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
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
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 · see citing works
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
Designing antimicrobial peptides: form follows function
Fjell, C. D., Hiss, J. A., Hancock, R. E. W., & Schneider, G. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3591 · Cited by 2,041 · see citing works
“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 2,042 · see citing works
“…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 1,894 · see citing works
“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 · see citing works
“35 What we present here is a step towards creating such a rich library of attributes for inorganic materials.”
CAESAR models for developmental toxicity
Cassano, A., Manganaro, A., Martin, T. M., Young, D., Piclin, N., Pintore, M., Bigoni, D., & Benfenati, E. (2010) · Chemistry Central Journal · doi:10.1186/1752-153x-4-s1-s4 · Cited by 1,589 · see citing works
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 · see citing works
“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 · see citing works
Towards the online computer-aided design of catalytic pockets
Falivene, L., Cao, Z., Petta, A., Serra, L., Poater, A., Oliva, R., Scarano, V., & Cavallo, L. (2019) · Nature Chemistry · doi:10.1038/s41557-019-0319-5 · Cited by 1,290 · see citing works
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works
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 916 · see citing works
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 · see citing works
Degree-Based Topological Indices
Gutman, İ. (2013) · Croatica Chemica Acta · doi:10.5562/cca2294 · Cited by 867 · see citing works
Fourches, D., Muratov, E., & Tropsha, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100176x · Cited by 832 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
“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 692 · see citing works
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 647 · see citing works
“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 638 · see citing works
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 · see citing works
“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).”
BackgroundMannhold, R., Poda, G. I., Ostermann, C., & Tetko, I. V. (2008) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.21494 · Cited by 663 · see citing works
OPERA 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 635 · see citing works
Molecular similarity: a key technique in molecular informatics
Bender, A., & Glen, R. C. (2004) · Organic & Biomolecular Chemistry · doi:10.1039/b409813g · Cited by 616 · see citing works
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 640 · see citing works
“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 586 · see citing works
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 · see citing works
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 576 · see citing works
Odor Coding by a Mammalian Receptor Repertoire
Saito, H., Chi, Q., Zhuang, H., Matsunami, H., & Mainland, J. D. (2009) · Science Signaling · doi:10.1126/scisignal.2000016 · Cited by 563 · see citing works
A history of graph entropy measures
Dehmer, M., & Mowshowitz, A. (2010) · Information Sciences · doi:10.1016/j.ins.2010.08.041 · Cited by 568 · see citing works
“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 563 · see citing works
Byvatov, E., Fechner, U., Sadowski, J., & Schneider, G. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0341161 · Cited by 569 · see citing works
Lusci, A., Pollastri, G., & Baldi, P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400187y · Cited by 557 · see citing works
“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
Nikolić, S., Kovačević, G., Miličević, A., et al. (2003) · Hrčak Portal of scientific journals of Croatia (University Computing Centre) · Cited by 538 · see citing works
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Zhou, B., & Trinajstić, N. (2009) · Journal of Mathematical Chemistry · doi:10.1007/s10910-008-9515-z · Cited by 520 · see citing works
Winter, R., Montanari, F., Noé, F., & Clevert, D.-A. (2018) · Chemical Science · doi:10.1039/c8sc04175j · Cited by 499 · see citing works
DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS
Mauri, A., Consonni, V., Pavan, M., Todeschini, R., & Chemometrics, M. (2006) · BOA (University of Milano-Bicocca) · Cited by 501 · see citing works
Self-citationMolecular Shape Diversity of Combinatorial Libraries: A Prerequisite for Broad Bioactivity
Sauer, W., & Schwarz, M. K. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025599w · Cited by 500 · see citing works
Repeated double cross validation
Filzmoser, P., Liebmann, B., & Вармуза, К. (2009) · Journal of Chemometrics · doi:10.1002/cem.1225 · Cited by 483 · see citing works
