Weighted Holistic Invariant Molecular Research Articles

Predicting organic structures directing agents for zeolites with conditional deep learning generative model

Organic structures directing agents (OSDAs) are important components of the synthesis of zeolites, and their molecular properties play a significant role in the design of zeolite structures. As of yet, scientists have usually used priori knowledge to select suitable OSDAs for synthesis of specific structures of zeolites. We utilize a comprehensive database of OSDAs, zeolites, and gel chemistry to develop a computationally efficient model with fewer parameters based on self-attention mechanism and Long and Short-term Memory networks (LSTM) for modeling the interactions between OSDAs, zeolite structures and gel chemistry. Our model is able to learn the simplified molecules input line-entry system (SMILES) syntax and grasp molecular physicochemical properties by comparing the custom metric and weighted holistic invariant molecular (WHIM) descriptor after dimensionality reduction. Our model is capable of producing OSDA-like molecules under the conditions of zeolite structures and chemical gels, and predicting OSDA molecules for the specific structures of zeolites.

Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks.

Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA–zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates.

Dynamical Behavior and Conformational Selection Mechanism of the Intrinsically Disordered Sic1 Kinase-Inhibitor Domain.

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.

Development of MLR and SVM Aided QSAR Models to Identify Common SAR of GABA Uptake Herbal Inhibitors used in the Treatment of Schizophrenia.

Background: Alterations in GABAnergic system are implicated in the pathophysiology of schizophrenia. Available antipsychotics that target GABA receptor form a desirable therapeutic strategy in the treatment regimen of schizophrenia, unfortunately, suffer serious setback due to their prolonged side effects. The present investigation focuses on developing QSAR models from the biological activity of herbal compounds and their derivatives that promise to be alternative candidates to GABA uptake inhibitors.Methods: Three sets of compounds were undertaken in the study to develop QSAR models. The first set consisted of nine compounds which included Magnolol, Honokiol and other GABA acting established compounds. The second set consisted of 16 derivatives of N-diarylalkenyl-piperidinecarboxylic acid. The third QSAR dataset was made up of thirty two compounds which were Magnolol and Honokiol derivatives. Multiple linear regressions (MLR) and support vector machine (SVM) supervised quantitative structure-activity relationship (QSAR) models were developed to predict the biological activity of these three sets. The purpose of taking three QSAR sets of diverse chemical structures but identical in their GABA targeting and pharmacological action was to identify common chemical structure features responsible for structure-activity relationship (SAR).Results: Linear and non-linear QSAR models confirmed that the three sets shared common structural descriptors derived from WHIM (Weighted Holistic Invariant Molecular descriptors), 3D-MoRSE and Eigenvalue classes.Conclusion: It was concluded that properties like electro negativity and polarizability play a crucial role in controlling the activity of herbal compounds against GABA receptor.

Assessment and modeling of the novel toxicity data set of phenols to Chlorella vulgaris

This study provides for the first time the 96h toxicity of 30 phenols to Chlorella vulgaris. Analysis of the novel data set revealed that the algal toxicity of polar narcotics and respiratory uncouplers was correlated strongly to the pH corrected hydrophobicity parameter, Log D, demonstrating the importance of ionization in the C. vulgaris test system. Compounds expected to act by more reactive mechanisms were shown to have toxicity in excess of that predicted by Log D and were successfully modelled using the activation energy index (AEI). Three global quantitative structure–activity relationships (QSARs) were constructed using the C. vulgaris data set and validated externally using a data set retrieved from literature comprising the toxicity of 58 compounds to freshwater alga Pseudokirchneriella subcapitata. Results revealed that the response-surface model was highly interpretable and provided acceptable predictions for polar narcotics and respiratory uncouplers, though it lacked the reliability to predict the toxicity of reactive phenols. In two other externally validated QSAR models, a WHIM (Weighted Holistic Invariant Molecular) descriptor, namely, Tm (T total size index/weighted by atomic masses), revealed promising results that could be used to predict algal toxicity of compounds other than phenols such as anilines.

