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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An automated PLS search for biologically relevant QSAR descriptors
Olah, M., Bologa, C., & Oprea, T. I. (2004) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-004-4060-8 · Cited by 91
“There are currently over 3000 molecular descriptors [3] that have been used in QSAR (Quantitative Structure Activity Relationship) studies [4, 5].”
Method
BitterX: a tool for understanding bitter taste in humans
Huang, W., Shen, Q., Su, X., et al. (2016) · Scientific Reports · doi:10.1038/srep23450 · Cited by 90
“As defined in the Handbook of Molecular Descriptors 15 , 46 and 20 descriptors were selected by Feature Selection (FS) as characteristics of bitterants for the models used in bitterant verification and TAS2R recognition, respectively (Supplementary Tables S1 and S2).”
Method
Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization–Support Vector Machine QSTR models
Aalizadeh, R., von der Ohe, P. C., & Τhomaidis, Ν. S. (2017) · Environmental Science Processes & Impacts · doi:10.1039/c6em00679e · Cited by 87
Machine Learning To Predict Standard Enthalpy of Formation of Hydrocarbons
Yalamanchi, K. K., van Oudenhoven, V. C. O., Tutino, F., Monge-Palacios, M., Alshehri, A. S., Gao, X., & Sarathy, S. M. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b04771 · Cited by 91
Receptor Dependent Multidimensional QSAR for Modeling Drug - Receptor Interactions
Polański, J. (2009) · Current Medicinal Chemistry · doi:10.2174/092986709788803286 · Cited by 91
Synthesis, antibacterial activity evaluation and QSAR studies of novel dispiropyrrolidines
Karthikeyan, K., Sivakumar, P. M., Doble, M., & Perumal, P. T. (2010) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2010.04.035 · Cited by 90
Linear Indices of the “Molecular Pseudograph's Atom Adjacency Matrix”: Definition, Significance-Interpretation, and Application to QSAR Analysis of Flavone Derivatives as HIV-1 Integrase Inhibitors
Marrero‐Ponce, Y. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049950k · Cited by 90
Prediction of Aqueous Solubility and Partition Coefficient Optimized by a Genetic Algorithm Based Descriptor Selection Method
Wegner, J. K., & Zell, A. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034006u · Cited by 91
Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening
Neves, B. J., Dantas, R. F., Senger, M. R., Melo-Filho, C. C., Valente, W. C. G., de Almeida, A. C. M., Rezende-Neto, J. M., Lima, E. F. C., Paveley, R. A., Furnham, N., et al. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b02038 · Cited by 91
Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning
Gomes, G., Pollice, R., & Aspuru‐Guzik, A. (2021) · Trends in Chemistry · doi:10.1016/j.trechm.2020.12.006 · Cited by 87
The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS
Tetko, I. V., Lowe, D. M., & Williams, A. J. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0113-y · Cited by 88
Comparison between first geometric–arithmetic index and atom-bond connectivity index
Das, K. C., & Trinajstić, N. (2010) · Chemical Physics Letters · doi:10.1016/j.cplett.2010.07.097 · Cited by 88
A new semi-automated workflow for chemical data retrieval and quality checking for modeling applications
Gadaleta, D., Lombardo, A., Toma, C., & Benfenati, E. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0315-6 · Cited by 92
The edge-Wiener index of a graph
Dankelmann, P., Gutman, İ., Mukwembi, S., et al. (2008) · Discrete Mathematics · doi:10.1016/j.disc.2008.09.040 · Cited by 87
“We also consider a variant of the Wiener index, put forward in [12] and called there the Schultz index of the second kind , but for which the name Gutman index has also sometimes been used [19].”
Correlation of blood–brain penetration using structural descriptors
KATRITZKY, A. R., Kuanar, M., Slavov, S. H., Dobchev, D. A., Fara, D. C., Karelson, M. M., Acree, W. E., Solov’ev, V. P., & Varnek, A. A. (2006) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2006.03.012 · Cited by 87
Synthesis, antimicrobial evaluation and QSAR analysis of novel nalidixic acid based 1,2,4-triazole derivatives
Aggarwal, N., Kumar, R., Dureja, P., et al. (2011) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2011.06.009 · Cited by 87
“, topological indices, hydrophobic and steric parameters) which are used as independent variables in QSAR modeling [46].”