On general sum-connectivity index
Zhou, B., & Trinajstić, N. (2009) · Journal of Mathematical Chemistry · doi:10.1007/s10910-009-9542-4 · Cited by 479 · see citing works
Rings in Clinical Trials and Drugs: Present and Future
Shearer, J., Castro, J. L., Lawson, A. D. G., MacCoss, M., & Taylor, R. D. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00473 · Cited by 491 · see citing works
How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468 · see citing works
“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 · see citing works
Consonni, V., Todeschini, R., & Pavan, M. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci015504a · Cited by 455 · see citing works
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 423 · see citing works
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 · see citing works
“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 407 · see citing works
Quantitative Nanostructure−Activity Relationship Modeling
Fourches, D., Pu, D., Tassa, C., Weissleder, R., Shaw, S. Y., Mumper, R. J., & Tropsha, A. (2010) · ACS Nano · doi:10.1021/nn1013484 · Cited by 388 · see citing works
Descriptors and their selection methods in QSAR analysis: paradigm for drug design
Danishuddin, M., & Khan, A. U. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.06.013 · Cited by 394 · see citing works
Protein-ligand interaction prediction: an improved chemogenomics approach
Jacob, L., & Vert, J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn409 · Cited by 382 · see citing works
“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 · see citing works
Benchmark Data Set for in Silico Prediction of Ames Mutagenicity
Hansen, K., Mika, S., Schroeter, T. S., Sutter, A. P., ter Laak, A., Steger‐Hartmann, T., Heinrich, N., & Müller, K. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900161g · Cited by 373 · see citing works
Open-source platform to benchmark fingerprints for ligand-based virtual screening
Riniker, S., & Landrum, G. A. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-26 · Cited by 363 · see citing works
Rao, H., Zhu, F., Yang, G. B., Li, Z., & Chen, Y. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr284 · Cited by 359 · see citing works
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 · see citing works
“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 · see citing works
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 302 · see citing works
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 300 · see citing works
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 297 · see citing works
Bender, A., Jenkins, J. L., Scheiber, J., Sukuru, S. C. K., Glick, M., & Davies, J. W. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800249s · Cited by 294 · see citing works
Lee, M., & Schneider, G. (2001) · Journal of Combinatorial Chemistry · doi:10.1021/cc000097l · Cited by 291 · see citing works
Chemical Informatics Functionality in R
Guha, R. (2007) · Journal of Statistical Software · doi:10.18637/jss.v018.i05 · Cited by 290 · see citing works
“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 288 · see citing works
Consonni, V., Todeschini, R., Pavan, M., & Gramatica, P. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0155053 · Cited by 286 · see citing works
Self-citationProtein Contact Networks: An Emerging Paradigm in Chemistry
Paola, L. D., Ruvo, M. D., Paci, P., Santoni, D., & Giuliani, A. (2012) · Chemical Reviews · doi:10.1021/cr3002356 · Cited by 272 · see citing works
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
Ghosh, K., Stuke, A., Todorović, M., Jørgensen, P. B., Schmidt, M. N., Vehtari, A., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201801367 · Cited by 262 · see citing works
Gramatica, P. (2020) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.20200701.oa1 · Cited by 263 · see citing works
“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 261 · see citing works
Integrating virtual screening in lead discovery
Oprea, T. I., & Matter, H. (2004) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2004.06.008 · Cited by 251 · see citing works
Toward the Development of “Nano‐QSARs”: Advances and Challenges
Puzyn, T., Leszczyńska, D., & Leszczyński, J. (2009) · Small · doi:10.1002/smll.200900179 · Cited by 247 · see citing works
Helma, C., Cramer, T., Krämer, S., & Raedt, L. D. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034254q · Cited by 247 · see citing works
Fifty years of graph matching, network alignment and network comparison
Emmert‐Streib, F., Dehmer, M., & Shi, Y. (2016) · Information Sciences · doi:10.1016/j.ins.2016.01.074 · Cited by 241 · see citing works
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Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
Varnek, A. A., & Baskin, I. I. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200409x · Cited by 245 · see citing works
Can we estimate the accuracy of ADME–Tox predictions?
