Substructural Molecular Fragments Research Articles

Chemoinformatics Analysis of the Colour Fastness Properties of Acid and Direct Dyes in Textile Coloration

The efficiency of chemoinformatics methods based on a fragment approach for the analysis of relationships between the chemical structure of textile dyes and colour fastness of the dyeings have been shown by examining a large set of properties, including the light fastness of acid dyes on wool and polyamide fibres, the sensitivity of acid dyes on wool to oxygen bleaching, the wash fastness of acid dyes on wool, the adsorption of direct dyes on cotton, and the photodegradation of azo dyes in solution. An analysis of the developed regression models depicted the contribution of ten substructural molecular fragments for each indicator of the colour fastness properties of acid and direct azo dyes on textile materials. The similarity of several individual multi-atomic fragments for acid and direct azo dyes was found for wool, polyamide, and cotton fibres, which indicates the coinciding mechanisms of the physicochemical processes that accompany the destruction of dyes while testing the light fastness and sensitivity of the dyeings to oxygen bleaching, as well as their adsorption/desorption with the wash fastness and dyeability of wool and cotton.

Prediction of Carbonate Selectivity of PVC-Plasticized Sensor Membranes with Newly Synthesized Ionophores through QSPR Modeling

Developing a potentiometric sensor with required target properties is a challenging task. This work explores the potential of quantitative structure-property relationship (QSPR) modeling in the prediction of potentiometric selectivity for plasticized polymeric membrane sensors based on newly synthesized ligands. As a case study, we have addressed sensors with selectivity towards carbonate—an important topic for environmental and biomedical studies. Using the logKsel(HCO3−/Cl−) selectivity data on 40 ionophores available in literature and their substructural molecular fragments as descriptors, we have constructed a QSPR model, which has demonstrated reasonable precision in predicting selectivities for newly synthesized ligands sharing similar molecular fragments with those employed for modeling.

Design of phosphoryl containing podands with Li+/Na+ selectivity using machine learning

ABSTRACT In this work we demonstrated, that machine learning opens a way for real design of ligands with required metal ion selectivity. We performed the ensemble QSPR modelling of the Li+/Na+ complexation selectivity and the stability constants for the Li+L and Na+L complexes of phosphoryl podands in nonaqueous solvent THF/СНCl3 (4:1 v/v). The models were built and cross-validated using MLR with the ISIDA QSPR program and SVM with the libSVM package. The program SVMsmf was implemented to fulfil an ensemble modelling using libSVM and the Substructural Molecular Fragments (SMF) descriptors. SMF were used as descriptors for the ensemble modelling, properties predictions by consensus models and design of combinatorial library of new ligands. SMF such as the P=O group, the ether and P=O groups bound through the aromatic ring contribute significantly to the Li+/Na+ selectivity. The developed models were applied for the prediction of the studied properties for a focused virtual library of 3057 phosphoryl podands generated using SMF contributions promising for selective binding of lithium. Consensus models selected hits for a synthesis by combinatorial library screening. Among the constructed selective ligands – hits, three new podands were synthesized, for which the experimentally estimated selectivity is in satisfactory agreement with that predicted.

Computational Model for Chromatographic Relative Retention Time of Polychlorinated Biphenyls Using Sub-structural Molecular Fragments

Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromato- graphic retention time to the chemical structure of a solute. Using the sub-structural molecular fragments (SMF) derived directly from the molecular structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were calculated. An eight-variable regression equation with the correlation coefficient of 0.9945 and the root mean square errors of 0.0134 was developed. Forward and backward stepwise regression variable selection and multi-linear regression analysis (MLRA) are combined to describe the effect of molecular structure on the RRT of PCB according to the QSRR method. To quantitatively relate RRT with the molecular structure MLR analysis is performed on the set of 163 sub-structural molecular fragments (SMF) provided by the ISIDA software. The eight fragments selected by variable subset selection, all belonging to the sub-fragments, adequately represent the structural factors influencing the affinity of PCB to SE-54 stationary phase in the separation process. Finally, a QSRR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 42 representative compounds excluded from model calibration. The prediction results from the MLR model are in good agreement with the experimental values. By applying the MLR method we can predict the test set with squared cross validated correlation coefficient (Q 2) of 0.9913 and root mean square error (RMSE) of 0.0169.