Method
Predicting Infinite-Dilution Activity Coefficients of Organic Solutes in Ionic Liquids
Eike, D., Brennecke, J. F., & Maginn, E. J. (2004) · Industrial & Engineering Chemistry Research · doi:10.1021/ie034152p · Cited by 87
Total and Local Quadratic Indices of the Molecular Pseudograph's Atom Adjacency Matrix: Applications to the Prediction of Physical Properties of Organic Compounds
Marrero‐Ponce, Y. (2003) · Molecules · doi:10.3390/80900687 · Cited by 87
An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts
Ferrari, T., & Gini, G. C. (2010) · Chemistry Central Journal · doi:10.1186/1752-153x-4-s1-s2 · Cited by 90
“…mean information content based on the distribution of distances in the graph; ALOGP , the Ghose-Crippen octanol water coefficient [9,17] and nrings , the number of rings in the molecular graph (the cyclomatic number: the smallest number of bonds which must be removed such that no…”
Background
Machine Learning, Quantum Chemistry, and Chemical Space
Ramakrishnan, R., & von Lilienfeld, O. A. (2017) · Reviews in computational chemistry · doi:10.1002/9781119356059.ch5 · Cited by 91
Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods
Feng, J., Lurati, L., Ouyang, H., Robinson, T., Wang, Y., Yuan, S., & Young, S. S. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034032s · Cited by 86
On geometric-arithmetic index
Yuan, Y., Zhou, B., & Trinajstić, N. (2009) · Journal of Mathematical Chemistry · doi:10.1007/s10910-009-9603-8 · Cited by 86
“There are numerous topological descriptors that have found some applications in theoretical chemistry, especially in QSPR/QSAR research [ 1 ].”
Background
QSAR study of antioxidant activity of wine polyphenols
Rastija, V., & Medič-Šarić, M. (2008) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2008.03.001 · Cited by 86
Distance spectral radius of trees with fixed maximum degree
Stevanović, D., & Ilić, A. (2010) · Electronic Journal of Linear Algebra · doi:10.13001/1081-3810.1366 · Cited by 86
“For more details on distance matrices and distance energy, see [6, 11, 15, 16, 19, 23].”
Background
Model-Based Design of Tailor-Made Biofuels
Dahmen, M., & Marquardt, W. (2016) · Energy & Fuels · doi:10.1021/acs.energyfuels.5b02674 · Cited by 87
Artificial neural network based predictions of cetane number for furanic biofuel additives
Kessler, T. J., Sacia, E. R., Bell, A. T., & Mack, J. H. (2017) · Fuel · doi:10.1016/j.fuel.2017.06.015 · Cited by 85
Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds
González‐Díaz, H., Gia, O. M., Uriarte, E., et al. (2003) · Journal of Molecular Modeling · doi:10.1007/s00894-003-0148-7 · Cited by 86
Chemical space navigation in lead discovery
Oprea, T. I. (2002) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(02)00329-0 · Cited by 87
Using Rule-Based Labels for Weak Supervised Learning
Goh, G. B., Siegel, C., Vishnu, A., & Hodas, N. O. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219838 · Cited by 96
“eering is a long-standing pillar in historical chemistry research that stretches back to the late 1940s [19]. Today, over 5000 molecular descriptors have been developed from ﬁrst principles knowledge [20]. Molecular descriptors are engineered chemical features, such as computable properties or sophisticated descriptio…”
Background
Computational Identification of Inhibitors of Protein-Protein Interactions
Zhong, S., Macias, A. T., & MacKerell, A. D. (2006) · Current Topics in Medicinal Chemistry · doi:10.2174/156802607779318334 · Cited by 85
“The information in SDF files may act as the starting point for the generation of 3D structures followed by for further geometry optimization [16] and are used in several database searching algorithms based on 2D molecular descriptors [237].”
