Molecular Similarity Indices Research Articles

Potent VEGFR-2 inhibitors for resistant breast cancer: a comprehensive 3D-QSAR, ADMET, molecular docking and MMPBSA calculation on triazolopyrazine derivatives

More people are being diagnosed with resistant breast cancer, increasing the urgency of developing new effective treatments. Several lines of evidence suggest that blocking the kinase activity of VEGFR-2 reduces angiogenesis and slows tumor growth. In this study, we developed novel VEGFR-2 inhibitors based on the triazolopyrazine template by using comparative molecular field analysis (CoMFA) and molecular similarity indices (CoMSIA) models for 3D-QSAR analysis of 23 triazolopyrazine-based compounds against breast cancer cell lines (MCF -7). Both CoMFA (Q2 = 0.575; R2 = 0.936, Rpred2 = 0.956) and CoMSIA/SE (Q2 = 0.575; R2 = 0.936, Rpred2 = 0.847) results demonstrate the robustness and stability of the constructed model. Six novel compounds with potent inhibitory activity were carefully designed, and screening of ADMET properties revealed their good oral bioavailability and ability to diffuse through various biological barriers. When compared with the most active molecule in the data set and with Foretinib (breast cancer drug), molecular docking revealed that the six designed compounds had strengthened affinity (−8.9 to −10 kcal/mol) to VEGFR-2. Molecular Dynamics Simulations and MMPBSA calculations were applied to the selected compound T01 with the highest predicted inhibitory activity, confirming its stability in the active pocket of VEGFR-2 over 100 ns. The present results provided the basis for the chemical synthesis of new compounds with improved inhibitory properties against the breast cancer cell line (MCF -7).

Profilin 3 genetic architecture in glioma formalin fixed paraffin embedded (FFPE) archive.

Pfn3 is an intron-less gene, encoding actin binding protein that affects structure of cytoskeleton. Although, Pfn3 is mentioned in Allen Brain Atlas and in adult and prenatal Human Brain Tissue Gene Expression Profiles dataset, however, no report on brain and/or brain tumor associated Pfn3 nucleotide sequences are available in the databases. Moreover, pfn3 and pfn4 are always considered as testicular specific genes. The current study explored transcriptional expression profile and genetic architecture of pfn3 in a cohort of fifty formalin fixed paraffin embedded (FFPE) human glioma archive tissues. Results of designed study highlighted the significant dysregulated transcriptional pattern of pfn3. Molecular similarity index indicated 97% in nucleotide and 93% homology in protein sequences (with clear differences in nine amino acid residues). Thus, molecular variations in the pfn3 may be corelated with the malignancy of brain tumors, as previously, pfn1 and pfn2 were reported as tumor suppressor genes in other types of cancer.

In silico exploration of hydroxylated polychlorinated biphenyls as estrogen receptor β ligands by 3D-QSAR, molecular docking and molecular dynamics simulations

Hydroxylated polychlorinated biphenyls (HO-PCBs), as the major metabolites of PCBs, have been reported to act as estrogen receptor β (ERβ) agonists. However, the chemical–biological interactions governing their activities toward ERβ have not been elucidated. Therefore, three dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations, to the best of our knowledge, for the first time were performed to explore the correlation between the structures and activities. The best 3D-QSAR model presented higher predictive ability (R 2 cv=0.543, R 2 pred=0.5793/R 2 cv=0.543, R 2 pred=0.6795) based on comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), respectively. At the same time, the derived contour maps indicated the important structural features required for improving the activity. Furthermore, molecular docking studies and MD simulations predicted the binding mode and the interactions between the ligand and the receptor. All the results would lead to a better understanding of the specific mechanism of HO-PCBs on estrogen receptor β (ERβ). Communicated by Ramaswamy H. Sarma

3D-QSAR and docking study on 3-benzimidazol-2-ylhydroquinolin-2-one derivatives as VEGFR-2 tyrosine kinase inhibitors

The present study is an attempt to formulate the three-dimensional quantitative structure–activity relationship (3D-QSARs) modeling of 3-benzimidazol-2-ylhydroquinolin-2-one derivatives inhibiting vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase. The 3D-QSARs were established for 36 3-benzimidazol-2-ylhydroquinolin-2-one derivatives as VEGFR-2 tyrosine kinase inhibitors using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) techniques. The negative logarithm of IC50 (pIC50) was used as the biological activity in the 3D-QSAR study. With the CoMFA model, the cross-validated value (q2) was 0.516, the non-cross-validated value (R2) was 0.927, and the external cross-validated value (Qext2) was 0.855; with the CoMSIA model, the corresponding q2, R2, and Qext2 values were 0.538, 0.980, and 0.809, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of nine compounds. Then, molecular docking was carried out to better understand of the interactions between VEGFR-2 tyrosine kinase target and inhibitors. Finally, based on results of the structure–activity relationship and of the molecular docking, seven VEGFR-2 tyrosine kinase inhibitors that showed excellent potencies have been constructed.

