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

The DOI matches its Crossref record.

Handbook of Molecular Descriptors

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

Open this paper in CoChat — free account

Abstract

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

Integrity signals

Registry

Crossref · 10.1002/9783527613106

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Sorted by citations

Items 801–900 of 1,001

Chemical Compatibility of Poly(Lactic Acid): A Practical Framework Using Hansen Solubility Parameters
Abbott, S. J. (2010) · Wiley eBooks · doi:10.1002/9780470649848.ch7 · Cited by 46
Exploring Statistical and Population Aspects of Network Complexity
Emmert‐Streib, F., & Dehmer, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034523 · Cited by 44
On the maximum and minimum Zagreb indices of graphs with connectivity at most k
Li, S., & Zhou, H. (2009) · Applied Mathematics Letters · doi:10.1016/j.aml.2009.08.015 · Cited by 42
Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse
Bhhatarai, B., & Gramatica, P. (2010) · Molecular Diversity · doi:10.1007/s11030-010-9268-z · Cited by 45
Chemoinformatics for rational discovery of safe antibacterial drugs: Simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals
Speck‐Planche, A., Kleandrova, V. V., & Cordeiro, M. N. D. S. (2013) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2013.03.015 · Cited by 42
Multi-Target Inhibitors for Proteins Associated with Alzheimer: In Silico Discovery using Fragment-Based Descriptors
Speck‐Planche, A., Kleandrova, V. V., Luan, F., & Cordeiro, M. N. D. S. (2013) · Current Alzheimer Research · doi:10.2174/1567205011310020001 · Cited by 42
“Nowadays, the number of molecular descriptor has increased in remarkable way [9].”
Background
Prediction of antibacterial compounds by machine learning approaches
Yang, X., Chen, D., Wang, M., Xue, Y. W., & Chen, Y. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.21148 · Cited by 43
“20, 26,27, 30– 34 In this work, a group of 198 molecular descriptors manually”
Background
CALCULATING THE DEGREE DISTANCE OF PARTIAL HAMMING GRAPHS
Ili, A., & Klav, S. (2010) · Cited by 42
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
Cortés‐Ciriano, I., & Bender, A. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.43757 · Cited by 39
Docking and QSAR study on the binding interactions between polycyclic aromatic hydrocarbons and estrogen receptor
Li, F., Wu, H., Li, L., Li, X., Zhao, J., & Peijnenburg, W. J. G. M. (2012) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2012.03.009 · Cited by 42
Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds
H., X., Wang, R., Yang, S. Y., Li, Z., Xue, Y. W., Wei, Y., Low, B. C., & Chen, Y. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800022e · Cited by 42
QSPR models for half-wave reduction potential of steroids: A comparative study between feature selection and feature extraction from subsets of or entire set of descriptors
Hemmateenejad, B., & Yazdani, M. (2008) · Analytica Chimica Acta · doi:10.1016/j.aca.2008.11.062 · Cited by 42
Novel topological descriptors for analyzing biological networks
Dehmer, M., Barbarini, N., Вармуза, К., & Graber, A. (2010) · BMC Structural Biology · doi:10.1186/1472-6807-10-18 · Cited by 43
“As already mentioned, topological indices have been proven to be powerful tools in drug design, chemometrics, bioinformatics, and mathematical and medicinal chemistry [23,24,26,28,29,34,41-43].”
Background
A Unified Approach to the Extremal Zagreb Indices for Trees, Unicyclic Graphs and Bicyclic Graphs 1
Deng, H. (2007) · Cited by 42
QSAR and Molecular Docking Techniques for the Discovery of Potent Monoamine Oxidase B Inhibitors: Computer-Aided Generation of New Rasagiline Bioisosteres
