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Handbook of Molecular Descriptors
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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Extended-Connectivity Fingerprints
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PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints
Yap, C. W. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21707 · Cited by 2,979
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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Designing antimicrobial peptides: form follows function
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“Commercial software packages are available that offer hundreds of descriptors of the molecular nature of mol ecules, and are often customized specifically to the type of molecule (for example, they can be specific for small compounds versus peptides and proteins) 142 .”
MethodBest Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,107
“…description of major tenets of QSAR modeling is beyond the scope of this paper; the overview of many popular QSAR modeling techniques including statistical and datamining techniques as well as approaches to descriptor calculations could be found in many reviews and monographs, e.g., [4,5] ).”
MethodADMET in silico modelling: towards prediction paradise?
Waterbeemd, H. V. D., & Gifford, E. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1032 · Cited by 1,924
“A wide variety of descriptors for use in QSAR studies have been developed over the last 40 year”
MethodA general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831
“35 What we present here is a step towards creating such a rich library of attributes for inorganic materials.”
CAESAR models for developmental toxicity
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Virtual Computational Chemistry Laboratory – Design and Description
Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D. J., Ertl, P., Palyulin, V. A., Radchenko, E. V., Zefirov, N. S., Makarenko, A. S., et al. (2005) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-005-8694-y · Cited by 1,504
“DRA-GON [ 22 ] provides more than 1600 molecular descriptors that are divided into 20 logical blocks.”
MethodSelf-citationEriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., & Gramatica, P. (2003) · Environmental Health Perspectives · doi:10.1289/ehp.5758 · Cited by 1,325
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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
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y-Randomization and Its Variants in QSPR/QSAR
Rücker, C., Rücker, G., & Meringer, M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700157b · Cited by 978
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
Degree-Based Topological Indices
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Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815
“2,3 In this context, one of our goals is to shed light into the structure and properties of CCS in terms of molecular atomization energies that is an essential molecular property measuring the stability of a molecule with respect to its constituent atoms.”
BackgroundVukičević, D., & Furtula, B. (2009) · Journal of Mathematical Chemistry · doi:10.1007/s10910-009-9520-x · Cited by 795
“Among them, topological indices have a prominent place [ 1 ].”
BackgroundLämmerhofer, M. (2009) · Journal of Chromatography A · doi:10.1016/j.chroma.2009.10.022 · Cited by 721
In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
Ekins, S., Mestres, J., & Testa, B. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707305 · Cited by 721
“The number and type of molecular descriptors is large and varied (Karelson, 2000; Todeschini and Consonni, 2000) and thus procedures to select those that are most relevant for modelling the biological effect of interest are extremely important (Walters and Goldman, 2005).”
BackgroundChemical Basis of Interactions Between Engineered Nanoparticles and Biological Systems
Mu, Q., Jiang, G., Chen, L., Zhou, H., Fourches, D., Tropsha, A., & Yan, B. (2014) · Chemical Reviews · doi:10.1021/cr400295a · Cited by 704
Hachmann, J., Olivares‐Amaya, R., Atahan-Evrenk, Ş., Amador‐Bedolla, C., Sánchez‐Carrera, R. S., Gold‐Parker, A., Vogt-Maranto, L., Brockway, A. M., & Aspuru‐Guzik, A. (2011) · The Journal of Physical Chemistry Letters · doi:10.1021/jz200866s · Cited by 668
“While the main objective of CEP is the first-principles characterization of OPV candidates, we also explore the use of cheminformatics descriptors [91, 92] and ideas from machine learning [93, 94], pattern recognition [95, 96], and drug discovery [54, 56, 64] to rapidly gauge their quality.”
BackgroundUniversal fragment descriptors for predicting properties of inorganic crystals
Isayev, O., Oses, C., Toher, C., Gossett, E., Stefano Curtarolo, A., & Tropsha, A. (2017) · Nature Communications · doi:10.1038/ncomms15679 · Cited by 649
Mannhold, R., Poda, G. I., Ostermann, C., & Tetko, I. V. (2008) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21494 · Cited by 663
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651
“41 The basic idea, typical also for other cheminfor-matics descriptors 42 (e.g. the signature descriptor 43,44 ) is to represent a molecule as the set of subgraphs up to a fixed diameter (here we use ECFP4, which is a max diameter of 4 bonds).”
