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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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ChemGPS-NP: Tuned for Navigation in Biologically Relevant Chemical Space
Larsson, J., Gottfries, J., Mureşan, S., & Backlund, A. (2007) · Journal of Natural Products · doi:10.1021/np070002y · Cited by 212
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“Descriptors can be classified as either one-, two- or three-dimensional, depending on whether they describe bulk properties, connectivities or conformation-dependent properties, respectively.(83,103) The use of descriptors in cheminformatics has provided simple rules to evaluate druglikeness, as in the case of Lipinsk…”
Comprehensive Medicinal Chemistry III
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QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds
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“The calculation procedures for these descriptors, with related literature references, are reported in ( 47 ).”
MethodComputing chemistry on the web
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Rigorous Free Energy Simulations in Virtual Screening
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Are the Chemical Structures in Your QSAR Correct?
Young, D., Martin, T. M., Venkatapathy, R., & Harten, P. F. (2008) · QSAR & Combinatorial Science · doi:10.1002/qsar.200810084 · Cited by 202
Anion and Cation Effects on Imidazolium Salt Melting Points: A Descriptor Modelling Study
López‐Martin, I., Burello, E., Davey, P. N., Seddon, K. R., & Rothenberg, G. (2007) · ChemPhysChem · doi:10.1002/cphc.200600637 · Cited by 196
QSPR analysis of some novel neighbourhood degree-based topological descriptors
Mondal, S., Dey, A., De, N., et al. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00262-0 · Cited by 196
Ab Initio Machine Learning in Chemical Compound Space
Huang, B., & von Lilienfeld, O. A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01303 · Cited by 214
“Coordinate-free, bonding neighbors (covalently bonded atom pairs) based graphs, as well as their systematic extensions to arbitrary number of neighboring shells, have formed an important research direction in cheminformat-ics for many years [32, 174, 176, 178, 214].”
BackgroundPredicting aqueous solubility from structure
Delaney, J. S. (2005) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03365-3 · Cited by 192
Graphs with maximum connectivity index
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Chemoinformatics Approaches to Virtual Screening
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Cheminformatics analysis and learning in a data pipelining environment
Hassan, M., Brown, R. D., Varma‐O'Brien, S., & Rogers, D. O. W. (2006) · Molecular Diversity · doi:10.1007/s11030-006-9041-5 · Cited by 184
“Molecular fingerprints [2] are representations of chemical structures originally designed to assist in chemical database”
BackgroundNamed Entity Recognition and Relation Detection for Biomedical Information Extraction
Perera, N., Dehmer, M., & Emmert‐Streib, F. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00673 · Cited by 201
“A potential advantage of such a representation is that the rich information from network studies about graph energy, graph entropy, molecular descriptors, or network comparisons could be utilized (Todeschini et al., 2002; Li et al., 2012; Dehmer et al., 2016; Emmert-Streib et al., 2016).”
BackgroundExtremality of degree-based graph entropies
Cao, S., Dehmer, M., & Shi, Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.03.133 · Cited by 182
“For more results on topological indices, we refer to [40,49,50,75].”
BackgroundAlgebraic graph-assisted bidirectional transformers for molecular property prediction
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Atom–bond connectivity index of trees
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Multi-Targeted Natural Products Evaluation Based on Biological Activity Prediction with PASS
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Antiherpesvirus drugs: a promising spectrum of new drugs and drug targets
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Feature Selection Methods in QSAR Studies
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Artificial intelligence in chemistry and drug design
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MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction
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“With´several´we here envision at least 10‐20 descriptors, but with today’s molecular descriptor engines the number of descriptors easily count in hundreds or thousands [ 42 ].”
BackgroundAalizadeh, R., Alygizakis, ., Schymanski, E., Krauss, M. K. V., Schulze, T., Ibáñez, M., McEachran, A. D., Chao, A., Williams, A. J., Gago-Ferrero, P., et al. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c02348 · Cited by 162
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In vivo, in vitro and in silico methods for small molecule transfer across the BBB
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Prediction of P-Glycoprotein Substrates by a Support Vector Machine Approach
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An automated framework for QSAR model building
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“Currently the number of chemical descriptors is so large that one of the biggest problems is selecting the most adequate features for each problem [58, 59].”
BackgroundPractical guidelines for the use of gradient boosting for molecular property prediction
Boldini, D., Grisoni, F., Kühn, D., Friedrich, L., & Sieber, S. A. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00743-7 · Cited by 202
“However, categorical inputs are very rarely found in molecular descriptors [41], therefore this aspect is not of big relevance for cheminformatics applications.”
BackgroundPredicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data
Sidorov, P., Naulaerts, S., Ariey-Bonnet, J., Pasquier, E., & Ballester, P. J. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00509 · Cited by 158
“Features For the use in machine learning, the structures of compounds must be encoded as vectors of numerical features known in chemoinformatics as molecular descriptors (Todeschini and Consonni, 2000).”