Speck‐Planche, A., & Kleandrova, V. V. (2012) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026611209061734 · Cited by 46
Radial basis function network-based quantitative structure–property relationship for the prediction of Henry’s law constant
Yao, X., Liu, M., Zhang, X., et al. (2002) · Analytica Chimica Acta · doi:10.1016/s0003-2670(02)00273-8 · Cited by 42
Accurate prediction of the blood–brain partitioning of a large set of solutes usingab initiocalculations and genetic neural network modeling
Hemmateenejad, B., Miri, R., Safarpour, M. A., et al. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20437 · Cited by 42
Asymmetric bagging and feature selection for activities prediction of drug molecules
Li, G., Meng, H.-H., Lu, W., Yang, J. Y., & Yang, M. Q. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-s6-s7 · Cited by 42
“examples, of which 542 (1.85%) is positive and 28832 (98.15%) is negative. The structure parameters [ 39 ] consist 64”
Influential
NL MIND-BEST: A web server for ligands and proteins discovery—Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum
González‐Díaz, H., Prado-Prado, F., Sobarzo‐Sánchez, E., Haddad, M., Chevalley, S., Valentin, A., Quetin‐Leclercq, J., Dea‐Ayuela, M. A., Gómez-Muñoz, M. T., Munteanu, C. R., et al. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2011.01.010 · Cited by 41
Turning deep-eutectic solvents into value-added products for CO2 capture: A desirability-based virtual screening study
Halder, A. K., Ambure, P. S., Pérez‐Castillo, Y., & Cordeiro, M. N. D. S. (2022) · Journal of CO2 Utilization · doi:10.1016/j.jcou.2022.101926 · Cited by 41
The relationship between the eccentric connectivity index and Zagreb indices
Hua, H., & Das, K. C. (2013) · Discrete Applied Mathematics · doi:10.1016/j.dam.2013.05.034 · Cited by 41
“Later they were used in the structure–propertymodel (see [30]).”
Method
Rational design, efficient syntheses and biological evaluation of N , N ′-symmetrically bis-substituted butylimidazole analogs as a new class of potent Angiotensin II receptor blockers
Agelis, G., Resvani, A., Koukoulitsa, C., Tůmová, T. (., Slaninová, J., Kalavrizioti, D., Spyridaki, K., Afantitis, A., Melagraki, G., Siafaka, A., et al. (2013) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2012.12.044 · Cited by 42
On some topological indices of the tensor products of graphs
Pattabiraman, K., & Paulraja, P. (2011) · Discrete Applied Mathematics · doi:10.1016/j.dam.2011.10.020 · Cited by 41
Topological Anisotropy of Stone-Wales Waves in Graphenic Fragments
Ori, O., Cataldo, F., & Putz, M. V. (2011) · International Journal of Molecular Sciences · doi:10.3390/ijms12117934 · Cited by 41
Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models
Martin, T. M., Grulke, C., Young, D. M., et al. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400267h · Cited by 41
Microwave assisted synthesis and QSAR study of novel NSAID acetaminophen conjugates with amino acid linkers
Tiwari, A. D., Panda, S. S., Girgis, A. S., et al. (2014) · Organic & Biomolecular Chemistry · doi:10.1039/c4ob01281j · Cited by 41
Filling environmental data gaps with QSPR for ionic liquids: Modeling n-octanol/water coefficient
Rybińska‐Fryca, A., Sosnowska, A., Grzonkowska, M., et al. (2015) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2015.10.023 · Cited by 41
Redox Polypharmacology as an Emerging Strategy to Combat Malarial Parasites
Sidorov, P., Desta, I., Chessé, M., et al. (2016) · ChemMedChem · doi:10.1002/cmdc.201600009 · Cited by 41
Proving a Conjecture of Gutman Concerning Trees with Minimal ABC Index
Lin, W., Lin, X., Gao, T., et al. (2013) · Cited by 41
On the first Zagreb index and multiplicative Zagreb coindices of graphs
Das, K. C., Akgüneş, N., Togan, M., et al. (2016) · Analele Universităţii "Ovidius" Constanţa. Seria Matematică · doi:10.1515/auom-2016-0008 · Cited by 41