BackgroundOPERA models for predicting physicochemical properties and environmental fate endpoints
Mansouri, K., Grulke, C., Judson, R., & Williams, A. J. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0263-1 · Cited by 650
Molecular similarity: a key technique in molecular informatics
Bender, A., & Glen, R. C. (2004) · Organic & Biomolecular Chemistry · doi:10.1039/b409813g · Cited by 631
In silico methods and tools for drug discovery
Shaker, B., Ahmad, S., Lee, J., Jung, C., & Na, D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104851 · Cited by 669
“Lavecchia reviewed various machine learning models designed to perform ligand searching using molecular descriptors and fingerprints representing the physicochemical properties of a chemical compound [51 – 56].”
MethodComments on the Definition of the Q 2 Parameter for QSAR Validation
Consonni, V., Ballabio, D., & Todeschini, R. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900115y · Cited by 587
Self-citationCouling, D. J., Bernot, R. J., Docherty, K. M., Dixon, J. K., & Maginn, E. J. (2005) · Green Chemistry · doi:10.1039/b511333d · Cited by 577
KATRITZKY, A. R., Kuanar, M., Slavov, S. H., Hall, C. D., Karelson, M. M., Kahn, I., & Dobchev, D. A. (2010) · Chemical Reviews · doi:10.1021/cr900238d · Cited by 582
Odor Coding by a Mammalian Receptor Repertoire
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“As in applications in mathematical chemistry [11,35,85], graph entropy measures were found to be the best measures to distinguish non-isomorphic process graphs uniquely [16].”
BackgroundInfluentialQuantitative Structure–Property Relationship Modeling of Diverse Materials Properties
Le, T. C., Epa, V. C., Burden, F. R., & Winkler, D. A. (2012) · Chemical Reviews · doi:10.1021/cr200066h · Cited by 570
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“24 An example is the prediction method combining logP, first order valency connectivity indices ( 1 χ V ), delta chi ( Δ 2 χ ), and information content ( 2 IC) by Louis et al. 25 Although nowadays many other descriptors have been incorporated into the prediction tools, no model seems to be able to…”
MethodThe Zagreb Indices 30 Years After
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DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS
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Sauer, W., & Schwarz, M. K. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025599w · Cited by 500
Repeated double cross validation
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On general sum-connectivity index
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“Thousands of molecular descriptors are now available 30 , and many of them are very difficult, if not impossible, of clear physico-chemical interpretation 31 .”
BackgroundQSRR: Quantitative Structure-(Chromatographic) Retention Relationships
Kaliszan, R. (2007) · Chemical Reviews · doi:10.1021/cr068412z · Cited by 461
Consonni, V., Todeschini, R., & Pavan, M. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci015504a · Cited by 455
Self-citationTetko, I. V., Sushko, I., Pandey, A. K., Zhu, H., Tropsha, A., Papa, E., Öberg, T., Todeschini, R., Fourches, D., & Varnek, A. A. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800151m · Cited by 430
Self-citationPredictive and mechanistic multivariate linear regression models for reaction development
Santiago, C. B., Guo, J.-Y., & Sigman, M. S. (2018) · Chemical Science · doi:10.1039/c7sc04679k · Cited by 403
“28 Therefore, useful mechanistic information can be gathered from well-validated mathematical models.”
BackgroundChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation
Dong, J. N., Cao, D., Miao, H., Shao, L., Deng, B., Yun, Y., Wang, N., Lü, A., Zeng, W., & Chen, A. F. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0109-z · Cited by 415
Quantitative Nanostructure−Activity Relationship Modeling
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Protein-ligand interaction prediction: an improved chemogenomics approach
Jacob, L., & Vert, J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn409 · Cited by 383
“For example, simple inner products between vectors of classical molecular descriptors have been widely investigated, including physicochemical properties of molecules or 2D and 3D fingerprints (Azencott et al., 2007; Todeschini and Consonni, 2002).”
BackgroundInfluentialMachine learning of molecular electronic properties in chemical compound space
Montavon, G., Rupp, M., Gobre, V. V., Vázquez‐Mayagoitia, Á., Hansen, K., Tkatchenko, A., Müller, K., & von Lilienfeld, O. A. (2013) · MPG.PuRe (Max Planck Society) · doi:10.3929/ethz-b-000071765 · Cited by 414
Benchmark Data Set for in Silico Prediction of Ames Mutagenicity
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Open-source platform to benchmark fingerprints for ligand-based virtual screening
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Rao, H., Zhu, F., Yang, G. B., Li, Z., & Chen, Y. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr284 · Cited by 359
Wiener Index of Hexagonal Systems
Dobrynin, A. A., Gutman, İ., Klavžar, S., et al. (2002) · Acta Applicandae Mathematicae · doi:10.1023/a:1016290123303 · Cited by 326
“The molecular topological index has found interesting chemical applications (for details, see [117]).”
