The DOI matches its Crossref record; 230 of the 231 references it cites were found in a registry or an index.
General Theory for Multiple Input-Output Perturbations in Complex Molecular Systems. 1. Linear QSPR Electronegativity Models in Physical, Organic, and Medicinal Chemistry
Abstract
In general perturbation methods starts with a known exact solution of a problem and add "small" variation terms in order to approach to a solution for a related problem without known exact solution. Perturbation theory has been widely used in almost all areas of science. Bhor's quantum model, Heisenberg's matrix mechanincs, Feyman diagrams, and Poincare's chaos model or "butterfly effect" in complex systems are examples of perturbation theories. On the other hand, the study of Quantitative Structure-Property Relationships (QSPR) in molecular complex systems is an ideal area for the application of perturbation theory. There are several problems with exact experimental solutions (new chemical reactions, physicochemical properties, drug activity and distribution, metabolic networks, etc.) in public databases like CHEMBL. However, in all these cases, we have an even larger list of related problems without known solutions. We need to know the change in all these properties after a perturbation of initial boundary conditions. It means, when we test large sets of similar, but different, compounds and/or chemical reactions under the slightly different conditions (temperature, time, solvents, enzymes, assays, protein targets, tissues, partition systems, organisms, etc.). However, to the best of our knowledge, there is no QSPR general-purpose perturbation theory to solve this problem. In this work, firstly we review general aspects and applications of both perturbation theory and QSPR models. Secondly, we formulate a general-purpose perturbation theory for multiple-boundary QSPR problems. Last, we develop three new QSPR-Perturbation theory models. The first model classify correctly >100,000 pairs of intra-molecular carbolithiations with 75-95% of Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp). The model predicts probabilities of variations in the yield and enantiomeric excess of reactions due to at least one perturbation in boundary conditions (solvent, temperature, temperature of addition, or time of reaction). The model also account for changes in chemical structure (connectivity structure and/or chirality paterns in substrate, product, electrophile agent, organolithium, and ligand of the asymmetric catalyst). The second model classifies more than 150,000 cases with 85-100% of Ac, Sn, and Sp. The data contains experimental shifts in up to 18 different pharmacological parameters determined in >3000 assays of ADMET (Absorption, Distribution, Metabolism, Elimination, and Toxicity) properties and/or interactions between 31723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model classifies more than 260,000 cases of perturbations in the self-aggregation of drugs and surfactants to form micelles with Ac, Sn, and Sp of 94-95%. The model predicts changes in 8 physicochemical and/or thermodynamics output parameters (critic micelle concentration, aggregation number, degree of ionization, surface area, enthalpy, free energy, entropy, heat capacity) of self-aggregation due to perturbations. The perturbations refers to changes in initial temperature, solvent, salt, salt concentration, solvent, and/or structure of the anion or cation of more than 150 different drugs and surfactants. QSPR-Perturbation Theory models may be useful for multi-objective optimization of organic synthesis, physicochemical properties, biological activity, metabolism, and distribution profiles towards the design of new drugs, surfactants, asymmetric ligands for catalysts, and other materials.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“More recently, Gonzalez-Diaz et al.109 developed the perturbation theory machine learning (PTML) methods that search for QSAR models capable of simultaneous prediction of many target properties under several experimental conditions.”