BackgroundMethod
A Method of Computing the PI Index of Benzenoid Hydrocarbons Using Orthogonal Cuts
John, P. E., Khadikar, P. V., & Singh, J. (2006) · Journal of Mathematical Chemistry · doi:10.1007/s10910-006-9100-2 · Cited by 85
“Numbers reflecting certain structural features of organic molecules that are obtained from the molecular graph are usually called graph invariants or more commonly topological indices [ 1 ].”
Background
A QSAR study of radical scavenging antioxidant activity of a series of flavonoids using DFT based quantum chemical descriptors – the importance of group frontier electron density
Sarkar, A., Middya, T. R., & Jana, A. D. (2011) · Journal of Molecular Modeling · doi:10.1007/s00894-011-1274-2 · Cited by 85
The importance of outlier detection and training set selection for reliable environmental QSAR predictions
Furusjö, E., Svenson, A., Rahmberg, M., et al. (2005) · Chemosphere · doi:10.1016/j.chemosphere.2005.07.002 · Cited by 85
Elucidating Structure–Property Relationships in Aluminum Alloy Corrosion Inhibitors by Machine Learning
Galvão, T. L. P., Novell-Leruth, G., Kuznetsova, A. I., Tedim, J., & Gomes, J. R. B. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.9b09538 · Cited by 82
QSAR Modeling of SARS‐CoV M pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV‐2
Alves, V. M., Bobrowski, T., Melo-Filho, C. C., Korn, D., Auerbach, S. S., Schmitt, C., Muratov, E. N., & Tropsha, A. (2020) · Molecular Informatics · doi:10.1002/minf.202000113 · Cited by 99
A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties
Comesana, A. E., Huntington, T. T., Scown, C. D., Niemeyer, K. E., & Rapp, V. H. (2022) · Fuel · doi:10.1016/j.fuel.2022.123836 · Cited by 84
Design and synthesis of eugenol derivatives, as potent 15-lipoxygenase inhibitors
Sadeghian, H., Seyedi, S. M., Saberi, M. R., Arghiani, Z., & Riazi, M. (2007) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2007.10.016 · Cited by 84
On Variable Zagreb Indices
Miličević, A., & Nikolić, S. (2004) · Institutional Repository of the Ruđer Bošković Institute (Ruđer Bošković Institute) · Cited by 84
A QSAR for the hydroxyl radical reaction rate constant: validation, domain of application, and prediction
Öberg, T. (2005) · Atmospheric Environment · doi:10.1016/j.atmosenv.2005.01.007 · Cited by 83
“Most of these descriptors are reviewed in the recent textbook by Todeschini and Consonni (2000).”
Background
Addressing Facts and Gaps in the Phenolics Chemistry of Winery By-Products
Machado, N. F. L., & Domínguez‐Perles, R. (2017) · Molecules · doi:10.3390/molecules22020286 · Cited by 96
Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules
Pronobis, W., Tkatchenko, A., & Müller, K. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00110 · Cited by 81
Machine learning activation energies of chemical reactions
Lewis‐Atwell, T., Townsend, P. A., & Grayson, M. N. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1593 · Cited by 85
“Notably, none of the descriptors for the systems required QM calculations, and could be obtained from only the geometric information of the molecule.(176,177) The most important features to the two BIreactive models were: the number of terminal, primary LEWIS-ATWELL ET AL.”
Background
Probabilistic Neural Network Model for the In Silico Evaluation of Anti-HIV Activity and Mechanism of Action
Vilar, S., Santana, L., & Uriarte, E. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm050932j · Cited by 83
QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg–Marquardt algorithm
Jalali‐Heravi, M., Asadollahi-Baboli, M., & Shahbazikhah, P. (2007) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2007.04.014 · Cited by 83
Toxicity Assessment in the Nanoparticle Era
Matteis, V. D., & Rinaldi, R. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-72041-8_1 · Cited by 85
Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods
Wang, D. D., Ou-Yang, L., Xie, H., Zhu, M., & Yan, H. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.02.007 · Cited by 85
Insights for Human Ether-a-Go-Go-Related Gene Potassium Channel Inhibition Using Recursive Partitioning and Kohonen and Sammon Mapping Techniques
Ekins, S., Balakin, K. V., Savchuk, N., & Ivanenkov, Y. A. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm060076r · Cited by 84
Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization
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On eccentric connectivity indexPreprint
Zhou, B., & Du, Z. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1007.2235 · Cited by 82
“used for quantitative structure-property relationship (QSPR) and quantitative structureactivity relationship (QSAR) studies [1-4].”