A descriptor of amino acids SVWG and its applications in peptide QSAR

A descriptor, SVWG (principal component scores vector of WHIM (weighted holistic invariant molecular index) descriptors and GETWAY (GEometry, Topology, and Atom‐Weights AssemblY) descriptors), was derived from a principal component analysis of a matrix of two structural variables of coded amino acid. SVWG scales were then applied in three panels of peptide quantitative structure activity relationship (QSAR) that were molded by partial least squares regression. The obtained models with the correlation coefficient () and cross‐validation correlation coefficient () were 0.893 and 0.830 for angiotensin‐converting enzyme inhibitors, 0.990 and 0.968 for antimicrobial peptides, and 0.955 and 0.908 for oxytocin, respectively. Satisfactory results showed that information related to biological activity can be systemically expressed by SVWG scales, which may be a useful structural expression methodology for study on peptide QSAR. Copyright © 2012 John Wiley & Sons, Ltd.

Conformational Free Energy Modeling of Druglike Molecules by Metadynamics in the WHIM Space

Protein-ligand affinities can be significantly influenced not only by the interaction itself but also by conformational equilibrium of both binding partners, free ligand and free protein. Identification of important conformational families of a ligand and prediction of their thermodynamics is important for efficient ligand design. Here we report conformational free energy modeling of nine small-molecule drugs in explicitly modeled water by metadynamics with a bias potential applied in the space of weighted holistic invariant molecular (WHIM) descriptors. Application of metadynamics enhances conformational sampling compared to unbiased molecular dynamics simulation and allows to predict relative free energies of key conformations. Selected free energy minima and one example of transition state were tested by a series of unbiased molecular dynamics simulation. Comparison of free energy surfaces of free and target-bound Imatinib provides an estimate of free energy penalty of conformational change induced by its binding to the target.

Predicting Infinite Dilution Activity Coefficients of Chlorinated Organic Compounds in Aqueous Solution Based on Three-Dimensional WHIM and GETAWAY Descriptors

The ability of the Weighted Holistic Invariant Molecular (WHIM) and GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors to represent the effect of molecular structure on the infinite dilution activity coefficients (γ∞) of chlorinated organic compounds in aqueous solution was investigated. To this end, the dataset of 45 chlorinated organic compounds was randomly divided into a training set (35) and a test set (10). The genetic algorithm was employed for descriptor selection and model development. A five-parameter correlation equation for ln(γ∞) was obtained, with a squared correlation coefficient (R2) of 0.9609 and a standard error of estimation (s) of 0.599 for the training set. The reliability of the proposed model was further illustrated by means of the leave-one-out cross-validation procedure and validation through an external test set. All descriptors involved in the model can be derived directly from the structures of the compounds, which makes it very useful in predicting γ∞ of other chlorinated organic compounds not involved in the present dataset.

Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Quantitative structure–retention relationships of pesticides in reversed-phase high-performance liquid chromatography based on WHIM and GETAWAY molecular descriptors

The ability of the Weighted Holistic Invariant Molecular (WHIM) and GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors to represent the effect of molecular structure on the retention of pesticides in reversed-phase high-performance liquid chromatography (RP-HPLC) is investigated. To this end, two retention data sets previously collected in water–acetonitrile mobile phase are re-examined. The first data set ( data-set-1) consists of retention data of 26 neutral compounds belonging to widely used pesticide classes, collected within the mobile phase composition range 40–65% (v/v) acetonitrile. The second data set ( data-set-2) describes retention of phenoxy acid herbicides and structurally related compounds (benzoic acid and phenylacetic acid derivatives), as a whole covering the p K a range 2.3–4.3, as a function of mobile phase composition, ranging between 30 and 70% (v/v) acetonitrile, and pH, ranging between 2 and 5. For each data set, the mobile phase attributes are combined with WHIM or GETAWAY descriptors into “mixed” predictive models in order to attempt retention modelling within the whole mobile phase composition range of analytical interest. Six- or seven-dimensional multilinear models, preliminarily selected using a genetic algorithm, were improved using a multi-layer artificial neural network (ANN) learned by back propagation. ANN performance was tested on three molecules not used in the learning stage and by leave-more-out cross validation. The results reveal that while WHIM descriptors seem not adequate to model retention of solutes of data-set-1, GETAWAY descriptors provide a satisfactory retention model. On the other hand WHIM and GETAWAY descriptors applied to data-set-2 provide a similar performance, even if slightly worse as compared with the above case. Accuracy of retention modelling in these cases is comparable or slightly poorer as compared with the results previously obtained by combining quantum chemical descriptors or usual physico-chemical solute properties (log k ow and p K a) and mobile phase attributes.