From QSAR without borders (2020) · cites this paper as [109]
Citations
Cites
Items 201–230 of 230
Perturbation theory without wave functions for the Zeeman effect in hydrogen
Fernández, F. M., & Morales, J. A. (1992) · Physical Review A · doi:10.1103/physreva.46.318 · Cited by 15
González‐Díaz, H. (2008) · Current Topics in Medicinal Chemistry · doi:10.2174/156802608786786615 · Cited by 14
Perron, Q., & Alexakis, A. (2007) · Tetrahedron Asymmetry · doi:10.1016/j.tetasy.2007.10.018 · Cited by 14
Saı́z-Urra, L., Pérez, A. J., Cruz‐Monteagudo, M., Pinedo-Rivilla, C., Aleu, J., Hernández‐Galán, R., & Collado, I. G. (2009) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf900375x · Cited by 14
Modification of the Wiener index 4
Feng, Y., Wang, Z., & Huang, Y. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20016 · Cited by 14
Desirability-Based Multi-Objective QSAR in Drug Discovery
Cruz‐Monteagudo, M., Cordeiro, M. D. S., Tejera, E., Domínguez, E. R., & Borges, F. (2012) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/138955712802762329 · Cited by 14
Huang, J. G., Yu, G., Yang, X., & Zhang, Z. (2004) · PubMed · Cited by 13
Rayleigh-Schrödinger perturbation theory for the anharmonic oscillator
Patnaik, P. K. (1987) · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields · doi:10.1103/physrevd.35.1234 · Cited by 13
Lipophilicity of Aminopyridazinone Regioisomers
Anwair, M. A. S., Károlyházy, L., Szabó, D. R., Balogh, B., Kövesdi, I., Harmat, V., Krenyácz, J., Gellért, Á., Takács‐Novák, K., & Mátyus, P. (2003) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf0343938 · Cited by 13
Kim, K. H., Gaisina, I. N., Gallier, F., Holzle, D. L., Blond, S. Y., Mesecar, A. D., & Kozikowski, A. P. (2009) · Journal of Molecular Modeling · doi:10.1007/s00894-009-0498-x · Cited by 13
González‐Díaz, H., Cabrera‐Pérez, M. Á., Agüero-Chapin, G., Cruz‐Monteagudo, M., Cancio, N. C., del Río, M. A., & Uriarte, E. (2008) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2008.07.006 · Cited by 13
On the Self-Assembly of a Highly Selective Benzothiazole-Based TIM Inhibitor in Aqueous Solution
Hassan, N., Gárate, M. P., Sandoval, T. E., Espinoza, L., Piñeiro, Á., & Ruso, J. M. (2010) · Langmuir · doi:10.1021/la102916x · Cited by 13
Self-citationNew atom-type-based AI topological indices: Application to QSPR studies of aldehydes and ketones
Ren, B. (2003) · Journal of Computer-Aided Molecular Design · doi:10.1023/b:jcam.0000005764.26206.74 · Cited by 12
García‐Calvo, O., Coya, E., Lage, S., Coldham, I., Sotomayor, N., & Lete, E. (2012) · European Journal of Organic Chemistry · doi:10.1002/ejoc.201200994 · Cited by 12
Self-citationBouzarth, E. L., Brooks, A., Camassa, R., Jing, H., Leiterman, T. J., McLaughlin, R. M., Superfine, R., Toledo, J., & Vicci, L. (2007) · Physical Review E · doi:10.1103/physreve.76.016313 · Cited by 12
Blanco, E., Verdes, P. V., Ruso, J. M., Prieto, G., & Sarmiento, F. (2008) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2008.10.012 · Cited by 11
Self-citationtRNA structure from a graph and quantum theoretical perspective
Galindo, J. F., Bermúdez-Santana, C. I., & Daza, É. E. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2005.10.017 · Cited by 11
Pérez‐Montoto, L. G., Prado-Prado, F., Ubeira, F. M., & González‐Díaz, H. (2009) · Current Proteomics · doi:10.2174/157016409789973761 · Cited by 11
A Stoichiometric Approach to Quantitative Structure−Property Relationships (QSPR)
Fishtik, I., & Datta, R. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0340310 · Cited by 11
Explicitly correlated atomic orbital basis second order Møller–Plesset theory
Hollman, D. S., Wilke, J. J., & Schaefer, H. F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4790582 · Cited by 10