Pharmacophore elucidation and 3D-QSAR analysis of a new class of highly potent inhibitors of acid ceramidase based on maximum common substructure and field fit alignment methods

Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) studies have been carried on a series of 2,4-dioxopyrimidine-1-carboxamides as acid ceramidase inhibitors. Two alignment rules for the compounds were defined using maximum common substructure and field fit. The best orientation was then searched by all-orientation search strategy, to minimize the effect of the initial orientation of the structures. The Kennard Stone algorithm was used to divide the entire set into training (25 compounds) and test (7 compounds) sets. Pharmacophore model identification was also performed using DISCOtech algorithm and refinement was carried out using GASP, to highlight important structural features that could be responsible for the inhibitory activity. All constructed models showed appropriate statistical parameters in terms of q2 and rpred2. Based upon the information obtained from CoMFA, CoMSIA, and developed pharmacophore pattern, some key features that may be used to design new inhibitors for acid ceramidase have been identified.

QSAR and Molecular Docking on Five-Membered Heterocyclopyrimidines as Thymidylate Synthase Inhibitors

The three-dimensional quantitative structure-activity relationships(3D-QSAR) were established for 38 five-membered heterocyclopyrimidine thymidylate synthase inhibitors by using comparative molecular field analysis(CoMFA) and comparative similarity indices analysis(CoMSIA) techniques.With the CoMFA model,the cross-validated value(q~2) was 0.662,the non-cross-validated value(R~2) was 0.921,and the external cross-validated value(Q_(ext)~2) was 0.85.And with the CoMSIA model, the corresponding q~2,R~2,and Q_(ext)~2 values were 0.672,0.884,and 0.81,respectively.The mode of action obtained by molecular docking was in agreement with the 3D-QSAR results.The results revealed that both models have good predictive capability to guide the design and structural modification of homologic compounds.Furthermore,these results also establish a base level for further research and development of new thymidylate synthase inhibitors.

Quantum similarity studies on the antimalarial agent Artemisinin

In this work, a QSAR analysis of antimalarial activities of a set of 21 ester and ether derivatives of dihydroartemisinin is performed by employing molecular quantum similarity indexes as descriptors. The QSAR model deals with the IC$_{50}$ biological response of the parasite P. falciparum, and is constructed employing a partial least square method from the quantum similarity results used as molecular descriptors, finding that a satisfactory quantitative model is obtained. A statistical analysis is performed to elucidate the relevant structural features in the biological activity, revealing the importance of the lactol oxygen conformation in dihydroartemisin.

Three-dimensional quantitative structure activity relationships of quorum-sensing and biofilm inhibitors in gram-negative bacteria

Thirty N-acyl homoserine lactone (AHL) analogs with variable antibacterial activity and displaying inhibition of biofilm formation were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) were carried out to determine the optimum structural requirements for selectivity and potency of quorum-sensing and bacterial biofilm inhibition. The best CoMFA model predicted a q2 value of 0.519 and an r2 value of 0.984 and revealed that electrostatic and steric properties play a significant role in potency and selectivity. The CoMSIA model predicted a q2 value of 0.411 and an r2 value of 0.938 based on a combination of steric, electrostatic, and hydrophobic effects. The analysis of the contour maps from each model provide insight into the structural requirements for increasing the activity of a compound. Consequently, manipulating the chemical and physical properties of substituted acyl groups on the homoserine lactone moiety can provide important information toward enhancing the antibacterial properties of the target chemical compound.

