GRID formalism for the comparative molecular surface analysis: application to the CoMFA benchmark steroids, azo dyes, and HEPT derivatives

Consensus-Based Pharmacophore Mapping for New Set of N-(disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides

A series of twenty-two novel N-(disubstituted-phenyl)-3-hydroxynaphthalene- 2-carboxamide derivatives was synthesized and characterized as potential antimicrobial agents. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[2-chloro-5-(trifluoromethyl)phenyl]-3-hydroxy- naphthalene-2-carboxamide showed submicromolar (MICs 0.16–0.68 µM) activity against methicillin-resistant Staphylococcus aureus isolates. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[4-bromo-3-(trifluoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide revealed activity against M. tuberculosis (both MICs 10 µM) comparable with that of rifampicin. Synergistic activity was observed for the combinations of ciprofloxacin with N-[4-bromo-3-(trifluoromethyl)phenyl]- and N-(4-bromo-3-fluorophenyl)-3-hydroxynaphthalene-2-carboxamides against MRSA SA 630 isolate. The similarity-related property space assessment for the congeneric series of structurally related carboxamide derivatives was performed using the principal component analysis. Interestingly, different distribution of mono-halogenated carboxamide derivatives with the –CF₃ substituent is accompanied by the increased activity profile. A symmetric matrix of Tanimoto coefficients indicated the structural dissimilarities of dichloro- and dimetoxy-substituted isomers from the remaining ones. Moreover, the quantitative sampling of similarity-related activity landscape provided a subtle picture of favorable and disallowed structural modifications that are valid for determining activity cliffs. Finally, the advanced method of neural network quantitative SAR was engaged to illustrate the key 3D steric/electronic/lipophilic features of the ligand-site composition by the systematic probing of the functional group.

Bioactivity of Methoxylated and Methylated 1-Hydroxynaphthalene-2-Carboxanilides: Comparative Molecular Surface Analysis

A series of twenty-six methoxylated and methylated N-aryl-1-hydroxynaphthalene- 2-carboxanilides was prepared and characterized as potential anti-invasive agents. The molecular structure of N-(2,5-dimethylphenyl)-1-hydroxynaphthalene-2-carboxamide as a model compound was determined by single-crystal X-ray diffraction. All the analysed compounds were tested against the reference strain Staphylococcus aureus and three clinical isolates of methicillin-resistant S.aureus as well as against Mycobacterium tuberculosis and M. kansasii. In addition, the inhibitory profile of photosynthetic electron transport in spinach (Spinacia oleracea L.) chloroplasts was specified. In vitro cytotoxicity of the most effective compounds was tested on the human monocytic leukaemia THP-1 cell line. The activities of N-(3,5-dimethylphenyl)-, N-(3-fluoro-5-methoxy-phenyl)- and N-(3,5-dimethoxyphenyl)-1-hydroxynaphthalene-2-carbox- amide were comparable with or even better than the commonly used standards ampicillin and isoniazid. All promising compounds did not show any cytotoxic effect at the concentration >30 µM. Moreover, an in silico evaluation of clogP features was performed for the entire set of the carboxamides using a range of software lipophilicity predictors, and cross-comparison with the experimentally determined lipophilicity (log k), in consensus lipophilicity estimation, was conducted as well. Principal component analysis was employed to illustrate noticeable variations with respect to the molecular lipophilicity (theoretical/experimental) and rule-of-five violations. Additionally, ligand-oriented studies for the assessment of the three-dimensional quantitative structure–activity relationship profile were carried out with the comparative molecular surface analysis to determine electron and/or steric factors that potentially contribute to the biological activities of the investigated compounds.

Design and synthesis of anticancer 1-hydroxynaphthalene-2-carboxanilides with a p53 independent mechanism of action

A series of 116 small-molecule 1-hydroxynaphthalene-2-carboxanilides was designed based on the fragment-based approach and was synthesized according to the microwave-assisted protocol. The biological activity of all of the compounds was tested on human colon carcinoma cell lines including a deleted TP53 tumor suppressor gene. The mechanism of activity was studied according to the p53 status in the cell. Several compounds revealed a good to excellent activity that was similar to or better than the standard anticancer drugs. Some of these appeared to be more active against the p53 null cells than their wild-type counterparts. Intercalating the properties of these compounds could be responsible for their mechanism of action.