On the Electronic Structure of Cocaine and its Metabolites
Rincón, D. A., Cordeiro, M. N. D. S., & Mosquera, R. A. (2009) · The Journal of Physical Chemistry A · doi:10.1021/jp9056048 · Cited by 10
Liao, Y., Wang, Z., Chen, J.-W., Han, S.-K., Wang, L., Lu, G., & Zhao, T. (1996) · Bulletin of Environmental Contamination and Toxicology · doi:10.1007/s001289900104 · Cited by 9
Tenorio-Borroto, E., Peñuelas-Rívas, C. G., Vázquez-Chagoyán, J. C., Prado-Prado, F., Xerardo Garcia-mera, B., & González‐Díaz, H. (2012) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026611209061815 · Cited by 8
González‐Díaz, H. (2012) · Current Topics in Medicinal Chemistry · doi:10.2174/156802612800166738 · Cited by 8
Intramolecular Carbolithiation Reactions
Fañanás, F. J., & Sanz, R. (2009) · Patai's chemistry of functional groups · doi:10.1002/9780470682531.pat0341 · Cited by 6
QSPR-based prediction of adsorption of halogenated aromatics on yellow-brown soil
Wei, D., Wu, C. D., Wang, L. S., & Hu, H. (2003) · SAR and QSAR in environmental research · doi:10.1080/10629360310000101773 · Cited by 5
The 4-arylaminocoumarin derivatives log P values calculated according to Rekker's method
Završnik, D., Špirtović, S., & Muratović, S. (2003) · Bosnian Journal of Basic Medical Sciences · doi:10.17305/bjbms.2003.3491 · Cited by 4
ChemInform Abstract: Carbolithiation of Carbon—Carbon Multiple Bonds
Lete, E., & Sotomayor, N. (2012) · ChemInform · doi:10.1002/chin.201236237 · Cited by 3
Self-citationBailey, W. F., & Fair, J. D. (2013) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.9.59 · Cited by 2
Persona, M., Kutarov, V. V., Kats, B. M., Persona, A., & Marczewska, B. (2007) · PubMed · Cited by 1
Cited by
Sorted by citations
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
Zahrt, A. F., Athavale, S. V., & Denmark, S. E. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00425 · Cited by 212
Halder, A. K., & Cordeiro, M. N. D. S. (2019) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.9b01306 · Cited by 205
Kleandrova, V. V., Luan, F., González‐Díaz, H., Ruso, J. M., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2014) · Environmental Science & Technology · doi:10.1021/es503861x · Cited by 145
Self-citationLuan, F., Kleandrova, V. V., González‐Díaz, H., Ruso, J. M., Melo, A., Speck‐Planche, A., & Cordeiro, M. N. D. S. (2014) · Nanoscale · doi:10.1039/c4nr01285b · Cited by 145
Self-citation
Metadata
- Authors
- Gonzalez-Diaz, H., Arrasate, S., Gomez-SanJuan, A., Sotomayor, N., Lete, E., Besada-Porto, L., & Ruso, J.
- Venue
- Current Topics in Medicinal Chemistry
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2013-08-01
- Volume
- 13
- Issue
- 14
- Pages
- 1713-1741
- ISSN
- 1568-0266 (ISSN-L)
- OpenAlex
- W2137226045
- Cited by
- 101
- References
- 231
- Citations by year
- 2025: 6 · 2024: 5 · 2022: 7 · 2021: 8 · 2020: 17 · 2019: 14 · 2018: 7 · 2017: 10 · 2016: 12 · 2015: 9 · 2014: 6
- FWCI
- 9.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Topics in Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Mathematics › Discrete Mathematics and Combinatorics › Graph theory and applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- QSPR, perturbation theory, electronegativity, carbolithiation, organolithium reactions, asymmetric catalysis, enantiomeric excess, reaction yield prediction, molecular descriptors, drug activity prediction, metabolic networks, drug repurposing
- MeSH terms
- Chemistry, Pharmaceutical, Pharmaceutical Preparations, Models, Molecular, Proteins, Quantum Theory, Quantitative Structure-Activity Relationship
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