MethodNovel strategies for the formulation and processing of poorly water-soluble drugs
Göke, K., Lorenz, T., Repanas, A., Schneider, F., Steiner, D., Baumann, K., Bunjes, H., Dietzel, A. H., Finke, J. H., Glasmacher, B., et al. (2017) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2017.05.008 · Cited by 161
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Graph-Theoretical Matrices in Chemistry
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Optimal assignment kernels for attributed molecular graphs
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ISIDA Property‐Labelled Fragment Descriptors
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On incidence energy of a graph
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Basic Overview of Chemoinformatics
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A review on machine learning methods for in silico toxicity prediction
Idakwo, G., Luttrell, J., Chen, M., Hong, H., Zhou, Z., Gong, P., & Zhang, C. (2018) · Journal of Environmental Science and Health Part C · doi:10.1080/10590501.2018.1537118 · Cited by 148
Mixture Toxicity of Priority Pollutants at No Observed Effect Concentrations (NOECs)
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“Several papers deal with the explanation of these structural descriptors (Todeschini et al., 1994; Todeschini and Consonni, 2000; Karelson, 2000).”
BackgroundIn vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review
Lambrinidis, G., Vallianatou, T., & Tsantili‐Kakoulidou, A. (2015) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2015.03.011 · Cited by 150
Atom-bond connectivity index of graphs
Das, K. C. (2010) · Discrete Applied Mathematics · doi:10.1016/j.dam.2010.03.006 · Cited by 143
“Among them, topological indices have a prominent place [7].”
BackgroundGraph Neural Networks for Prediction of Fuel Ignition Quality
Schweidtmann, A. M., Rittig, J. G., König, A. A., Grohe, M., Mitsos, A., & Dahmen, M. (2020) · Energy & Fuels · doi:10.1021/acs.energyfuels.0c01533 · Cited by 151
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Puratchikody, A., & Doble, M. (2006) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2006.10.025 · Cited by 138
Three New/Old Vertex-Degree-Based Topological Indices
Gutman, İ., Furtula, B., & Elphick, C. H. (2014) · Cited by 137
Santana, L., Uriarte, E., González‐Díaz, H., Zagotto, G., Soto‐Otero, R., & Méndez‐Álvarez, E. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm0509849 · Cited by 136
New public QSAR model for carcinogenicity
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“Generation and selection of descriptors Nowadays thousands of chemical descriptors such as constitutional, quantum chemical, topological, geometrical, charge related, semi-empirical, thermodynamic and others can be calculated for chemical structure [ 56 ,57].”
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“These kinds of descriptors have been proposed to evaluate the charge transfer between pairs of atoms, and therefore the global charge transfer in the molecule (Todeschini and Consonni, 2000).”
BackgroundInfluentialSimilarity Searching Using 2D Structural Fingerprints
Willett, P. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_5 · Cited by 137
von Lilienfeld, O. A. (2013) · International Journal of Quantum Chemistry · doi:10.1002/qua.24375 · Cited by 134
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“There the initial success of several simple invariants, such as the Wiener index [27] and the two Zagreb indices [11], motivated subsequent introduction of hundreds, if not thousands, of topological indices and their application to various chemically relevant problems [22].”
BackgroundExisting and Developing Approaches for QSAR Analysis of Mixtures
Muratov, E. N., Varlamova, E. V., Artemenko, A. G., Полищук, П. Г., & Kuz’min, V. Е. (2012) · Molecular Informatics · doi:10.1002/minf.201100129 · Cited by 139
Multiplicative Versions of First Zagreb Index
Eliasi, M., Iranmanesh, A., & Gutman, İ. (2012) · Cited by 129
Chen, B., Zhang, T., Bond, T., & Gan, Y. (2015) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2015.06.054 · Cited by 130
“For DBP scientists interested in the use of compositional descriptors, a series of functional group descriptor values for regulated and emerging DBPs, including carboxyl, carbonyl, nitrile, and halogens, are provided in Table 4, with more information available in the original references [59,61].”
BackgroundDeconinck, É., Hancock, T., Coomans, D. H., et al. (2005) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2005.03.008 · Cited by 129
Accelerating organic solar cell material's discovery: high-throughput screening and big data
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Calculation of Quantum-Mechanical Descriptors for QSPR at the DFT Level: Is It Necessary?
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Quantitative structure–activity relationship: promising advances in drug discovery platforms
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“Considering the huge number of descriptors [47], determining a suitable choice of MDs greatly influences the success and performance of any developed QSAR model [48-50].”
BackgroundConsonni, V., & Todeschini, R. (2009) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-1-4020-9783-6_3 · Cited by 144
Self-citationMaxima and Minima of the Hosoya Index and the Merrifield-Simmons Index
Wagner, S. G., & Gutman, İ. (2010) · Acta Applicandae Mathematicae · doi:10.1007/s10440-010-9575-5 · Cited by 125
“The molecular structure descriptor Z was soon re-named into Hosoya index or Hosoya topological index, whereas the name “topological index” is nowadays used for any of the countless graph invariants [28, 95] that are found (or are claimed) to have some chemical applicability.”
BackgroundThe BEAM-project: prediction and assessment of mixture toxicities in the aquatic environment
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% V Bur index and steric maps: from predictive catalysis to machine learning
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The importance of discerning shape in molecular pharmacology
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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
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