Chemical Reactivity of Molecular Systems in Media Organized at the Molecular Level

A qualitative analysis of the main polarization effects in molecular systems is presented. Both the single and multiple site reactivity cases are discussed and illustrative applications are given for selected molecular systems: acrylonitrile, propene, formamide, and the trans-nitrous acid. The polarization effects are expected to determine the directional specificity of an early approach (before the substantial charge transfer and the intramolecular rearrangements) of both reactants in a chemical reaction. The preferred direction of such an approach is therefore postulated as that generating the minimum energy change of reactants in the Field of their respective reaction partners, including the stabilization due to the relaxation of their densities in a changed environment. The example of the electrophilic substitution in the five membered heterocycles is examined. It demonstrates the importance of the density polarization effects for explaining the observed α-preference in these reactions for furan, pyrrole and N-methyl pyrrole. Next we discuss the relation between the density relaxation and the internal stability of molecular systems. Finally, we briefly comment on the Atoms-in-a-Molecule description.

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Nucleic acids: function and potential for abiogenesis

Quarterly Reviews of Biophysics ◽

10.1017/s0033583517000038 ◽

2017 ◽

Vol 50 ◽

Cited By ~ 32

Author(s):

Falk Wachowius ◽

James Attwater ◽

Philipp Holliger

Keyword(s):

Nucleic Acids ◽

Prebiotic Chemistry ◽

Molecular Level ◽

Rna World ◽

Information Storage ◽

Molecular Systems ◽

Rna Molecules ◽

Functional Potential ◽

Self Replication ◽

Uniform Composition

AbstractThe emergence of functional cooperation between the three main classes of biomolecules – nucleic acids, peptides and lipids – defines life at the molecular level. However, how such mutually interdependent molecular systems emerged from prebiotic chemistry remains a mystery. A key hypothesis, formulated by Crick, Orgel and Woese over 40 year ago, posits that early life must have been simpler. Specifically, it proposed that an early primordial biology lacked proteins and DNA but instead relied on RNA as the key biopolymer responsible not just for genetic information storage and propagation, but also for catalysis, i.e. metabolism. Indeed, there is compelling evidence for such an ‘RNA world’, notably in the structure of the ribosome as a likely molecular fossil from that time. Nevertheless, one might justifiably ask whether RNA alone would be up to the task. From a purely chemical perspective, RNA is a molecule of rather uniform composition with all four bases comprising organic heterocycles of similar size and comparable polarity and pKa values. Thus, RNA molecules cover a much narrower range of steric, electronic and physicochemical properties than, e.g. the 20 amino acid side-chains of proteins. Herein we will examine the functional potential of RNA (and other nucleic acids) with respect to self-replication, catalysis and assembly into simple protocellular entities.

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Virtual Screening of Marine Natural Compounds by Means of Chemoinformatics and CDFT-Based Computational Peptidology

10.20944/preprints202008.0690.v1 ◽

2020 ◽

Author(s):

Norma Flores-Holguín ◽

Juan Frau ◽

Daniel Glossman-Mitnik

Keyword(s):

Virtual Screening ◽

Density Functional ◽

Computational Study ◽

Chemical Reactivity ◽

Conceptual Density Functional Theory ◽

Qsar Studies ◽

Molecular Systems ◽

Biological Targets ◽

Global And Local

This work presents the results of a computational study of the chemical reactivity and bioactivity properties of the members of the Theopapuamides A-D family of marine peptides by making use of our own proposed methodology named Computational Peptidology (CP) that has been successfully considered in previous studies of this kind of molecular systems. CP allowed for the determination of the global and local descriptors that come from Conceptual Density Functional Theory (CDFT) that can give an idea of the chemical reactivity properties of the marine natural products under study which are already known to be related to their bioactivity. At the same time, the validity of the procedure based on the adoption of the KID (Koopmans in DFT) technique as well as the MN12SX/Def2TZVP/H2O model chemistry has been successfully verified. Together with several Chemoinformatic tools that can be used for the improvement of process of Virtual Screening, some additional properties of these marine peptides were identified related to their ability to behave as useful drugs. With the further object of analyzing their bioactivity some parameters of usefulness for future QSAR studies, their predicted biological targets and the the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) parameters related to the Theopapuamides A-D pharmacokinetics are also reported.