“Later they were used in the structure-property model (see [20]).”
Method
The wide-spectrum antimicrobial effect of novel N-alkyl monoquaternary ammonium salts and their mixtures; the QSAR study against bacteria
Soukup, O., Benková, M., Doležal, R., Sleha, R., Maliňák, D., Salajkova, S. A., Marková, A., Hympánová, M., Prchal, L., Ryšková, L., et al. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.112584 · Cited by 41
In silicoprediction of human serum albumin binding for drug leads
Vallianatou, T., Lambrinidis, G., & Tsantili‐Kakoulidou, A. (2013) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2013.777424 · Cited by 41
Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods
Li, H., Ung, C. Y., Yap, C. W., Xue, Y. W., Li, Z., & Chen, Y. (2006) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2006.01.007 · Cited by 41
“For the independent evaluation set, these compounds were divided into training and independent validation set based on their distribution in the chemical space, which is defined by the commonly used structural and chemical descriptors [49].”
Method
Docking and 3D-QSAR studies on the Ah receptor binding affinities of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs)
Li, F., Li, X., Liu, X., Zhang, L., You, L., Zhao, J., & Wu, H. (2011) · Environmental Toxicology and Pharmacology · doi:10.1016/j.etap.2011.09.001 · Cited by 41
A Unified Approach to Extremal Cacti for Different Indices
Liu, H., & Lu, M. (2008) · Cited by 41
Predicting the NO3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors
Gramatica, P., Pilutti, P., & Papa, E. (2003) · Atmospheric Environment · doi:10.1016/s1352-2310(03)00293-0 · Cited by 40
A TOPS-MODE approach to predict permeability coefficients
González, M. P., Helguera, A. M., & González‐Díaz, H. (2004) · Polymer · doi:10.1016/j.polymer.2003.12.014 · Cited by 41
Molecular Screening and Toxicity Estimation of 260,000 Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) through Machine Learning
Lai, T. T., Kuntz, D., & Wilson, A. K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00374 · Cited by 41
Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review
Noviandy, T. R., Maulana, A., Idroes, G. M., Emran, T. B., Tallei, T. E., Helwani, Z., & Idroes, R. (2023) · Infolitika Journal of Data Science · doi:10.60084/ijds.v1i1.91 · Cited by 45
“Molecular descriptors are important components of QSAR studies that serve as the quantitative representations of a molecule's structural and chemical characteristics [35].”
Background
On the Maximum Zagreb Indices of Graphs with k Cut Vertices
Zhao, Q., & Li, S. (2009) · Acta Applicandae Mathematicae · doi:10.1007/s10440-009-9534-1 · Cited by 40
“In 1975, Randić proposed a structural descriptor called branching index [48] that later became well-known Randić connectivity index, which is the most used molecular descriptor in QSPR and QSAR; see [20, 32, 33, 46, 52].”
Method
Application of random forests method to predict the retention indices of some polycyclic aromatic hydrocarbons
Goudarzi, N., Shahsavani, D., Emadi-Gandaghi, F., et al. (2014) · Journal of Chromatography A · doi:10.1016/j.chroma.2014.01.048 · Cited by 40
A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection
Speck‐Planche, A., Kleandrova, V. V., Luan, F., & Cordeiro, M. N. D. S. (2012) · Molecular BioSystems · doi:10.1039/c2mb25093d · Cited by 39
Evolutionary Computation and QSAR Research
Aguiar‐Pulido, V., Gestal, M., Cruz‐Monteagudo, M., Rabuñal, J. R., Dorado, J., & Munteanu, C. R. (2013) · Current Computer - Aided Drug Design · doi:10.2174/1573409911309020006 · Cited by 41
Generalized Nonrandom Two-Liquid (NRTL) Interaction Model Parameters for Predicting Liquid–Liquid Equilibrium Behavior
Gebreyohannes, S., Neely, B. J., & Gasem, K. A. M. (2014) · Industrial & Engineering Chemistry Research · doi:10.1021/ie501699a · Cited by 40