Method
Neighborhood Behavior of in Silico Structural Spaces with Respect to in Vitro Activity Spaces−A Novel Understanding of the Molecular Similarity Principle in the Context of Multiple Receptor Binding Profiles
Horvath, D., & Jeandenans, C. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025634z · Cited by 81
Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons
Teixeira, A. L., Leal, J. P., & Falcão, A. O. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-9 · Cited by 81
Benchmarking of QSAR Models for Blood-Brain Barrier Permeation
Konovalov, D. A., Coomans, D. H., Deconinck, E., & Heyden, Y. V. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700100f · Cited by 81
Classification and regression tree analysis for molecular descriptor selection and retention prediction in chromatographic quantitative structure–retention relationship studies
Put, R., Perrin, C., Questier, F., et al. (2003) · Journal of Chromatography A · doi:10.1016/s0021-9673(03)00004-9 · Cited by 80
A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine
Fatemi, M. H., & Gharaghani, S. (2007) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2007.08.057 · Cited by 80
Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists
Büyükbingöl, E., Sisman, A., Akyıldız, M., et al. (2007) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2007.03.065 · Cited by 80
Comparison of Ligand- and Structure-Based Virtual Screening on the DUD Data Set
von Korff, M., Freyss, J., & Sander, T. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800303k · Cited by 82
A Similarity‐based Data‐fusion Approach to the Visual Characterization and Comparison of Compound Databases
Medina‐Franco, J. L., Maggiora, G. M., Giulianotti, M. A., Pinilla, C., & Houghten, R. A. (2007) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2007.00579.x · Cited by 81
“Chemical spaces are typically represented as co-ordinate-based spaces, where each co-ordinate axis is defined by some type of molecular descriptor (6), but this is not absolutely required.”
Background
On the general sum-connectivity index of trees
Du, Z., Zhou, B., & Trinajstić, N. (2010) · Applied Mathematics Letters · doi:10.1016/j.aml.2010.10.038 · Cited by 80
Markovian Backbone Negentropies: Molecular descriptors for protein research. I. Predicting protein stability in Arc repressor mutants
de Armas, R. R., González‐Díaz, H., Molina, R., & Uriarte, E. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20159 · Cited by 80
“13,14 In this context, some speciﬁc and very successful indices (for small molecules) that use the concept of Shannon’s entropy 15 from the point of view of the information theory have proven to be very effective in drug design.”
Background
3D-MEDNEs: An Alternative “In Silico” Technique for Chemical Research in Toxicology. 1. Prediction of Chemically Induced Agranulocytosis
González‐Díaz, H., Marrero‐Ponce, Y., Hernández, I., et al. (2003) · Chemical Research in Toxicology · doi:10.1021/tx0256432 · Cited by 80
Information Indices with High Discriminative Power for Graphs
Dehmer, M., Grabner, M., & Вармуза, К. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031214 · Cited by 78
Note on the comparison of the first and second normalized zagreb eccentricity indices.
Vukičević, D., & Graovac, A. (2010) · PubMed · Cited by 79
ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins
Ruiz‐Blanco, Y. B., Paz, W., Green, J. R., & Marrero‐Ponce, Y. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0586-0 · Cited by 79
“In the field of cheminformatics, millions of molecular descriptors (MDs) for small-to-mid sized compounds [5] are currently implemented in software packages such as: DRAGON, TOMOCOMD-CARDD, PADEL, CDK descriptor calculator, ADRIANA CODE, CODESSA-PRO and CERIUS [6-17].”