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A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

10.26434/chemrxiv.13856123 ◽

2021 ◽

Author(s):

Tom Young ◽

Tristan Johnston-Wood ◽

Volker Deringer ◽

Fernanda Duarte

Keyword(s):

Active Learning ◽

Large Scale ◽

Learning Strategy ◽

Gaussian Approximation ◽

Force Fields ◽

Molecular System ◽

Chemical Reactivity ◽

Bulk Water ◽

Interatomic Potentials ◽

Molecular Systems

Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-level quantum-mechanical data. However, machine-learned force fields generally require considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models for diverse chemical systems can be developed in an autonomous way, requiring only hundreds to a few thousand energy and gradient evaluations on the reference potential-energy surface. Our approach relies on a decomposition of the condensed-phase molecular system into intra- and inter-molecular terms, and on the definition of a prospective error metric to quantify accuracy. We demonstrate applications to a range of molecular systems: from bulk water, organic solvents, and a solvated ion onwards to the description of chemical reactivity, including, a bifurcating Diels–Alder reaction in the gas phase and non-equilibrium dynamics (SN2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for complex and/or reactive molecular systems.

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MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666191226092431 ◽

2020 ◽

Vol 20 (4) ◽

pp. 305-317 ◽

Cited By ~ 1

Author(s):

Paula Carracedo-Reboredo ◽

Ramiro Corona ◽

Mikel Martinez-Nunes ◽

Carlos Fernandez-Lozano ◽

Georgia Tsiliki ◽

...

Keyword(s):

Markov Chain ◽

Organic Synthesis ◽

Medicinal Chemistry ◽

Molecular Descriptors ◽

Chemical Reactivity ◽

Single Molecules ◽

Enantiomeric Excess ◽

Molecular Systems ◽

Specificity And Sensitivity ◽

Metabolic Reactions

Aim: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). Background: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). Objective: Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. Methods: We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. Results: We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. Conclusion: The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

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Activation of Corn Starch and Effect of the Activation on its Reactivity

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.549.183 ◽

2012 ◽

Vol 549 ◽

pp. 183-187 ◽

Cited By ~ 1

Author(s):

Yan Wang ◽

Jia Ying Xin ◽

Tie Liu ◽

Kai Lin ◽

Chao Yue Zhang ◽

...

Keyword(s):

Particle Size ◽

Water Solubility ◽

Corn Starch ◽

Cold Water ◽

Molecular Level ◽

Chemical Reactivity ◽

Level Structure ◽

Average Particle Size ◽

Average Particle ◽

Structure And Morphology

Native corn starch (NS) was activated by treatment with NaOH /Urea /H2O solution at low temperature to improve its chemical reactivity. Effects of the activation on the molecular level structure and morphology of the corn starch were investigated by mean of SEM. It was found that the average particle size of activated corn starch (AS) decreased to nanometer level, smaller than those of NS. The cold water solubility (CWS) has been increased from 0.45% to 96.4%. Effects of the activation on reactivity of the corn starches were investigated by analyzing the influences of the activation on degrees of substitutions (DS) of the esterifications. The DS of AS was higher than that of NS, from 0 to 0.1578, which indicated that NaOH/urea activation enhanced the chemical reaction activity of corn starch.

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ChemInform Abstract: Chemical Reactivity of the [Nb2S11]4- Anion: Selective Substitution of Sulfur by Selenium on a Molecular Level.