Ecotoxicological modelling of cosmetics for aquatic organisms: A QSTR approach
Khan, K., & Roy, K. (2017) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2017.1352621 · Cited by 40
“Eig06_EA(dm) [70] is eigen value n (number of edges) 6 from edge adjacency matrix weighed by dipole moment, and it was inversely correlated with the toxicity of the PCPs.”
Background
Statistical models are able to predict ionic liquid viscosity across a wide range of chemical functionalities and experimental conditions
Beckner, W., Mao, C. M., & Pfaendtner, J. (2018) · Molecular Systems Design & Engineering · doi:10.1039/c7me00094d · Cited by 38
Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing
Sansare, S., Duran, T., Mohammadiarani, H., et al. (2021) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2021.120713 · Cited by 40
Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures
Halder, A. K., Haghbakhsh, R., Voroshylova, I. V., Duarte, A. R. C., & Cordeiro, M. N. D. S. (2021) · Molecules · doi:10.3390/molecules26195779 · Cited by 41
Biological activity and profiling of Salvia sclarea essential oil obtained by steam and hydrodistillation extraction methods via chemometrics tools
Aćimović, M., Cvetković, M., Jeremić, J. S., Pezo, L. L., Varga, A. O., Čabarkapa, I. S., & Kiprovski, B. (2021) · Flavour and Fragrance Journal · doi:10.1002/ffj.3684 · Cited by 40
Use of Molecular Descriptors for Absorption, Distribution, Metabolism, and Excretion Predictions
Winiwarter, S., Ridderström, M., Ungell, A. ‐., et al. (2007) · Elsevier eBooks · doi:10.1016/b0-08-045044-x/00140-1 · Cited by 40
Feature Selection for Descriptor Based Classification Models. 1. Theory and GA-SEC Algorithm
Wegner, J. K., Fröhlich, H., & Zell, A. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0342324 · Cited by 40
In silico-aided prediction of biological properties of chemicals: oestrogen receptor-mediated effects
Roncaglioni, A., & Benfenati, E. (2007) · Chemical Society Reviews · doi:10.1039/b616276m · Cited by 40
Chemical Descriptors Are More Important Than Learning Algorithms for Modelling
Young, S. S., Yuan, F., & Zhu, M. (2012) · Molecular Informatics · doi:10.1002/minf.201200031 · Cited by 40
“We report a small, simple study to show that, for these chemistry problems, the choice of the learning algorithm can be far less important than the choice of the underlying chemical space as defined by the predictors used – note also that a vast array of chemical descriptors is available.([9])”
Background
Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: The nitroindazolinone chemotype
Marrero‐Ponce, Y., Siverio-Mota, D., Gálvez-Llompart, M., Recio, M. C., Giner, R. M., García‐Domenech, R., Torrens, F., Arán, V. J., Cordero-Maldonado, M. L., Esguerra, C. V., et al. (2011) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2011.07.053 · Cited by 41
“These theoretical indices are numbers that describe the structural information of molecules through graph-theoretical invariants and can be considered as structureexplicit descriptors [102].”
Background
Classification and Virtual Screening of Androgen Receptor Antagonists
Li, J., & Gramatica, P. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100078u · Cited by 43
Comparative Study of Topological Indices of Macro/Supramolecular RNA Complex Networks
Agüero-Chapin, G., Antunes, A., Ubeira, F. M., Chou, K., & González‐Díaz, H. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8001809 · Cited by 40
Molecular modelling and QSAR analysis of the estrogenic activity of terpenoids isolated fromFerulaplants
Rasulev, B., Saidkhodzhaev, A. I., Nazrullaev, S. S., et al. (2007) · SAR and QSAR in environmental research · doi:10.1080/10629360701428631 · Cited by 40
“A detailed list of descriptor types calculated by DRAGON is described in Todeschini [32].”
Method