Modeling of the Inhibition of the Intermediate‐Conductance Ca2+‐Activated K+ Channel (IKCa1) by Some Triarylmethanes Using Quantum Chemical Properties Derived From Ab Initio Calculations

AbstractInhibition of the Intermediate‐Conductance Ca2+‐Activated K+ Channel (IKCa1) by some Triarylmethane (TRAM) derivatives has been successfully modeled by using quantum chemical properties derived from Ab Initio calculations and Weighted Holistic Invariant Molecular (WHIM) descriptors. The predictive model was conducted by Partial Least Squares (PLS) method in combination with Genetic Algorithm (GA). Models with good predictivity were obtained both in cross‐validation procedures and external test set predictions. Our results show that Highest Occupied Molecular Orbital (HOMO) energy, some electronic properties, and topological distributions are important parameters influencing the binding of TRAMs with IKCa1. In addition, our model identified some relevant patterns that can be useful for understanding the IKCa1 inhibitory process and the design of new blockers.

QSARs for congener-specific toxicity of polyhalogenated dibenzo-p-dioxins with DFT and WHIM theory

Polyhalogenated dibenzo-p-dioxins (PHDDs) have become the most notorious pollutants in the environment. However, the origin of their congener-specific toxicity is not well understood. For explaining the difference in toxicity between PHDDs as well as their potencies of aryl hydrocarbon hydroxylase (AHH) and 7-ethoxyresorufin O-deethylase (EROD) induction, quantitative structure–activity relationships (QSARs) were constructed through the combined application of DFT (density functional theory) and WHIM (weighted holistic invariant molecular) theory. Results from the QSAR analyses suggest that dispersion interaction along the lateral sites of PHDDs should interpret the vast majority of variance of binding affinities as well as the consequent toxicity. Although electrostatic interaction is comparatively less influential, it should not be negligible. Long-range dispersion interaction is also described in QSARs with minute influence. Quadrupole moment tensor perpendicular to the ring plane, i.e., Qzz and its implicated electrostatic interaction plays an important role in the contribution to induction potencies.

WHIM Descriptors of Shape

AbstractWHIM descriptors are 3D structural descriptors obtained from the (x,y,z)‐atomic coordinates of a molecular conformation of a chemical, and are used successfully in QSAR modelling. They are built in so as to capture relevant 3D information regarding different features of molecular structure: size, shape, symmetry and atom distribution. Different weights are used to obtain particular information for each set of descriptors. Recently, some doubts have been raised in the literature regarding the ability of Weighted Holistic Invariant Molecular (WHIM) descriptors of shape (the K non‐directional WHIM descriptors) to describe the difference between linear and non‐linear molecules. A data set of seventy aliphatic and aromatic chemicals of different shape (linear and branched congeners) has been studied here, and is represented by K descriptors (global non‐directional WHIM descriptors of shape) based on different weights. The K descriptors range from 0 for a spherical molecule to 1 for a perfectly linear molecule. The present study confirms the findings, already reported by the author, that the WHIM descriptors of shape (K) are unequivocally able to differentiate linear and non‐linear molecules.