Prediction of Alternative Gasoline Sorption in a Semicrystalline Poly(ethylene).

In this work, we first report the acquisition of new experimental data and then the development of quantitative structure-property relationships on the basis of sorption values for neat compounds and up to quinary mixtures of some hydrocarbons, alcohols, and ethers, in a semicrystalline poly(ethylene). Two machine learning methods (i.e., genetic function approximation and support vector machines) and two families of descriptors (i.e., functional group counts and substructural molecular fragments) were used to derive predictive models. Models were then used to predict sorption variations when increasing the number of carbon atoms in a series of hydrocarbons and for n-alkan-1-ols. In addition to the performed internal/external validations, the model was further tested for surrogate gasolines containing ca. 300 compounds, and predicted sorption values were in excellent agreement with experimental data (R(2) = 0.940).

The complexation of metal ions with various organic ligands in water: prediction of stability constants by QSPR ensemble modelling

Quantitative structure–property relationship modelling of the stability constants (log K) for the 1:1 (M:L) complexes of metal ions (M = Li+, Na+, K+, Be2+, Al3+, Ga3+, In3+, VO2+, Fe3+, Th4+, NpO2 +, Am3+) with structurally diverse organic ligands in aqueous solution was performed using ensemble multiple linear regression (eMLR) analysis, support vector machines, associative neural networks and substructural molecular fragments’ descriptors. The models were validated with cross-validation procedures and with complementary external test set. For eMLR in the 5-fold cross-validation, root-mean squared error of log K varies from 0.49 (Li+) to 2.30 (In3+), and it is comparable with the systematic errors in experimental data. Designed predictor for end users implements consensus models together with the estimation of their applicability domain.

Prediction of the Adsorption Capability onto Activated Carbon of Liquid Aliphatic Alcohols using Molecular Fragments Method

Quantitative structure-property relationship (QSPR) for estimating the adsorption of aliphatic alcohols onto activated carbon were developed using substructural molecular fragments (SMF) method. The adsorption capacity of activated carbon (gr/100grC) for 150 aliphatic alcohols onto activated carbon (AC) is studied under equilibrium conditions. Forward and backwards stepwise regression variable selection and multilinear regression (MLR) are combined to describe the effect of molecular structure on the adsorption capacity of activated carbon according to the QSPR method. To quantitatively relate adsorption capacity (Qe) with the molecular structure MLR analysis is performed on the set of 15 substructural molecular fragments (SMF) provided by the software ISIDA. The five fragments selected by variable subset selection, all belonging to the subfragments, adequately represent the structural factors influencing the affinity of alcohols to AC in the adsorption process. Finally, a QSPR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 30 representative compounds excluded from model calibration. The prediction results from the MLR models are in good agreement with the experimental values. By applying MLR method we can predict the test set (30 compounds) with squared cross validated correlation coefficient (Q 2 ext) of 0.9538 and root mean square error (RMSE) of 2.0832.

Prediction on the Auto-ignition Temperature Using Substructural Molecular Fragments

Quantitative structure-property relationship (QSPR) modeling of the auto-ignition temperature was performed using ensemble multiple linear regression analysis and substructural molecular fragments method implemented in ISIDA software. In this paper, molecular fragments were used as molecular descriptors and we chose the length of sequences respectively from two to eight and twelve. Afterwards, the model was developed between the auto-ignition temperature and molecular descriptors and evaluated by the internal and external validations. Meanwhile, a new metric γ2m was adopted to assess the built model further. The model applicability domain was checked by the ISIDA software to verify prediction reliability. The results show that the prediction results are in good agreement with the experimental ones. The value of ▪ is more than 0.5, and the value of Δγ2m is lower than 0.2. The above results indicate that the established model is great successful. This paper provides a new and effective method for engineering to predict AIT of organic compounds.