QSPR-based estimation of the atmospheric persistence for chloronaphthalene congeners
Puzyn, T., Mostrąg, A., Suzuki, N., & Falandysz, J. (2008) · Atmospheric Environment · doi:10.1016/j.atmosenv.2008.04.048 · Cited by 40
Stochastic Generator of Chemical Structure. 3. Reaction Network Generation
Faulon, J., & Sault, A. G. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci000029m · Cited by 40
GETAWAY descriptors to predicting A2A adenosine receptors agonists
González, M. P., Terán, C., Teijeira, M., & González‐Moa, M. J. (2005) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2005.04.014 · Cited by 40
A novel approach to predict a toxicological property of aromatic compounds in the Tetrahymena pyriformis
González, M. P., González‐Díaz, H., Cabrera‐Pérez, M. Á., & Molina, R. (2004) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2003.11.028 · Cited by 40
QSAR study on carbonic anhydrase inhibitors: aromatic/heterocyclic sulfonamides containing 8-quinoline-sulfonyl moieties, with topical activity as antiglaucoma agents
Agrawal, V. K., Bano, S., Supuran, C. T., et al. (2004) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2004.03.002 · Cited by 40
Prediction of dihydrofolate reductase inhibition and selectivity using computational neural networks and linear discriminant analysis
Mattioni, B. E., & Jurs, P. C. (2003) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(02)00187-0 · Cited by 40
A class of modified Wiener indices
Gutman, İ., Vukičević, D., & Žerovnik, J. (2004) · University of Maribor digital library (University of Maribor) · Cited by 40
Topological Ranking of C28Fullerenes Reactivity
Ori, O., Cataldo, F., & Graovac, A. (2009) · Fullerenes Nanotubes and Carbon Nanostructures · doi:10.1080/15363830902782332 · Cited by 39
“Several computations of the topological Wiener index W have been performed during the last six decades on various chemical structures 1 seen as graphs made by N carbon atoms arranged, as in fullerenes, in pentagonal or hexagonal rings.”
Method
Natural/random protein classification models based on star network topological indices
Munteanu, C. R., González‐Díaz, H., Borges, F., & Magalhães, A. L. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.07.018 · Cited by 39
“The summary file contains the following TIs (Todeschini and Consonni, 2002): Shannon entropy of the n powered Markov matrices (Shn): Shn ¼ X i pi logðpiÞ, (1) where pi are the ni elements of the p vector, resulted from the matrix multiplication of the powered Markov normalized matrix (ni ni) and a…”
Selection of representative emerging micropollutants for drinking water treatment studies: A systematic approach
Jin, X., & Peldszus, S. (2011) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2011.11.035 · Cited by 39
Computational estimation of logarithm of n-octanol/air partition coefficient and subcooled vapor pressures of 75 chloronaphthalene congeners
Puzyn, T., & Falandysz, J. (2005) · Atmospheric Environment · doi:10.1016/j.atmosenv.2004.11.022 · Cited by 39
Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors
Kumar, A., & Chauhan, S. (2017) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2017.1293729 · Cited by 39
“the possible combination of atom pairs and/or bonds that are present in the structure together but disconnected from each other [48].”
Background
Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space
Hachmann, J., Afzal, M. A. F., Haghighatlari, M., & Pal, Y. (2018) · Molecular Simulation · doi:10.1080/08927022.2018.1471692 · Cited by 38
The possibility to use multi-walled carbon nanotubes as a sorbent for dispersive solid phase extraction of selected pharmaceuticals and their metabolites: Effect of extraction condition
Jakubus, A., Godlewska, K., Gromelski, M., Jagiełło, K., Puzyn, T., Stepnowski, P., & Paszkiewicz, M. (2019) · Microchemical Journal · doi:10.1016/j.microc.2019.02.051 · Cited by 40