BackgroundGonzález‐Díaz, H., Vilar, S., Santana, L., & Uriarte, E. (2007) · Current Topics in Medicinal Chemistry · doi:10.2174/156802607780906771 · Cited by 306
Some New Trends in Chemical Graph Theory
García‐Domenech, R., Gálvez, J., de Julián‐Ortiz, J. V., & Pogliani, L. (2008) · Chemical Reviews · doi:10.1021/cr0780006 · Cited by 308
Distance spectra of graphs: A survey
Aouchiche, M., & Hansen, P. (2014) · Linear Algebra and its Applications · doi:10.1016/j.laa.2014.06.010 · Cited by 301
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
Gastegger, M., Schwiedrzik, L., Bittermann, M. R., Berzsenyi, F., & Marquetand, P. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019667 · Cited by 309
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Chemical Informatics Functionality in R
Guha, R. (2007) · Journal of Statistical Software · doi:10.18637/jss.v018.i05 · Cited by 304
“Many types of descriptors are available and a good overview can be found in Todeschini and Consonni (2002).”
BackgroundCombinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
Zhu, H., Tropsha, A., Fourches, D., Varnek, A. A., Papa, E., Gramatica, P., Öberg, T., Dao, P., Cherkasov, A. R., & Tetko, I. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700443v · Cited by 291
Consonni, V., Todeschini, R., Pavan, M., & Gramatica, P. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0155053 · Cited by 286
Self-citationProtein Contact Networks: An Emerging Paradigm in Chemistry
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Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
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Gramatica, P. (2020) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.20200701.oa1 · Cited by 266
“A comprehensive analysis of thousands of molecular descriptors was done by Todeschini & Consonni in their Handbooks (Todeschini & Consonni, 2000, 2008).”
MethodStatistical external validation and consensus modeling: A QSPR case study for Koc prediction
Gramatica, P., Giani, E., & Papa, E. (2006) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2006.06.005 · Cited by 260
Integrating virtual screening in lead discovery
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Can we estimate the accuracy of ADME–Tox predictions?
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“The “missing fragments” approach is not directly applicable to methods that rely on descriptors determined for molecules as a whole, e.g. topological descriptors [27, 28 ].”
MethodRecent Advances in QSAR Studies
Puzyn, T., Leszczyński, J., & Cronin, M. (2009) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-1-4020-9783-6 · Cited by 245
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Challenges and advances in computational chemistry and physics
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“IA was calculated by DRAGON software (Todeschini and Consonni 2000).”
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Proteomics, networks and connectivity indices
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“As a consequence, fresh interest has recently been shown in the development and application of new CIs and TPGIs with the intention of extending the potential applications of QSAR approaches [18, 34, 37, 42, 43].”
BackgroundAdvancing risk assessment of engineered nanomaterials: Application of computational approaches
Gajewicz, A., Rasulev, B., Dinadayalane, T. C., Urbaszek, P., Puzyn, T., Leszczyńska, D., & Leszczyński, J. (2012) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2012.05.014 · Cited by 226
“the atom-centered fragments, describing H (hydrogen) attached to C(sp(3)) with 1X (heteroatom) attached to the next C [223].”
BackgroundMethodInfluentialEvaluation and Discovery of Novel Synthetic Chalcone Derivatives as Anti-Inflammatory Agents
Wu, J., Li, J., Cai, Y., et al. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200946h · Cited by 223
von Lilienfeld, O. A., Ramakrishnan, R., Rupp, M., & Knoll, A. M. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24912 · Cited by 225
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“While the relevant literature of the last decades has portrayed the definition of infinitely many network measures and methods as a relatively simply undertaking; overall, understanding this complex mathematical apparatus has turned out to be very complicated [51,52].”
BackgroundBayer’s in silico ADMET platform: a journey of machine learning over the past two decades
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A practical overview of quantitative structure-activity relationship
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Quantitative self-assembly prediction yields targeted nanomedicines
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The Zagreb coindices of graph operations
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“We refer the reader to the monograph [21] for a review.”
BackgroundOn structure-sensitivity of degree-based topological indices
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Gramatica, P., Pilutti, P., & Papa, E. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049923u · Cited by 208
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Metadata
- Authors
- Todeschini, R., & Consonni, V.
- Venue
- Methods and Principles in Medicinal Chemistry
- Publisher
- Wiley
- Published
- 2000-09-22
- ISSN
- 1432-4636 (ISSN-L)
- 1865-0562
- 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
- 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 ›
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