BackgroundMethod
Prediction of Flash Point Temperature of Pure Components Using a Quantitative Structure–Property Relationship Model
Gharagheizi, F., & Alamdari, R. F. (2007) · QSAR & Combinatorial Science · doi:10.1002/qsar.200730110 · Cited by 79
On the Randić index
Delorme, C., Favaron, O., & Rautenbach, D. (2002) · Discrete Mathematics · doi:10.1016/s0012-365x(02)00256-x · Cited by 79
The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors
KATRITZKY, A. R., Fara, D. C., Petrukhin, R., et al. (2002) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026023392922 · Cited by 79
Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks
Gupta, V. K., Khani, H., Ahmadi-Roudi, B., et al. (2010) · Talanta · doi:10.1016/j.talanta.2010.11.017 · Cited by 78
Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets
Zhou, Y., Cahya, S., Combs, S. A., Nicolaou, C. A., Wang, J., Desai, P. V., & Shen, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00671 · Cited by 82
Screening and Ranking of POPs for Global Half-Life: QSAR Approaches for Prioritization Based on Molecular Structure
Gramatica, P., & Papa, E. (2007) · Environmental Science & Technology · doi:10.1021/es061773b · Cited by 78
QSAR study of selective ligands for the thyroid hormone receptor β
Liu, H., & Gramatica, P. (2007) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2007.05.016 · Cited by 78
A General Method for Exploiting QSAR Models in Lead Optimization
Lewis, R. A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049228d · Cited by 77
TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: Evaluation of different classification model combinations using bond-based linear indices
Casañola‐Martín, G. M., Marrero‐Ponce, Y., Khan, M. T. H., Ather, A., Sultan, S., Torrens, F., & Rotondo, R. (2006) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2006.10.067 · Cited by 78
Thiazolides as Novel Antiviral Agents. 1. Inhibition of Hepatitis B Virus Replication
Stachulski, A. V., Pidathala, C., Row, E. C., Sharma, R., Berry, N. G., Iqbal, M., Bentley, J., Allman, S. A., Edwards, G., Helm, A., et al. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200153p · Cited by 79
Universal Approach for Structural Interpretation of QSAR/QSPR Models
Polishchuk, P. G., Kuz'min, V. E., Artemenko, A. G., & Muratov, E. N. (2013) · Molecular Informatics · doi:10.1002/minf.201300029 · Cited by 76
“The simplex representation of molecular structure (SiRMS) approach was used to generate fragment descriptors, and the set of whole-molecule parameters([35]) describing the molecule as a whole along with some molecular fragments (topological, constitutional, connectivity, informational, 2D autocorrelations, molecular p…”
Method
Carbonic anhydrase inhibitors: the first QSAR study on inhibition of tumor-associated isoenzyme IX with aromatic and heterocyclic sulfonamides
Jaiswal, M., Khadikar, P. V., Scozzafava, A., et al. (2004) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2004.03.099 · Cited by 77
Hemostatic polymers: the concept, state of the art and perspectives
di Lena, F. (2014) · Journal of Materials Chemistry B · doi:10.1039/c3tb21739f · Cited by 79
QSAR and Classification of Murine and Human Soluble Epoxide Hydrolase Inhibition by Urea-Like Compounds
McElroy, N. R., Jurs, P. C., Morisseau, C. H. P., et al. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm020269o · Cited by 77
Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents
Speck‐Planche, A., Kleandrova, V. V., Luan, F., & Cordeiro, M. N. D. S. (2012) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2012.04.012 · Cited by 77
“In the last 20 years, the quantity of molecular descriptors has been increased in considerable way (Todeschini and Consonni, 2000).”