QSRR Model for predicting retention indices of Satureja kitaibelii Wierzb. ex Heuff. essential oil composition
Aćimović, M., Pezo, L. L., Tešević, V., Čabarkapa, I. S., & Todosijević, M. M. (2020) · Industrial Crops and Products · doi:10.1016/j.indcrop.2020.112752 · Cited by 39
Modeling the antileishmanial activity screening of 5-nitro-2-heterocyclic benzylidene hydrazides using different chemometrics methods
Garkani‐Nejad, Z., & Ahmadi-Roudi, B. (2009) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.11.019 · Cited by 39
Prediction of cytotoxicity data (CC50) of anti-HIV 5-pheny-l-phenylamino-1H-imidazole derivatives by artificial neural network trained with Levenberg–Marquardt algorithm
Chamjangali, M. A., Beglari, M., & Bagherian, G. (2007) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2007.01.005 · Cited by 39
Molecular modeling of histamine H3 receptor and QSAR studies on arylbenzofuran derived H3 antagonists
Dastmalchi, S., Hamzeh‐Mivehroud, M., Ghafourian, T., & Hamzeiy, H. (2007) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2007.05.002 · Cited by 40
“The predictive power of the models was assessed using leave-one-out cross-validation method [16], as well as performing the internal validation procedure as outlined below.”
Method
G-Protein-Coupled Receptor Affinity Prediction Based on the Use of a Profiling Dataset: QSAR Design, Synthesis, and Experimental Validation
Rolland, C., Gozalbes, R., Nicolaï, E., et al. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm0500673 · Cited by 39
CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals
Bhhatarai, B., Teetz, W., Liu, T., Öberg, T., Jeliazkova, N., Kochev, N. T., Pukalov, O., Tetko, I. V., Kovarich, S., Papa, E., et al. (2011) · Molecular Informatics · doi:10.1002/minf.201000133 · Cited by 39
Prediction of PAH mutagenicity in human cells by QSAR classification
Papa, E., Pilutti, P., & Gramatica, P. (2008) · SAR and QSAR in environmental research · doi:10.1080/10629360701843482 · Cited by 39
“descriptors: the 2D-topological descriptor S1K (1-path Kier alpha-modified shape index) [33,34], and the functional group nArNO2 (the number of aromatic nitro groups).”
Background
Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose
Ahmed, L., Rasulev, B., Kar, S., et al. (2017) · Nanoscale · doi:10.1039/c7nr00770a · Cited by 39
Expected value of first Zagreb connection index in random cyclooctatetraene chain, random polyphenyls chain, and random chain network
Raza, Z., Akhter, S., & Shang, Y. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2022.1067874 · Cited by 38
“The l-degree of a given vertex v ∈ V(H), presented by dl(v), is the cardinality of set of vertices of V(H) whose distance from v is l, where d1(v) dH(v) and d2(v) τv [this is known as the connection number of v (Todeschini and Consonni, 2000)].”
Background
Modelling physico-chemical properties of (benzo)triazoles, and screening for environmental partitioning
Bhhatarai, B., & Gramatica, P. (2010) · Water Research · doi:10.1016/j.watres.2010.11.006 · Cited by 38
Modelling the effect of mixture components on permeation through skin
Ghafourian, T., Samaras, E. G., Brooks, J. D., et al. (2010) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2010.07.014 · Cited by 38
On sum-connectivity matrix and sum-connectivity energy of (molecular) graphs.
Zhou, B., & Trinajstić, N. (2010) · PubMed · Cited by 38
Descriptive and predictive models for Henry’s law constant of CO 2 in ionic liquids: A QSPR study
Ghaslani, D., Gorji, Z. E., Gorji, A. E., et al. (2017) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2016.12.020 · Cited by 38
Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons
Yalamanchi, K. K., Monge-Palacios, M., van Oudenhoven, V. C. O., Gao, X., & Sarathy, S. M. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c02785 · Cited by 37