Tetko, I. V., Bruneau, P., Mewes, H., Rohrer, D. C., & Poda, G. I. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.06.013 · Cited by 243 · see citing works
“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 244 · see citing works
Molecular similarity and diversity in chemoinformatics: From theory to applications
Maldonado, A. G., Doucet, J. P., Petitjean, M., & Fan, B. (2006) · Molecular Diversity · doi:10.1007/s11030-006-8697-1 · Cited by 239 · see citing works
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Molecular design of flotation collectors: A recent progress
Liu, G., Yang, X., & Zhong, H. P. (2017) · Advances in Colloid and Interface Science · doi:10.1016/j.cis.2017.05.008 · Cited by 237 · see citing works
Entropy and the Complexity of Graphs Revisited
Mowshowitz, A., & Dehmer, M. (2012) · Entropy · doi:10.3390/e14030559 · Cited by 231 · see citing works
Withnall, M., Lindelöf, E., Engkvist, O., & Chen, H. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0407-y · Cited by 232 · see citing works
Challenges and advances in computational chemistry and physics
Leszczyński, J. (2006) · Springer eBooks · Cited by 230 · see citing works
Li, F., Xie, Q. L., Li, X., Li, N., Chi, P., Chen, J., Wang, Z., & Hao, C. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0901457 · Cited by 228 · see citing works
“IA was calculated by DRAGON software (Todeschini and Consonni 2000).”
Bender, A., & Cortes-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.11.037 · Cited by 232 · see citing works
Proteomics, networks and connectivity indices
González‐Díaz, H., González‐Díaz, Y., Santana, L., Ubeira, F. M., & Uriarte, E. (2008) · PROTEOMICS · doi:10.1002/pmic.200700638 · Cited by 227 · see citing works
“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 · see citing works
“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 · see citing works
von Lilienfeld, O. A., Ramakrishnan, R., Rupp, M., & Knoll, A. M. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24912 · Cited by 218 · see citing works
Holzinger, A., Dehmer, M., & Jurišica, I. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s6-i1 · Cited by 219 · see citing works
“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
Göller, A. H., Kuhnke, L., Montanari, F., Bonin, A., Schneckener, S., ter Laak, A., Wichard, J., Lobell, M., & Hillisch, A. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.001 · Cited by 215 · see citing works
A practical overview of quantitative structure-activity relationship
Isarankura‐Na‐Ayudhya, C., Naenna, T., Nantasenamat, C., & Prachayasittikul, V. (2009) · PubMed · doi:10.17877/de290r-690 · Cited by 214 · see citing works
Quantitative self-assembly prediction yields targeted nanomedicines
Shamay, Y., Shah, J., Işık, M., Mizrachi, A., Leibold, J., Tschaharganeh, D. F., Roxbury, D., Budhathoki-Uprety, J., Nawaly, K., Sugarman, J. L., et al. (2018) · Nature Materials · doi:10.1038/s41563-017-0007-z · Cited by 216 · see citing works
The Zagreb coindices of graph operations
Ашрафи, А. Р., Došlić, T., & Hamzeh, A. (2010) · Discrete Applied Mathematics · doi:10.1016/j.dam.2010.05.017 · Cited by 212 · see citing works
“We refer the reader to the monograph [21] for a review.”
BackgroundOn structure-sensitivity of degree-based topological indices
Furtula, B., Gutman, İ., & Dehmer, M. (2013) · Applied Mathematics and Computation · doi:10.1016/j.amc.2013.03.072 · Cited by 210 · see citing works
Gramatica, P., Pilutti, P., & Papa, E. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049923u · Cited by 209 · see citing works
Miners, J. O., Smith, P. A., Sorich, M. J., McKinnon, R. A., & Mackenzie, P. I. (2004) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev.pharmtox.44.101802.121546 · Cited by 210 · see citing works
- 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 › Geometry and Topology › Graph theory and applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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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