3D-QSAR of fungal quorum-sensing inhibiting analogs of farnesol

Thirty-four analogs with variable antifungal activity were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analyses (CoMSIA) were conducted on the group of analogs to determine the structural requirements for selectivity and potency in inhibiting biofilm formation and fungal growth. The best CoMFA model predicted a q2 = 0.5 and an r2 = 0.991, and revealed that electrostatic properties play a significant role in potency and selectivity. The best CoMSIA model combined electrostatics, hydrogen bond acceptor and donor, and hydrophobic fields with a q2 = 0.664 r2 = 0.952, S = 0.099, and F = 139.892. The analyses of the contour maps from both models provide significant insight into the structural necessities for a potent compound. Therefore, manipulating various chemical properties of the substituted groups on the farnesol chain can be used to enhance the fungicidal properties of the target compound.

Computer-aided molecular design of highly potent HIV-1 RT inhibitors: 3D QSAR and molecular docking studies of efavirenz derivatives

Ligand- and structure-based design approaches have been applied to an extended series of 74 efavirenz compounds effectively inhibiting wild type (WT) and mutant type (K103N) HIV-1 reverse transcriptase (RT). For ligand-based approach, three dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), were performed. The starting geometry of efavirenz was obtained from X-ray crystallographic data. The efavirenz derivatives were constructed and fully optimized by ab-initio molecular orbital method at HF/3-21G level. Reliable QSAR models for high predictive abilities were developed. Regarding WT and K103N inhibitions, CoMFA models with = 0.651 and 0.678 and CoMSIA models with = 0.662 and 0.743 were derived, respectively. The interpretation obtained from the models highlights different structural requirements for inhibition of WT and K103N HIV-1 RT. To elucidate potential binding modes of efavirenz derivatives in the binding pocket of WT and K103N HIV-1 RT, structure-based approach based on computational docking studies of selected efavirenz compounds were performed by using GOLD and FlexX programs. The results derived from docking analysis give additional information and further probe the inhibitor-enzyme interactions. The correlation of the results obtained from 3D QSAR and docking models validate each other and lead to better understanding of the structural requirements for the activity. Therefore, these integrated results are informative to provide key features and a helpful guideline for novel compound design active against HIV-1 RT.

Molecular Quantum Similarity Analysis of Estrogenic Activity.

The main objective of this study was to evaluate the capability of 120 aromatic chemicals to bind to the human alpha estrogen receptor (hERα) by the use of quantum similarity methods. The experimental data were segregated into two categories, i.e., those compounds with and without estrogenicity activity (active and inactive). To identify potential ligands, semiquantitative structure−activity relationships were developed for the complete set correlating the presence or lack of binding affinity to the estrogen receptor with structural features of the molecules. The structure−activity relationships were based upon molecular similarity indices, which implicitly contain information related to changes in the electron distributions of the molecules, along with indicator variables, accounting for several structural features. In addition, the whole set was split into several chemical classes for modeling purposes. Models were validated by dividing the complete set into several training and test sets to allow for e...

Molecular quantum similarity analysis of estrogenic activity.

The main objective of this study was to evaluate the capability of 120 aromatic chemicals to bind to the human alpha estrogen receptor (hER alpha) by the use of quantum similarity methods. The experimental data were segregated into two categories, i.e., those compounds with and without estrogenicity activity (active and inactive). To identify potential ligands, semiquantitative structure-activity relationships were developed for the complete set correlating the presence or lack of binding affinity to the estrogen receptor with structural features of the molecules. The structure-activity relationships were based upon molecular similarity indices, which implicitly contain information related to changes in the electron distributions of the molecules, along with indicator variables, accounting for several structural features. In addition, the whole set was split into several chemical classes for modeling purposes. Models were validated by dividing the complete set into several training and test sets to allow for external predictions to be made.

Looking for an Unambiguous Geometrical Definition of Organic Series from 3‐D Molecular Similarity Indices.

Looking for an Unambiguous Geometrical Definition of Organic Series from 3‐D Molecular Similarity Indices.

Looking for an Unambiguous Geometrical Definition of Organic Series from 3-D Molecular Similarity Indices

A mathematically consistent definition of series was derived by the application of Principal Component Analysis to 3-D similarity indices (ASP Software). For two data sets of aromatic molecules, a mono-dimensional numerical ordering was derived, corresponding to the consensus distance from the series lead. The series is unambiguously defined in terms of location along the mono-dimensional axis, along which shape and electrostatic features of the molecules vary in a coordinated way. Molecules along the axis are characterized by the same pattern of electrostatic potential and shape shared by the lead compound, of which they represent linearly "shifted" replicas. This approach can contribute to the exploration of the chemical spaces from a local "fine grain" perspective, thus complementing the "coarse grain" descriptions normally used in the analysis of large data sets.