Background
Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity
Marrero‐Ponce, Y., Medina, R., Torrens, F., Martinez, Y., Zaldívar, V. R., & Castro, E. A. (2005) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2005.02.015 · Cited by 77
Judging the Significance of Multiple Linear Regression Models
Livingstone, D. J., & Salt, D. W. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049111p · Cited by 77
Solution Properties and Practical Limits of Concentrated Electrolytes for Nonaqueous Redox Flow Batteries
Zhang, J., Corman, R. E., Schuh, J. K., et al. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b02009 · Cited by 76
Quantitative structure activity relationship and artificial neural network as vital tools in predicting coordination capabilities of organic compounds with metal surface: A review
Quadri, T. W., Olasunkanmi, L. O., Fayemi, O. E., Akpan, E. D., Verma, C., Sherif, E. M., Khaled, K. F., & Ebenso, E. E. (2021) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2021.214101 · Cited by 78
Classification of a Diverse Set of Tetrahymena pyriformis Toxicity Chemical Compounds from Molecular Descriptors by Statistical Learning Methods
Xue, Y. W., Li, H., Ung, C. Y., Yap, C. W., & Chen, Y. (2006) · Chemical Research in Toxicology · doi:10.1021/tx0600550 · Cited by 76
Anti-leishmanial and anti-trypanosomal activities of 1,4-dihydropyridines: In vitro evaluation and structure–activity relationship study
Reimão, J. Q., Scotti, L., & Tempone, A. G. (2010) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2010.09.015 · Cited by 76
Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods
Burden, F. R., & Winkler, D. A. (2009) · QSAR & Combinatorial Science · doi:10.1002/qsar.200810173 · Cited by 76
Chemometrics in QSAR
Todeschini, R., Consonni, V., & Gramatica, P. (2009) · Elsevier eBooks · doi:10.1016/b978-044452701-1.00007-7 · Cited by 85
Self-citation
Ligand-Based Approaches to In Silico Pharmacology
Vidal, D., García-Serna, R., & Mestres, J. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_19 · Cited by 80
A Survey of Quantitative Descriptions of Molecular Structure
Guha, R., & Willighagen, E. (2012) · Current Topics in Medicinal Chemistry · doi:10.2174/156802612804910278 · Cited by 83
Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning
Nguyen, P., Loveland, D., Kim, J. T., Karande, P., Hiszpanski, A. M., & Han, T. Y.-J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01318 · Cited by 77
Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets
Orosz, Á., Héberger, K., & Rácz, A. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.852893 · Cited by 77
“The descriptors are not to be used with their raw calculated (or measured) values; descriptor reduction should be carried out ﬁ rst, namely, constant and highly correlated descriptors are to be eliminated (Todeschini and Consonni, 2000; Rácz et al., 2019a).”
BackgroundInfluential
A QSAR for Baseline Toxicity: Validation, Domain of Application, and Prediction
Öberg, T. (2004) · Chemical Research in Toxicology · doi:10.1021/tx0498253 · Cited by 75
Unicyclic and bicyclic graphs having minimum degree distance
Tomescu, A. I. (2007) · Discrete Applied Mathematics · doi:10.1016/j.dam.2007.09.010 · Cited by 75
“They provide correlations with physical, chemical and thermodynamic parameters of chemical compounds [1–3,10,12].”
Background
Linear and Branched PEIs (Polyethylenimines) and Their Property Space
Lungu, C. N., Diudea, M. V., Putz, M. V., & Grudziński, I. P. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17040555 · Cited by 82
Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides
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Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM)
Vorberg, S., & Tetko, I. V. (2013) · Molecular Informatics · doi:10.1002/minf.201300030 · Cited by 75
“Dragon is a software package from Talete [40] which calculates 4885 molecular descriptors.”
Method
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Similarity Measure for Molecular Structure: A Brief Review
Bero, S. A., Muda, A. K., Choo, Y., Muda, N. A., & Pratama, S. F. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/892/1/012015 · Cited by 74
“There are various types of descriptors reported [8-10] that had been used in the studies of molecular similarity.”
Background
Hepatoprotection of sesquiterpenoids: A quantitative structure–activity relationship (QSAR) approach
Vinholes, J. R., Rudnitskaya, A. M., Gonçalves, P., et al. (2013) · Food Chemistry · doi:10.1016/j.foodchem.2013.09.039 · Cited by 74
ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds
González‐Díaz, H., Bonet, I., Terán, C., Clercq, E. D., Bello, R., García, M. M., Santana, L., & Uriarte, E. (2006) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2006.11.016 · Cited by 72
Zagreb indices of graphs
Das, K. C., Xu, K., & Nam, J. (2015) · Frontiers of Mathematics · doi:10.1007/s11464-015-0431-9 · Cited by 74

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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