Chemometric selection of a small set of pharmaceutical active substances used in determining the orthogonality and similarity of chromatographic systems

A set of 68 active pharmaceutical substances (mainly basic, but also some neutral and acidic) was earlier used to determine the orthogonality/similarity of chromatographic systems. The orthogonality of the systems was evaluated from correlation coefficients-based weighted-average-linkage (WAL) dendrograms and color maps. To increase the throughput in assessing systems, a representative subset of substances that leads to analogous conclusions about orthogonality/similarity was selected. Both the Kennard and Stone (K&S) algorithm applied on the autoscaled principal component analysis data from the Weighted Holistic Invariant Molecular (WHIM) descriptors of the test molecules, and WAL dendrograms on retention data were used for that purpose. A subset of 10 substances was found to give similar conclusions about orthogonality and similarity of the systems examined.

Quantitative structure–activity relationship studies of vitamin D receptor affinity for analogues of 1α,25-dihydroxyvitamin D 3. 1: WHIM descriptors

The weighted holistic invariant molecular (WHIM) approach has been applied to the study of the VDR affinity of 86 vitamin D analogues. A model able to describe more than 71% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of three different approaches, including the use of BCUT, Galvez topological charge indices, and 2D autocorrelations descriptors, was able to explain more than 38% of the variance in the mentioned property, even with more variables in the equation.

Comparison of Ridge Regression, Partial Least-Squares, Pairwise Correlation, Forward- and Best Subset Selection Methods for Prediction of Retention Indices for Aliphatic Alcohols

A quantitative structure-retention relationship (QSRR) study based on multiple linear regression (MLR) was performed for the description and prediction of Kováts retention indices (RI) of alcohol compounds. Alcohols were of saturated, linear or branched types and contained a hydroxyl group on the primary, secondary or tertiary carbon atoms. Constitutive and weighted holistic invariant molecular (WHIM) descriptors were used to represent the structure of alcohols in the MLR models. Before the model building, five variable selection methods were applied to select the most relevant variables from a large set of descriptors, respectively. The selected molecular properties were included into the MLR models. The efficiency of the variable selection methods was also compared. The selection methods were as follows: ridge regression (RR), partial least-squares method (PLS), pair-correlation method (PCM), forward selection (FS) and best subset selection (BSS). The stability and the validity of the MLR models were tested by a cross-validation technique using a leave-n-out technique. Neither RR nor PLS selected variables were able to describe the Kováts retention index properly, and PCM gave reliable results in the description but not for prediction. We built models with good predicting ability using FS and BSS as a selection method. The most relevant variables in the description and prediction of RIs were the mean electrotopological state index, the molecular mass, and WHIM indices characterizing size and shape.

Kohonen Network Study of Aromatic Compounds Based on Electronic and Nonelectronic Structure Descriptors

Atoms in Molecules (AIM) and Electron Localization Function (ELF) methodologies were applied to describe the electronic structure of 88 aromatic compounds. The analyzed database contains molecules substituted by nucleophilic and electrophilic groups which are responsible for electron density distribution in the molecule and further for its reactivity. Radial Distribution Function (RDF), Weighted Holistic Invariant Molecular (WHIM), Three-Dimensional Molecule Representation of Structures based on Electron Diffraction (3D-MoRSE) and Geometry, Topology and Atom-Weights Assembly (GETAWAY) descriptors were taken into account describing the structures of the analyzed molecules. According to generated descriptor space the classification of the molecules has been subsequently performed using unsupervised learning strategy and Kohonen network. The final step of descriptor space testing was supervised learning of Counter-Propagation Artificial Neural Network (CPANN) using n-octanol/water partition coefficient (logP), dipole moment (DM) and molecular refractivity (MR) as target values.