Complexation of Mn2+, Fe2+, Y3+, La3+, Pb2+, and UO22+ with Organic Ligands: QSPR Ensemble Modeling of Stability Constants

Quantitative structure–property relationship (QSPR) modeling of the stability constant logK of the 1:1 (M:L) complexes of 6 transition-metal cations (M) with 261 (Mn2+), 87 (Fe2+), 105 (Y3+), 186 (La3+), 226 (Pb2+), and 66 (UO22+) organic ligands (L) in aqueous solutions at 298 K and an ionic strength 0.1 M was performed using ensemble multiple linear regression analysis and substructural molecular fragments as descriptors. The models have been validated in external 5-fold cross-validations procedure and on new ligands recently reported in the literature. Predicted logK values were calculated by consensus models as arithmetic means of 315 (Mn2+), 119 (Fe2+), 260 (Y3+), 290 (La3+), 304 (Pb2+), and 249 (UO22+) individual models. Absolute prediction error of logK is below 1.0 for 75% (UO22+), 70% (Mn2+, Fe2+, La3+), 65% (Pb2+), and 60% (Y3+) of the ligands and comparable with the systematic errors in experimental data. The developed QSPR models were used to screen selective ligands for the studied cations. The obtained models are incorporated in the COMET predictor available at http://infochim.u-strasbg.fr/cgi-bin/predictor.cgi.

Stability constants of complexes of Zn2+, Cd2+, and Hg2+ with organic ligands: QSPR consensus modeling and design of new metal binders

QSPR modeling of the stability constant log K of the complexes of Zn2+, Cd2+ and Hg2+ with various 556 (Zn2+), 347 (Cd2+) and 76 (Hg2+) organic ligands in water for the M2+ + L = (M2+)L equilibrium at 298 K and an ionic strength 0.1 M was performed. Two machine-learning methods were used: Multiple Linear Regression Analysis (MLR) and Partial Robust M-regression Algorithm (PRM). The PRM method was realized for consensus modeling using substructural molecular fragments (SMF) as descriptors. Using different types of SMF, ensembles of individual predictive MLR and PRM models were prepared to build consensus models (CM). The root mean squared error of test set predictions of fivefold cross-validations is 1.8 and 1.9 (Zn2+), 1.9 and 2.2 (Cd2+), 2.7 and 2.8 (Hg2+) for the MLR and PRM approaches correspondingly. Experimental log K values vary in the range of 0.8–21.9 (Zn2+), 0.9–23.3 (Cd2+) and 1.6–29.7 (Hg2+). Extra validation of the models has been performed on a set of ligands recently reported in the literature. The QSPR models are sampled for the design of new binders of the Zn2+, Cd2+ Hg2+ cations.

ChemInform Abstract: Modeling of Ion Complexation and Extraction Using Substructural Molecular Fragments.

A substructural molecular fragment (SMF) method has been developed to model the relationships between the structure of organic molecules and their thermodynamic parameters of complexation or extraction. The method is based on the splitting of a molecule into fragments, and on calculations of their contributions to a given property. It uses two types of fragments: atom/bond sequences and “augmented atoms” (atoms with their nearest neighbors). The SMF approach is tested on physical properties of C2−C9 alkanes (boiling point, molar volume, molar refraction, heat of vaporization, surface tension, melting point, critical temperature, and critical pressures) and on octanol/water partition coefficients. Then, it is applied to the assessment of (i) complexation stability constants of alkali cations with crown ethers and phosphoryl-containing podands, and of β-cyclodextrins with mono- and 1,4-disubstituted benzenes, and (ii) solvent extraction constants for the complexes of uranyl cation by phosphoryl-containing ...

ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors

In this paper we illustrate the application of the ISIDA (In SIlico design and Data Analysis) software to perform virtual screening of large databases of compounds and reactions and to assess some ADME/Tox properties. ISIDA represents an ensemble of tools allowing users to store, search and analyze the data, to perform similarity searches in large databases of molecules and reactions, to build and validate QSAR models, and to generate and screen virtual combinatorial libraries. It uses its own descriptors (substructural molecular fragments and fuzzy pharmacophore triplets). Workflow can be easily organized by combining different ISIDA modules. Several examples of ISIDA applications (similarity search of potent benzodiazepine ligands with FPT, QSAR modeling of aqueous solubility, aquatic toxicity, tissue-air partition coefficients, anti-HIV activity, and screening of the “Chimiotheque Nationale” Database), are discussed. Particular attention is paid to mining reaction databases using Condensed Reaction Graphs approach.

Exhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids: How Accurately Can We Predict Melting Points?

Several popular machine learning methods--Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), modified version of the partial least-squares analysis (PLSM), backpropagation neural network (BPNN), and Multiple Linear Regression Analysis (MLR)--implemented in ISIDA, NASAWIN, and VCCLAB software have been used to perform QSPR modeling of melting point of structurally diverse data set of 717 bromides of nitrogen-containing organic cations (FULL) including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT). Several types of descriptors were tested: E-state indices, counts of atoms determined for E-state atom types, molecular descriptors generated by the DRAGON program, and different types of substructural molecular fragments. Predictive ability of the models was analyzed using a 5-fold external cross-validation procedure in which every compound in the parent set was included in one of five test sets. Among the 16 types of developed structure--melting point models, nonlinear SVM, ASNN, and BPNN techniques demonstrate slightly better performance over other methods. For the full set, the accuracy of predictions does not significantly change as a function of the type of descriptors. For other sets, the performance of descriptors varies as a function of method and data set used. The root-mean squared error (RMSE) of prediction calculated on independent test sets is in the range of 37.5-46.4 degrees C (FULL), 26.2-34.8 degrees C (PYR), 38.8-45.9 degrees C (IMZ), and 34.2-49.3 degrees C (QUAT). The moderate accuracy of predictions can be related to the quality of the experimental data used for obtaining the models as well as to difficulties to take into account the structural features of ionic liquids in the solid state (polymorphic effects, eutectics, glass formation).

QSPR Modeling of the AmIII/EuIII Separation Factor: How Far Can we Predict ?

Exhaustive quantitative structure‐property relationship (QSPR) modeling of the separation factor logSF for 46 polyazaheterocyclic ligands extracting Am3+ and Eu3+ from nitric acid aqueous solution to the 1,1,2,2–tetrachloroethane phase has been done using different computational approaches. Modeling methods included Multiple Linear Regression, Radial Basis Function Neural Networks, and Associated Neural Networks; two types of descriptors (substructural molecular fragments and molecular descriptors) and different techniques of variable selection have been employed. The developed QSPR models applied for novel t‐Bu‐hemi‐BTP ligand resulted in logSF=1.07−1.46; these predicted values somewhat exceed the experimental value logSF=1.0. Several hypothetical extractants potentially possessing high logSF values are proposed. An influence of uncertainties in initial experimental data as well as the choice of the theoretical approach on the performance of QSPR models is discussed.

Structure-property modelling of complex formation of strontium with organic ligands in water

The method of substructural molecular fragments based on representation of the molecular graph by ensembles of fragments and involving calculations of those contributions to the given property is applied to the modelling of stability constants of the complexes of strontium(II) with organic ligands in water. Reliability of predictions of developed structure-property models was examined using three different test sets of structurally diverse ligands. The obtained models have been used for generation and screening of combinatorial library of virtual ligands. Some hypothetical efficient Sr(II) binders were suggested.

Benchmarking of Linear and Nonlinear Approaches for Quantitative Structure−Property Relationship Studies of Metal Complexation with Ionophores

A benchmark of several popular methods, Associative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), Maximal Margin Linear Programming (MMLP), Radial Basis Function Neural Network (RBFNN), and Multiple Linear Regression (MLR), is reported for quantitative-structure property relationships (QSPR) of stability constants logK1 for the 1:1 (M:L) and logbeta2 for 1:2 complexes of metal cations Ag+ and Eu3+ with diverse sets of organic molecules in water at 298 K and ionic strength 0.1 M. The methods were tested on three types of descriptors: molecular descriptors including E-state values, counts of atoms determined for E-state atom types, and substructural molecular fragments (SMF). Comparison of the models was performed using a 5-fold external cross-validation procedure. Robust statistical tests (bootstrap and Kolmogorov-Smirnov statistics) were employed to evaluate the significance of calculated models. The Wilcoxon signed-rank test was used to compare the performance of methods. Individual structure-complexation property models obtained with nonlinear methods demonstrated a significantly better performance than the models built using multilinear regression analysis (MLRA). However, the averaging of several MLRA models based on SMF descriptors provided as good of a prediction as the most efficient nonlinear techniques. Support Vector Machines and Associative Neural Networks contributed in the largest number of significant models. Models based on fragments (SMF descriptors and E-state counts) had higher prediction ability than those based on E-state indices. The use of SMF descriptors and E-state counts provided similar results, whereas E-state indices lead to less significant models. The current study illustrates the difficulties of quantitative comparison of different methods: conclusions based only on one data set without appropriate statistical tests could be wrong.