Probability-driven 3D pharmacophore mapping of antimycobacterial potential of hybrid molecules combining phenylcarbamoyloxy and N-arylpiperazine fragments

The current study examines in silico characterization of the structure-inhibitory potency for a set of phenylcarbamic acid derivatives containing an N-arylpiperazine scaffold, considering the electronic, steric and lipophilic properties. The main objective of the ligand-based modelling was the systematic study of classical comparative molecular field analysis (CoMFA)/comparative molecular surface analysis (CoMSA) performance for the modelling of in vitro efficiency observed for these phenylcarbamates, revealing their inhibitory activities against a virulent Mycobacterium tuberculosis H₃₇R_v strain. We compared the findings of efficiency modelling produced by a standard 3D methodology (CoMFA) and its neural counterparts (CoMSA) regarding multiple training/test subsets and variables used. Moreover, systematic space inspection, splitting values into the analysed training/test subsets, was performed to monitor statistical estimator performance while mapping the probability-driven pharmacophore pattern. Consequently, a 'pseudo-consensus' 3D-quantitative structure-activity relationship (3D-QSAR) approach was applied to retrieve an 'average' pharmacophore hypothesis by the investigation of the most densely populated training/test subpopulations to specify the potentially important factors contributing to the inhibitory activity of phenylcarbamic acid analogues. In addition, examination of descriptor-based similarity with a principal component analysis (PCA) procedure was employed to visualize noticeable variations in the performance of these molecules with respect to their structure and activity profiles.

Towards Intelligent Drug Design System: Application of Artificial Dipeptide Receptor Library in QSAR-Oriented Studies

The pharmacophore properties of a new series of potential purinoreceptor (P2X) inhibitors determined using a coupled neural network and the partial least squares method with iterative variable elimination (IVE-PLS) are presented in a ligand-based comparative study of the molecular surface by comparative molecular surface analysis (CoMSA). Moreover, we focused on the interpretation of noticeable variations in the potential selectiveness of interactions of individual inhibitor-receptors due to their physicochemical properties; therefore, the library of artificial dipeptide receptors (ADP) was designed and examined. The resulting library response to individual inhibitors was arranged in the array, preprocessed and transformed by the principal component analysis (PCA) and PLS procedures. A dominant absolute contribution to PC1 of the Glu attached to heptanoic gating acid and Phe bonded to the linker m-phenylenediamine/triazine scaffold was revealed by the PCA. The IVE-PLS procedure indicated the receptor systems with predominant Pro bonded to the linker and Glu, Gln, Cys and Val directly attached to the gating acid. The proposed comprehensive ligand-based and simplified structure-based methodology allows the in-depth study of the performance of peptide receptors against the tested set of compounds.

Multidimensional (3D/4D-QSAR) probability-guided pharmacophore mapping: investigation of activity profile for a series of drug absorption promoters

A hybrid approach that combines 3D and 4D-QSAR methods based on grid and neural paradigms with automated IVE-PLS procedure was examined to identify the pharmacophore pattern for cholic acid derivatives as potential drug absorption promoters.

Study of lipophilicity of alpha-asarone derivatives and their deterrent activity against the Colorado potato beetle

Five positional isomers and thirty derivatives of alpha-asarone wereobtained as a bioactive set of substances, and their deterrent activity against the Colorado potato beetle was studied. The lipophilicity values of thirty-six compounds were determined by using experimental, chromatographic and theoretical methods. The logk w values for all compounds were determined by reversed-phase high-performance liquid chromatography using five different columns. The octan-1-ol/water partition coefficients values were determined experimentally as a measure of lipophilicity. For the purpose of comparison, theoretical lipophilic parameters for all compounds were calculated with the use of chemical programs. The experimental, chromatographic and calculated values were compared using linear regression analysis. The most significant correlations were found between the chromatographic lipophilicities using methanol/water for all tested columns and calculated values. It has been found that 13 compounds have the lowest lipophilicities, which correspond to deterrent activity. The presented results show that the computational method is useful for determining the lipophilicity of a series of homologues, but not for positional isomers. In addition, this study shows that determination of lipophilicity using HPLC with various stationary phases is practical and reasonable for all alpha-asarone derivatives.

Estimation of Anti-HIV Activity of HEPT Analogues Using MLR, ANN, and SVM Techniques

The present study deals with the estimation of the anti-HIV activity (log1/C) of a large set of 107 HEPT analogues using molecular descriptors which are responsible for the anti-HIV activity. The study has been undertaken by three techniques MLR, ANN, and SVM. The MLR model fits the train set with R²=0.856 while in ANN and SVM with higher values of R² = 0.850, 0.874, respectively. SVM model shows improvement to estimate the anti-HIV activity of trained data, while in test set ANN have higher R² value than those of MLR and SVM techniques. R_m² = metrics and ridge regression analysis indicated that the proposed four-variable model MATS5e, RDF080u, T(O⋯O), and MATS5m as correlating descriptors is the best for estimating the anti-HIV activity (log 1/C) present set of compounds.