Quantitative structure–retention relationships XIV: Prediction of gas chromatographic retention indices for saturated O-, N-, and S-heterocyclic compounds

In this study, quantitative structure–retention relationship (QSRR) technique was used to find the best approximation and to predict gas chromatographic retention indices for O-, N-, and S-heterocyclic compounds on standard nonpolar polydimethyl siloxane stationary phase. Boiling point (BP) and calculated properties were used to encode the structure of compounds. Three- and two-dimensional calculated properties such as weighted–holistic invariant molecular (WHIM) descriptors, geometry topology and atom weights assembly (GETAWAY) descriptors, connectivity indices, and zero-dimensional constitutive descriptors were used. Variable subset selection (VSS) and partial least squares (PLS) projections to latent structures were used to select the most significant variables from a large set of descriptors. Multiple linear regression (MLR) and PLS were applied to find the relationship between selected properties and gas chromatographic retention indices. PLS was not able to select the most important descriptors (boiling point or molecular weight). The predictive ability of the models was tested by cross-validation. Solely calculated descriptors were not able to give proper models. Boiling point was always necessary for good prediction. PLS models containing boiling points were suitable for retention index prediction, whereas MLR did not give real linear models.

Neuronal nicotinic acetylcholine receptor agonists: pharmacophores, evolutionary QSAR and 3D-QSAR models.

Neuronal nicotinic acetylcholine ion channel receptors (nAChRs) exist as several subtypes and are involved in a variety of functions and disorders of the central nervous system (CNS), such as Alzheimer's and Parkinson's diseases. The lack of reliable information on the 3D structure of nAChRs prompted us to focus efforts on pharmacophore and structure-affinity relationships (SAFIRs). The use of DISCO (DIStance COmparison) and Catalyst/HipHop led to the formulation of a pharmacophore that is made of three geometrically unrelated features: (i) an ammonium head involved in coulombic and/or H-bond interactions, (ii) a lone pair of a pyridine nitrogen or a carbonyl oxygen, as H-bond acceptor site, and (iii) a hydrophobic molecular region generally constituted by aliphatic cycles. The quantitative SAFIR (QSAFIR) study was carried out on about three hundred nicotinoid agonists, and coherent results were obtained from classical Hansch-type approach, 3D QSAFIRs, based on Comparative Molecular Field Analysis (CoMFA), and trade-off models generated by Multi-objective Genetic QSAR (MoQSAR), a novel evolutionary software that makes use of Genetic Programming (GP) and multi-objective optimization (MO). Within each congeneric series, Hansch-type equations revealed detrimental steric effects as the major factors modulating the receptor affinity, whereas CoMFA allowed us to merge progressively single-class models in a more global one, whose robustness was supported by crossvalidation, high prediction statistics and satisfactory predictions of the affinity data of a true external ligand set (r(2)(pred) = 0.796). Next, MoQSAR was used to analyze a data set of 58 highly active nicotinoids characterized by 56 descriptors, that are log P, MR and 54 low inter-correlated WHIM (Weighted Holistic Invariant Molecular) indices. Equivalent QSAFIR models, that represent different compromises between structural model complexity, fitting and internal model complexity, were found. Our attention was mostly engaged by a number of nonlinear QSAFIRs, which relate nAChR affinity with the log P and directional WHIM descriptors. The results reviewed herein show as QSAFIRs may helpfully complement the pharmacophores, thus enhancing the applicability of computer-aided methodologies in the field of nAChR agonists.

Traditional versus WHIM molecular descriptors in QSAR approaches applied to fish toxicity studies

Quantitative structure–activity relationships (QSAR) were performed on nine congenetic aromatic hydrocarbons. Acute response was evaluated in freshwater fish species. QSAR were built by Hansch's approaches and weighted holistic invariant molecular (WHIM) indices. The prediction power of QSAR from both approaches was evaluated. Single regression analysis derivated by Hansch's approach seem suitable for non-polar compounds. However, for all species, it has not a high predictive power ( Q 2 LOO) of the biological activity from only K ow as molecular descriptor. Multiple regression analysis obtained from WHIM descriptors showed Q 2 LOO higher than 80%, indicating that molecular descriptors have a prediction power greater than K ow.