Inverse Quantum Chemistry: Concepts and Strategies for Rational Compound Design

The rational design of molecules and materials is becoming more and more important. With the advent of powerful computer systems and sophisticated algorithms, quantum chemistry plays an important role in rational design. While traditional quantum chemical approaches predict the properties of a predefined molecular structure, the goal of inverse quantum chemistry is to find a structure featuring one or more desired properties. Herein, we review inverse quantum chemical approaches proposed so far and discuss their advantages as well as their weaknesses.Comment: 43 pages, 5 figure

In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

A QSPR Study of Sweetness Potency Using the CODESSA Program

A total of 397 natural and artificial comprehensively referenced sweeteners were classified by their structures into nine sets. The sweetness potencies were correlated with quantum Chemical and other molecular descriptors using the heuristic and the best multi-linear regression methods of the CODESSA software package. QSPR models (two-parameter unless otherwise indicated) emerged for each subclass of sweeteners with R2 values of 0.835 for 47 aldoximes, 0.959 for 8 acesulfamates, 0.919 for 9 sulfamates, 0.941 for 10 α-arylsulfonylalkanoic acids, 0.715 for 27 guanidines (0.802 in a three-parameter correlation), 0.769 for 30 ureas/thioureas (0.888 in a three-parameter correlation), 0.905 for 20 natural sweeteners, 0.957 for 7 miscellaneous sweeteners (one-parameter correlation), 0.688 for 87 peptides (five-parameter correlation). A significant global five-parameter QSPR theoretical model with R2 of 0.686 for the entire set of sweeteners is presented and discussed with reference to the possible existence of single or multiple sweetness receptors

Machine Learning, Quantum Mechanics, and Chemical Compound Space

We review recent studies dealing with the generation of machine learning models of molecular and solid properties. The models are trained and validated using standard quantum chemistry results obtained for organic molecules and materials selected from chemical space at random

Adsorption and Quantum Chemical Studies on the Inhibition Potentials of Some Thiosemicarbazides for the Corrosion of Mild Steel in Acidic Medium

Three thiosemicarbazides, namely 2-(2-aminophenyl)-N phenylhydrazinecarbothioamide (AP4PT), N,2-diphenylhydrazinecarbothioamide (D4PT) and 2-(2-hydroxyphenyl)-N-phenyl hydrazinecarbothioamide (HP4PT), were investigated as corrosion inhibitors for mild steel in H2SO4 solution using gravimetric and gasometric methods. The results revealed that they all inhibit corrosion and their % inhibition efficiencies (%IE) follow the order: AP4PT > HP4PT > D4PT. The %IE obtained from the gravimetric and gasometric experiments were in good agreement. The thermodynamic parameters obtained support a physical adsorption mechanism and the adsorption followed the Langmuir adsorption isotherm. Some quantum chemical parameters were calculated using different methods and correlated with the experimental %IE. Quantitative structure activity relationship (QSAR) approach was used on a composite index of some quantum chemical parameters to characterize the inhibition performance of the studied molecules. The results showed that the %IE were closely related to some of the quantum chemical parameters, but with varying degrees. The calculated/theoretical %IE of the molecules were found to be close to their experimental %IE. The local reactivity has been studied through the Fukui and condensed softness indices in order to predict both the reactive centers and to know the possible sites of nucleophilic and electrophilic attacks

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost

Evaluation of pesticide toxicity: a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides

The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, LC50, and the avian oral toxicity, LD50, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevalidation = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system

Quantum theory of QSAR

Es discuteix aquí la forma de desenvolupar un formalisme on les mesures de semblança quàntiques (QSM) es transformen en un producte natural, que sorgeix d'un marc de treball específic relacionat amb la teoria quàntica. Aquesta fita s'empra per establir una connexió fonamental entre la teoria quàntica i les QSAR, que s'estudien més endavant des del punt de vista de la química quàntica discreta. A fi d'assolir aquest objectiu es revisen en un primer pas diverses eines teòriques. D'aquesta manera la primera secció s'associa a la construcció del concepte de conjunt etiquetat. Més tard, la definició d'objecte quàntic (QO) s'aclareix emprant tant el rerefons de la teoria quàntica com els conceptes previs, que formen part del formalisme de conjunt etiquetat. Per definir un QO, es demostra que les funcions de densitat (DF) tenen un paper principal i es presenta una possible forma matemàtica simplificada amb propòsits computacionals. En el camí de preparar les eines per dilucidar el problema, els conjunts convexos resulten ser prominents, mentre que la noció de semiespai vectorial, apareix com a conseqüència. Les regles de transformació, un aparell dissenyat per connectar les funcions d'ona amb les DF, es defineixen en un proper pas. També es descriuen diversos aspectes d'aquest tipus de discussió preliminar, entre altres el concepte de distribucions d'energia cinètica, que apareixen dins la definició dels espais de Hilbert generals i els espais de Sobolev. Les QSM, com una font de la representació discreta de les estructures moleculars, es fan evidents dins d'aquest concepte. Un desenvolupament posterior de la teoria intenta estudiar els processos de discretització; això és: la transformació dels espais funcionals d'infinites dimensions en espais n-dimensionals. Aquest resultat afegeix noves perspectives a la representació discreta de QO, ja que: a) esdevé una font de nous descriptors, b) descriu el fonament de les QSAR, cosa que permet la construcció de models adequats comWays of developing the formalism where Quantum Similarity Measures (QSM) become a natural product issuing from a specific mathematical framework related to quantum theory are discussed. This fact is used to establish a fundamental connection between Quantum Theory and QSAR, which is analysed in turn within the realm of discrete quantum chemistry. In order to achieve such an objective several theoretical tools are revised in a previous step. The first section is devoted to constructing the concept of the Tagged Set. Next, the definition of Quantum Object (QO) is clarified by means of Quantum Theory background ideas and the previous Tagged Set formalism. In the definition of QO, Density Functions (DF) are shown to play a fundamental role and a possible simplified mathematical picture is presented for possible computational purposes. In the process of preparing the problem-solving tools, convex sets become prominent, and the notion of Vector Semispace appears as a consequence. The Transformation Rule, a device to connect Wavefunctions with DF, is defined in a new step. Various products of this preliminary discussion are described, among them the concept of Kinetic Energy distributions, issuing from the background concept of extended Hilbert and Sobolev spaces. QSM as a source of discrete representation of molecular structures is made evident in this context. Further theoretical development undertakes precise study of discretization, that is, the transformation of infinite-dimensional functional spaces into n-dimensional ones. This result adds new perspectives to the discrete representation of QO, because a) It provides a source of new QO descriptors, b) It describes the QSAR theoretical background enabling the construction of adequate models like tuned-QSAR, and c) It allows the construction of sound and general alternatives of Hammet?s ó or log P parameters. In this context, QSM appear to produce QSAR models constructed with unbiased descriptors, deducible from quant