ChemInform ◽

10.1002/chin.200036003 ◽

2010 ◽

Vol 31 (36) ◽

pp. no-no

Author(s):

Oliver Krause ◽

Peter Duerichen ◽

Christian Naether ◽

Wolfgang Bensch

Keyword(s):

Molecular Level ◽

Chemical Reactivity ◽

Selective Substitution

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In Silico Pharmacokinetics, ADMET Study and Conceptual DFT Analysis of Two Plant Cyclopeptides Isolated From Rosaceae as a Computational Peptidology Approach

Frontiers in Chemistry ◽

10.3389/fchem.2021.708364 ◽

2021 ◽

Vol 9 ◽

Author(s):

Norma Flores-Holguín ◽

Juan Frau ◽

Daniel Glossman-Mitnik

Keyword(s):

Density Functional Theory ◽

Density Functional ◽

Chemical Reactivity ◽

Conceptual Density Functional Theory ◽

Functional Theory ◽

Local Descriptors ◽

Molecular Systems ◽

Biological Targets ◽

Global And Local

This research presents the outcomes of a computational determination of the chemical reactivity and bioactivity properties of two plant cyclopeptides isolated from Rosaceae through the consideration of Computational Peptidology (CP), a protocol employed previously in the research of similar molecular systems. CP allows the prediction of the global and local descriptors that are the integral foundations of Conceptual Density Functional Theory (CDFT) and which could help in getting in the understanding of the chemical reactivity properties of the two plant cyclopeptides under study, hoping that they could be related to their bioactivity. The methodology based on the Koopmans in DFT (KID) approach and the MN12SX/Def2TZVP/H2O model chemistry has been successfully validated. Various Chemoinformatics tools have been used to improve the process of virtual screening, thus identifying some additional properties of these two plant cyclopeptides connected to their ability to behave as potentially useful drugs. With the further objective of analyzing their bioactivity, the CP protocol is complemented with the estimation of some useful parameters related to pharmacokinetics, their predicted biological targets, and the Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) parameters related to the bioavailability of the two plant cyclopeptides under study are also reported.

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A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

10.26434/chemrxiv.13856123.v1 ◽

2021 ◽

Author(s):

Tom Young ◽

Tristan Johnston-Wood ◽

Volker Deringer ◽

Fernanda Duarte

Keyword(s):

Active Learning ◽

Large Scale ◽

Learning Strategy ◽

Gaussian Approximation ◽

Force Fields ◽

Molecular System ◽

Chemical Reactivity ◽

Bulk Water ◽

Interatomic Potentials ◽

Molecular Systems

Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-level quantum-mechanical data. However, machine-learned force fields generally require considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models for diverse chemical systems can be developed in an autonomous way, requiring only hundreds to a few thousand energy and gradient evaluations on the reference potential-energy surface. Our approach relies on a decomposition of the condensed-phase molecular system into intra- and inter-molecular terms, and on the definition of a prospective error metric to quantify accuracy. We demonstrate applications to a range of molecular systems: from bulk water, organic solvents, and a solvated ion onwards to the description of chemical reactivity, including, a bifurcating Diels–Alder reaction in the gas phase and non-equilibrium dynamics (SN2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for complex and/or reactive molecular systems.

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Conceptual DFT as a Novel Chemoinformatics Tool for Studying the Chemical Reactivity Properties of the Amatoxin Family of Fungal Peptides

Open Chemistry ◽

10.1515/chem-2019-0129 ◽

2019 ◽

Vol 17 (1) ◽

pp. 1133-1139 ◽

Cited By ~ 1

Author(s):

Norma Flores-Holguín ◽

Juan Frau ◽

Daniel Glossman-Mitnik

Keyword(s):

Density Functional ◽

Chemical Reactivity ◽

Active Regions ◽

Chemical Hardness ◽

Conceptual Density Functional Theory ◽

Fukui Functions ◽

Reactivity Descriptors ◽

Molecular Systems ◽

Chemical Structures ◽

Simple Protocol

AbstractThe chemical structures and molecular reactivities of the Amatoxin group of fungi-derived peptides have been determined by means of the consideration of a model chemistry that has been previously validated as well-behaved for our purposes. The reactivity descriptors were calculated on the basis of a methodological framework built around the concepts that are the outcome of the so called Conceptual Density Functional Theory (CDFT). This procedure in connection with the different Fukui functions allowed to identify the chemically active regions within the molecules. By considering a simple protocol designed by our research group for the estimation of the pKa of peptides through the information coming from the chemical hardness, these property has been established for the different molecular systems explored in this research. The information reported through this work could be of interest for medicinal chemistry researchers in using this knowledge for the design of new medicines based on the studied peptides or as a help for the understanding of the toxicity mechanisms exerted by them.

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