Receptor independent and receptor dependent CoMSA modeling with IVE-PLS: application to CBG benchmark steroids and reductase activators

Comparative molecular surface analysis (CoMSA) with robust IVE-PLS variable elimination if tested for the benchmark CBG steroid series provides highly predictive RI 3D QSAR models, but failed however to model the activity of sulforaphane (SP) activators of quinone reductase. The application of the SP poses obtained from multipose molecular docking to model the RD IVE-PLS CoMSA resulted in a predictive form. This model indicated lipophilic potential as the activity determinant. The individual molecular surface areas of the highest contribution to the SP activity was identified and visualized by CoMSA contour plots.

Modeling robust QSAR

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

Modeling Robust QSAR. 2. Iterative Variable Elimination Schemes for CoMSA: Application for Modeling Benzoic Acid pKa Values

A variety of issues decide the efficiency of 3D QSAR methods, and their practical importance for drug design is still controversial. This refers both to the predictive ability and the possibility for the indication of these areas within 3D molecular representations that are responsible for biological or chemical effects. Technically, the latter comes down to the selection or elimination of the reliable variables during 3D QSAR modeling using the Partial Least-Squares (PLS) method. In this paper we used a series of benzoic acids to test the dependence between the predictive ability and variable selection performance of PLS with Iterative Variable Elimination (IVE-PLS) in the Comparative Molecular Surface Analysis (CoMSA) modeling of Hammett constant which correlates with the pKa values. Modeling this chemical effect allowed us to select the IVE-PLS variant that plots the contour maps indicating a carboxylic function, i.e., the region including the dissociation reaction center that determines the respective pKa values. In fact, it appeared that a novel robust IVE version is capable of the indication of the proper contour plots independent of the method used for the calculation of partial atomic charges (AM1 or Gasteiger-Marsili).

3D QSAR study of hypolipidemic asarones by comparative molecular surface analysis

Three-dimensional quantitative structure-activity relationship (3D QSAR) modeled for alpha-asarone derivatives using the comparative molecular surface analysis (CoMSA) allowed us to reveal a correlation between the activity of these compounds and the electrostatic potential at the molecular surface. The grid formalism (s-CoMSA) allowed us to indicate a pharmacophore that is of key importance for compound activity. The CoMSA formalism coupled with the iterative variable elimination method gives a highly predictive model.

Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation

In this paper we report a novel three-dimensional QSAR approach, kNN-MFA, developed based on principles of the k-nearest neighbor method combined with various variable selection procedures. The kNN-MFA approach was used to generate models for three different data sets and predict the activity of test molecules through each of these models. The three data sets used were the standard steroid benchmark, an antiinflammatory and an anticancerous data set. The study resulted in kNN-MFA models having better statistical parameters than the reported CoMFA models for all the three data sets. It was also found that stochastic methods generate better models resulting in more accurate predictions as compared to stepwise forward selection procedures. Thus, kNN-MFA method represents a good alternative to CoMFA-like methods.

Modeling robust QSAR. 1. Coding molecules in 3D-QSAR--from a point to surface sectors and molecular volumes

Shape analysis is a powerful tool in chemistry and drug design. In the current work, we compare the results of CoMFA and Comparative Molecular Surface Analysis (CoMSA), the 3D-QSAR method, for a series of hypolipidemic and antiplatelet asarones and antifungal N-myristoyltransferase inhibitors. In this publication we show that a sector CoMSA formalism enables an analysis of the biological activity that is more directly related to the molecular shape and individual molecular functionalities than the traditional uniform and directionless CoMFA field. Iterative Variable Elimination allowed us to identify the potential pharmacophoric sites. We modeled QSARs for both series and demonstrate that sector-based molecular descriptors give very predictive models and allow one to generate a spatial interpretation of the QSAR models. In particular, we identified the central aromatic ring and carbonyl functions as the moieties determining the activity of the asarones series, while the pattern of substitution of the aromatic ring determines the activity of N-myristoyltransferase inhibitors.

A 4D-QSAR study on anti-HIV HEPT analogues

We used the 4D-QSAR method coupled with the PLS analysis and uninformative variable elimination or its variants for the investigations of the antiviral activity of HEPT, a series of conformationally flexible molecules that bind HIV-1 reverse transcriptase. An analysis of several Hopfinger's and SOM-4D-QSAR models indicated that both methods yield comparable results. Generally, charge descriptors provide better modeling efficiency. We have shown that the method properly indicates the mode of interaction revealed by X-ray studies. It also allows us to calculate highly predictive QSAR models.

Self-organizing neural networks for modeling robust 3D and 4D QSAR: application to dihydrofolate reductase inhibitors

We have used SOM and grid 3D and 4D QSAR schemes for modeling the activity of a series of dihydrofolate reductase inhibitors. Careful analysis of the performance and external predictivities proves that this method can provide an efficient inhibition model.