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General Theory for Multiple Input-Output Perturbations in Complex Molecular Systems. 1. Linear QSPR Electronegativity Models in Physical, Organic, and Medicinal Chemistry

Gonzalez-Diaz, Humberto, Arrasate, Sonia, Gomez-SanJuan, Asier, Sotomayor, Nuria, Lete, Esther, Besada-Porto, Lina, Ruso, Juan·2013·Current Topics in Medicinal Chemistry

DOI 10.2174/1568026611313140011

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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.

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Whitesides, G. M., & Grzybowski, B. A. (2002) · Science · doi:10.1126/science.1070821 · Cited by 7,550
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Jacobsen, E. N., Pfaltz, A., & 山本, 尚. (1999) · Springer eBooks · Cited by 3,786
CHARMM-GUI Membrane Builder for mixed bilayers and its application to yeast membranes.
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Authors: Gonzalez-Diaz, H., Arrasate, S., Gomez-SanJuan, A., Sotomayor, N., Lete, E., Besada-Porto, L., & Ruso, J.
ORCID: Arrasate, Sonia
Sotomayor, Nuria
Lete, Esther
Ruso, Juan
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)
DOI: 10.2174/1568026611313140011
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

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General Theory for Multiple Input-Output Perturbations in Complex Molecular Systems. 1. Linear QSPR Electronegativity Models in Physical, Organic, and Medicinal Chemistry

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