Distance Indices and Their Hyper-Counterparts: Intercorrelation and Use in the Structure-Property Modeling
Trinajstić, N., Nikolić, S., Basak, S. C., et al. (2001) · SAR and QSAR in environmental research · doi:10.1080/10629360108035370 · Cited by 38
Structurally “Targeted” Quantitative Structure−Property Relationship Method for Property Prediction
Brauner, N., Stateva, R. P., Cholakov, G. S., et al. (2006) · Industrial & Engineering Chemistry Research · doi:10.1021/ie051155o · Cited by 38
Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F
Wang, X. S., Tang, H., Golbraikh, A., & Tropsha, A. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700404c · Cited by 39
A Novel RBF Neural Network Training Methodology to Predict Toxicity to Vibrio Fischeri
Melagraki, G., Afantitis, A., Sarimveis, H., Igglessi‐Markopoulou, O., & Alexandridis, A. (2006) · Molecular Diversity · doi:10.1007/s11030-005-9008-y · Cited by 38
Statistical Confidence for Variable Selection in QSAR Models via Monte Carlo Cross-Validation
Konovalov, D. A., Sim, N., Deconinck, E., et al. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700283s · Cited by 38
Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches
Basant, N., Gupta, S., & Singh, K. P. (2015) · Toxicology Research · doi:10.1039/c5tx00321k · Cited by 38
In silico screening of estrogen-like chemicals based on different nonlinear classification models
Liu, H., Papa, E., Walker, J. D., & Gramatica, P. (2007) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2007.01.003 · Cited by 38
“The list of these molecular descriptors and their meanings is provided with literature references by the DRAGON package [31]; the calculation procedure is explained in detail, with related literature references, in the Handbook of Molecular Descriptors [32].”
Method
The compressed feature matrix?a fast method for feature based substructure search
Abolmaali, S., Wegner, J. K., & Zell, A. (2003) · Journal of Molecular Modeling · doi:10.1007/s00894-003-0126-0 · Cited by 38
Neighborhood Behavior of in Silico Structural Spaces with Respect to In Vitro Activity Spaces−A Benchmark for Neighborhood Behavior Assessment of Different in Silico Similarity Metrics
Horvath, D., & Jeandenans, C. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025635r · Cited by 38
PI Index of Polyhex Nanotori
Ашрафи, А. Р., & Rezaei, F. (2007) · Cited by 38
Predictive QSAR Modeling: Methods and Applications in Drug Discovery and Chemical Risk Assessment
Golbraikh, A., Wang, X. S., Zhu, H., & Tropsha, A. (2012) · Springer eBooks · doi:10.1007/978-94-007-0711-5_37 · Cited by 37
Entropy multi-target QSAR model for prediction of antiviral drug complex networks
Prado-Prado, F., García, I., Xerardo Garcia-mera, B., & González‐Díaz, H. (2011) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2011.02.003 · Cited by 37
Traditional Chinese herbs as chemical resource library for drug discovery of anti-infective and anti-inflammatory
Ding, W.-X., Gu, J., Cao, L., Li, N., Ding, G., Wang, Z., Chen, L., Xu, X., & Xiao, W. (2014) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2014.05.066 · Cited by 37
Prediction of gastro-intestinal absorption using multivariate adaptive regression splines
Deconinck, E., Xu, Q., Put, R., et al. (2005) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2005.05.034 · Cited by 37
Irving-Williams Order in the Framework of Connectivity Index 3χv Enables Simultaneous Prediction of Stability Constants of Bivalent Transition Metal Complexes
Miličević, A., Branica, G., & Raos, N. (2011) · Molecules · doi:10.3390/molecules16021103 · Cited by 40

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Handbook of Molecular Descriptors

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cited by

Related

Metadata

Record

About these checks