Quantitative structure-property relationships for predicting chlorine demand and disinfection byproducts formation in drinking water

Models are important tools for designing or redesigning water treatment processes and technologies to minimize disinfection byproducts (DBPs) formation without compromising disinfection efficiency. Empirical models, which are the most common, are based on bulk water quality parameters that vary with time and space. These parameters may not always have linear relationships with chlorine demand and DBPs formation which make structure-based models more attractive to study. In this dissertation, Quantitative Structure-Property Relationship (QSPR) models which make use of structural properties of individual molecules were developed using experimental data obtained from the literature. The amounts are reported in moles of chlorine (HOCl) consumed or DBP formed per mole of a compound (Cp). The QSPRs were derived by multiple linear regression of chlorine demand or DBPs on a set of significant constitutional descriptors. The QSPRs were also tested for predictive power using cross validation and external validation for which the criteria were: Rc2 \u3e 0.6, q2 \u3e 0.5, 0.85 ≤ k ≤ 1.15 and Rt = (Ri2-Ro2)/Ri2 \u3c 0.1. The eight descriptor QSPR for HOCl demand had good statistics of fit (Rc2 = 0.86 and SDE = 1.24 mol-HOCl/mol-Cp, N = 159) and also showed high predictive power on cross validation data (q2LMO = 0.86, RMSELMO = 1.21 mol-Cl2/mol-Cp) and external validation data (q2ext = 0.88, RMSELMO = 1.17 mol-HOCl/mol-Cp). The QSPR also met all the criteria for QSPR predictive power and was robust. This model was integrated with AlphaStep model of natural organic matter (NOM) so as to estimate chlorine demand of surface waters. The predicted chlorine demand was 27.55 μmol-HOCl/mg-C which is comparable to 27-33 μmol-HOCl/mg-C reported for surface waters. The 4 descriptor QSPR for total organic halide (TOX) formation had Rc2 = 0.72 and SDE = 0.43 mol-Cl/mol-Cp. The Leave-One-Out validation of the QSPR (q2LOO = 0.60, RMSE = 0.5 mol-Cl/mol-Cp, N = 49) and external validation (q2Ext = 0.67, RMSE = 0.48 mol-Cl/mol-Cp, N = 12). These statistics showed that the QSPR had high predictive power and also was robust. Results from integration of the QSPR with AlphaStep predicted TOX in surface water to be 183.6 μmol-Cl/mg-C which comparable 170-298 μg-Cl/mol-Cp for the experimental TOX formation measured for whole dissolved organic matter. Trichloromethane (TCM) and trichloroacetic (TCAA) were the two specific DBPs studied. The QSPR for TCM formation had three descriptors and statistics of fit were Rc2 = 0.97 and SDE = 0.08 mol-TCM/mol-Cp and was validated by LMO data and external data. The results showed that LMO cross validation (q2LMO = 0.94, RMSE = 0.09 mol-TCM/mol-Cp, N = 90) and external validation (q2Ext = 0.94, RMSE = 0.08 mol-TCM/mol-Cp, N = 27) met criteria of predictive power and was therefore robust. The model prediction of 0.33 mol-TCM/mol-Cp was higher than 0.13 mol-TCM/mol-Cp observed for tannic acid. The QSPRs for predicting TCAA formation were developed but none of them met all the criteria for predictive power and were therefore not robust. The relationship between predicted TCAA and experimental data was too weak to be useful. This implies that TCAA formation has insignificant linear relationship with constitutional descriptors and it may better be predicted by QSPRs derived from non-linear algorithms. A major drawback of the constitutional descriptors is that they cannot explain electronic or steric effects. It is not easy to explain the differences in electron density and steric effects when same number of substituents occupy different position relative each other in aromatic ring (e.g., catechol vs. quinol). Use of geometrical descriptors (e.g., molecular volume, solvent accessible area), quantum-chemical descriptors (e.g., dipole moment, polarizability) or electrostatic descriptors (e.g., partial charge, polarity index) is recommended