Cited by 34
In silico prediction and screening of γ‐secretase inhibitors by molecular descriptors and machine learning methods
Yang, X., Lv, W., Chen, Y., et al. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21411 · Cited by 34
“45 The training set is used to develop the prediction models and select most appropriate sets of molecular descriptors, and the external testing set is used for evaluating the performance of the resulting models.”
Method
Bond additive modeling 10. Upper and lower bounds of bond incident degree indices of catacondensed fluoranthenes
Vukičević, D., & Đurđević, J. (2011) · Chemical Physics Letters · doi:10.1016/j.cplett.2011.08.095 · Cited by 34
Combined 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Derivatives of Peptide Epoxyketone and Tyropeptin-Boronic Acid as Inhibitors Against the β5 Subunit of Human 20S Proteasome
Liu, J., Zhang, H., Xiao, Z., Wang, F., Wang, X., & Wang, Y. (2011) · International Journal of Molecular Sciences · doi:10.3390/ijms12031807 · Cited by 35
On Harary Matrix, Harary Index and Harary Energy
Cui, Z., & Liu, B. (2012) · Cited by 34
A new structure-based model for estimation of true critical volume of multi-component mixtures
Sobati, M. A., Abooali, D., Maghbooli, B., et al. (2016) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2016.04.007 · Cited by 34
Critical comparison of shake-flask, potentiometric and chromatographic methods for lipophilicity evaluation (log P o/w ) of neutral, acidic, basic, amphoteric, and zwitterionic drugs
Port, A., Bordas, M., Enrech, R., Pascual, R., Rosés, M., Ràfols, C., Subirats, X., & Bosch, E. (2018) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2018.07.010 · Cited by 33
Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach
Beltran, J. A., Aguilera‐Mendoza, L., & Brizuela, C. A. (2018) · BMC Genomics · doi:10.1186/s12864-018-5030-1 · Cited by 34
Towards understanding aromatase inhibitory activity via QSAR modeling
Shoombuatong, W., Schaduangrat, N., & Nantasenamat, C. (2018) · EXCLI journal · doi:10.17179/excli2018-1417 · Cited by 34
4D- quantitative structure–activity relationship modeling: making a comeback
Fourches, D., & Ash, J. R. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1664467 · Cited by 34
Structure-Antifouling Activity Relationship and Molecular Targets of Bio-Inspired(thio)xanthones
Almeida, J. R., Palmeira, A., Campos, A. M. O., Cunha, I., Freitas, M., Barreiro, A., Turkina, M. V., Vasconcelos, V. M., Pinto, M. M. M., Correia‐da‐Silva, M., et al. (2020) · Biomolecules · doi:10.3390/biom10081126 · Cited by 37
“This type of descriptor reflects how differences in size, shape, and constitution affect the electronic charge distribution and inter-atomic distances of the molecules [39].”
BackgroundInfluential
A relation between a vertex-degree-based topological index and its energy
Gutman, İ., Monsalve, J. D., & Rada, J. (2021) · Linear Algebra and its Applications · doi:10.1016/j.laa.2021.11.021 · Cited by 34
Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design
Suay‐García, B., Bueso-Bordils, J. I., Falcó, A., Antón-Fos, G. M., & Aleman, P. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031620 · Cited by 40
Prediction of the self-accelerating decomposition temperature of organic peroxides using the quantitative structure–property relationship (QSPR) approach
Pan, Y., Zhang, Y., Jiang, J., et al. (2014) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2014.06.007 · Cited by 34
Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network
Habibi‐Yangjeh, A., Pourbasheer, E., & Danandeh-Jenagharad, M. (2008) · Monatshefte für Chemie - Chemical Monthly · doi:10.1007/s00706-008-0951-z · Cited by 34
“A wide variety of molecular descriptors has been reported for using in QSPR=QSAR models [ 10 ].”
Choosing the exponent in the definition of the connectivity index
Gutman, İ., & Lepović, M. (2001) · Journal of the Serbian Chemical Society · doi:10.2298/jsc0109605g · Cited by 34
QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices
González‐Díaz, H., Cruz‐Monteagudo, M., Viña, D., et al. (2005) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2005.01.047 · Cited by 34
StructRank: A New Approach for Ligand-Based Virtual Screening
Rathke, F., Hansen, K., Brefeld, U., et al. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100308f · Cited by 34
Complete characterization of graphs for direct comparing Zagreb indices
Horoldagva, B., Das, K. C., & Selenge, T.-A. (2016) · Discrete Applied Mathematics · doi:10.1016/j.dam.2016.07.008 · Cited by 34
“Soon after that,M1 andM2 were recognized asmeasures of the branching of the carbon-atom molecular skeleton [11], and since then these are frequently used for structure–property modeling [18,19].”
Background
Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds☆Species: Rat; Sex: Male; Route of administration: Water
Helguera, A. M., Cordeiro, M. N. D. S., Cabrera‐Pérez, M. Á., et al. (2008) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2008.04.008 · Cited by 34
Proteins QSAR with Markov average electrostatic potentials
González‐Díaz, H., & Uriarte, E. (2005) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2005.07.056 · Cited by 34
On inverse degree and topological indices of graphs
Das, K. C., Xu, K., & Wang, J. (2016) · Filomat · doi:10.2298/fil1608111d · Cited by 33
“Nowadays, there exists a legion of topological indices that found particular applications in chemistry [26].”
Background
Unified QSAR & network‐based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks
Prado-Prado, F., Ubeira, F. M., Borges, F., & González‐Díaz, H. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21292 · Cited by 33
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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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