Can molecular similarity-activity models for intravenous general anaesthetics help explain their mechanism of action?

The importance of molecular shape and electrostatic potential in determining the activities of 11 structurally-diverse i.v. general anaesthetics was investigated using computational chemistry techniques. The free plasma anaesthetic concentrations that abolished the response to noxious stimulation were obtained from the literature. The similarities in the molecular shapes and electrostatic potentials of the agents to eltanolone (the most potent anaesthetic agent in the group) were calculated using Carbo indices, and correlated with in vivo potency. The best model obtained was based on the similarities of the anaesthetics to two eltanolone conformers (r2=0.820). This model correctly predicted the potencies of the R- and S-enantiomers of ketamine, but identified alphaxalone as an outlier. Exclusion of alphaxalone substantially improved the activity correlation (r2=0.972). A bench mark model based on octanol/water partition coefficients (r2=0.647) failed to predict the potency order of the ketamine enantiomers. The results demonstrate that a single activity model can be formulated for chiral and non-chiral i.v. anaesthetic agents using molecular similarity indices.

Deriving a quantitative chirality measure from molecular similarity indices.

A versatile new method has been developed as a continuous symmetry measure for chiral compounds. The application of principal component analysis (PCA) to the complete N x N pairwise similarity matrices (electrostatic potential and shape indices) of a series of dihydropyridine calcium channel antagonists allowed to single out a chirality component and to compute a chirality score in terms of the between-enantiomers difference on the component value. The possibility to have chirality defined continuously at the series level could be of importance in eudismic analyses where the relative potency of two enantiomers is studied as well as in QSAR studies dealing with chiral molecules in order to improve the power of the generated models.

Three-Dimensional Structure-Activity Relationships of Synthetic Pyrethroids: 1. Similarity in Bioactive Conformations and Their Structure-Activity Pattern

The three-dimensional “bioactive” conformations of various types of pyrethroid insecticides were deduced from among optimized conformations which were generated on the basis of the exhaustive conformational exploration of the reference compound, MTI-800. A “folded” rather than an “extended” conformation was proposed to be the “bioactive structure” of pyrethroid molecules included in this study. The folded “bioactive” structures were further analyzed in terms of molecular (dis)similarity indices to elucidate the effect of the “local” structural variations on the activity. The results indicate that the overall bioactive conformation as well as the local 3D similarity of the molecule are important in governing the potency variations in the insecticidal activity.

QSAR Studies of Antiviral Agents Using Molecular Similarity Analysis and Structure-Activity Maps

Quantitative structure-activity relationships (QSAR) were developed for nucleoside analogs with anti-HIV activity. These compounds were investigated to determine the correlation of structure and toxicity/activity using molecular similarity analysis and structure-activity maps. A multiple-formula approach was used to perform quantitative molecular similarity analysis (QMSA) and QSAR study. Molecular descriptors such as number of atoms and bonds of a molecule (NAB), maximum common substructure (MaCS), and molecular similarity index (MSI) were used in our structure-activity relationship study. The MaCS of two molecules is defined as the substructure with the greatest NAB value common to both molecules. The MSI of two molecules X and Y is defined as MSI (X,Y) = [MaCS (X,Y) /NAB (X) ] X [MaCS (X,Y) /NAB (Y) ]. MaCS and MSI quantify the similarity between two molecular structures. Structure-activity maps (structure-toxicity map and structure-antiviral map) and QMSA were used to determine the site and type of modification for reduced toxicity and improved activity of new compounds.

Optimization of Carb� molecular similarity index using gradient methods

A steepest descent method for optimizing the Carbó molecular similarity index was implemented and evaluated. Comparisons were made between this procedure and the extensively used simplex method. Several data sets were considered, and in each case the gradient method showed a substantial improvement in the time taken for the optimization to converge while comparable similarity values were obtained. In some cases, performance enhancements of up to an order of magnitude were observed. © 1997 by John Wiley & Sons, Inc.

On Flexible Fitting Using The Molecular Similarity Index. A neural network analysis for antileishmaniasis compounds

AbstractPentamidine analogues bind to the minor groove by using the pharmacophoric conformation as being isohelical do DNA. A QSAR model has disclosed three physiochemical descriptors as being important for such compounds against Leishmania mexicana amazonensis: Clog P, electrotopological index and Carbo similarity index. A comparison of the QSAR equation derived from multiple regression analysis, MRA, and the one from neural network analysis, NN, shows the latter to be of higher significance.