“In Silico” Design of Potential Anti-HIV Actives Using Fragment Descriptors

Substructural Molecular Fragments (SMF) method was applied for computer-aided design of new compounds potentially possessing high anti-HIV activities: tetrahydroimidazobenzodiazepinone (TIBO) derivatives and 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives. Using available experimental data, the SMF method was first applied to build QSAR models based on fragment descriptors (atom/bond sequences and "augmented atoms"). The focused virtual combinatorial libraries containing 891 TIBO derivatives and 2640 HEPT derivatives were then generated systematically attaching selected substituents to corresponding Markush structures. Finally, a screening of those libraries using developed QSAR models led to several hits potentially possessing high anti-HIV activity.

Structure—property modeling of metal binders using molecular fragments

The method of Substructural Molecular Fragments (SMF) based on splitting of a molecular graph into a limited number of topological fragments and calculation of their contributions to a given property was applied to the structure—property modeling of (i) stability constants of the complexes formed by the K+ cation with phosphoryl-containing podands in a mixed THF—CHCl3 solvent, (ii) distribution coefficients for Hg and Pt extraction by podands from water into 1,2-dichloroethane, and (iii) stability constants of complexes of crown ethers with alkali metal cations in methanol. A procedure for the reduction of the number of fragment descriptors in structure—property relationship (QSPR) models based on the Student t-test was proposed. The use of selected variables improves the robustness of the structure—property models.

Anti‐HIV Activity of HEPT, TIBO, and Cyclic Urea Derivatives: Structure—Property Studies, Focused Combinatorial Library Generation, and Hits Selection Using Substructural Molecular Fragments Method.

Substructural molecular fragments (SMF) method [Solov'ev, V. P.; Varnek, A.; Wipff, G. J. Chem. Inf. Comput. Sci. 2000, 40, 847-858] was applied to assess anti-HIV activity for large data sets for three families of compounds: 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives, tetrahydroimidazobenzodiazepinone (TIBO) derivatives, and cyclic urea (CU) derivatives. The SMF method uses 49 types of topological descriptors (atom/bond sequences and augmented atoms) which, being coupled with 3 linear and nonlinear fitting equations, allows the user to generate up to 147 structure-property models. For each family of compounds, the modeling was performed on several training sets followed by the validation calculations where three best fit models were applied. Calculated activities well reproduce available experimental data. On the basis of the optimal molecular fragments, the focused combinatorial library containing 252 virtual HEPT derivatives has been generated. Its filtering led to several hits potentially possessing anti-HIV activity.

Anti-HIV activity of HEPT, TIBO, and cyclic urea derivatives: structure-property studies, focused combinatorial library generation, and hits selection using substructural molecular fragments method.

Substructural molecular fragments (SMF) method [Solov'ev, V. P.; Varnek, A.; Wipff, G. J. Chem. Inf. Comput. Sci. 2000, 40, 847-858] was applied to assess anti-HIV activity for large data sets for three families of compounds: 1-[2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) derivatives, tetrahydroimidazobenzodiazepinone (TIBO) derivatives, and cyclic urea (CU) derivatives. The SMF method uses 49 types of topological descriptors (atom/bond sequences and "augmented atoms") which, being coupled with 3 linear and nonlinear fitting equations, allows the user to generate up to 147 structure-property models. For each family of compounds, the modeling was performed on several training sets followed by the validation calculations where three best fit models were applied. Calculated activities well reproduce available experimental data. On the basis of the "optimal" molecular fragments, the focused combinatorial library containing 252 virtual HEPT derivatives has been generated. Its filtering led to several hits potentially possessing anti-HIV activity.