Molecular Similarity Indices Analysis Research Articles

3D-QSAR, molecular docking, ADMET, simulation dynamic, and retrosynthesis studies on new styrylquinolines derivatives against breast cancer

Abstract Breast cancer is the most common illness among women, accounting for 25% of all diagnoses, as stated by the American Cancer Society. Current research focuses on 43 compounds of styrylquinoline derivatives as potential inhibitors of tubulin to design a new drug that could potentially be effective against breast cancer cells in humans. The target compounds were subjected to a three-dimensional quantitative structure-activity relationship/comparative molecular similarity indices analysis (CoMSIA) approach, where CoMSIA models were used; the best results obtained are (Q 2 = 0.84, R 2 = 0.97, r ext 2 {r}_{\text{ext}}^{2} = 0.91), H-bond acceptor field was discovered to be important for increasing inhibitory activity by examining the contour maps (54%), and it plays a key role in the prediction of anticancer activity. Based on the contour maps of the CoMSIA models, we obtained information that allows us to propose four new molecules with higher cancer inhibitory than the 43 compounds found in the literature. The molecular docking was applied to determine the likely types of binding between the tubulin protein (PDB ID: 4O2B) and the proposed compounds, and the results show that M1 has a higher total score of 6.53 and two interactions with important conventional hydrogen bond type, followed by compound M2 with a total score of 5.74. Furthermore, the designed molecules showed better pharmacokinetic properties based on absorption, distribution, metabolism, excretion, and toxicity properties. Molecular dynamics simulations at 100 ns were conducted to confirm the binding stability of the selected ligands (M1 and M2) with tubulin protein. The simulation parameters used in the current study are root mean square deviation, root mean square fluctuation, H-bond, Rg, solvent accessible surface area, and binding energy. As a result, the designed compounds (ligands M1 and M2) have shown noteworthy potential as a drug candidate for experimental in vivo and in vitro testing due to their potential inhibition of breast cancer. Finally, the study of retrosynthesis in this work facilitates the synthesis of drug candidates.

Computational Prediction of 3,5-Diaryl-1H-Pyrazole and spiropyrazolines derivatives as potential acetylcholinesterase inhibitors for alzheimer disease treatment by 3D-QSAR, molecular docking, molecular dynamics simulation, and ADME-Tox

The efficacy of 40 synthesized variants of 3,5-diaryl-1H-pyrazole and spiropyrazoline’ derivatives as acetylcholinesterase inhibitors is verified using a quantitative three-dimensional structure-activity relationship (3D-QSAR) by comparative molecular field analysis (CoMFA) and molecular similarity index analysis (CoMSIA) models. In this research, different field models proved that CoMSIA/SE model is the best model with high predictive power compared to several models (Qved2 = O.65; R2 = 0.980; R2test = 0.727). Also, contour maps produced by CoMSIA/SE model have been employed to prove the key structural needs of the activity. Consequently, six new compounds have been generated. Among these compounds, M4 and M5 were the most active but remained toxic and had poor absorption capacities. While the M1, M2, M3 and M6 remained highly active while respecting ADMET's characteristics. Molecular docking results showed compound M2 better with acetylcholinesterase than compound 22. The interactions are classical hydrogen bonding with residues TYR:124, TYR:72, and SER:293, which play a critical role in the biological activity as AChE inhibitors. MD results confirmed the docking results and showed that compound M2 had satisfactory stability with (ΔGbinding = −151.225 KJ/mol) in the active site of AChE receptor compared with compound 22 (ΔGbinding = −133.375 KJ/mol). In addition, both compounds had good stability regarding RMSD, Rg, and RMSF. The previous results show that the newly designed compound M2 is more active in the active site of AChE receptor than compound 22. Communicated by Ramaswamy H. Sarma

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

Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) on a Series of Piperazine-Carboxamides Fatty Acid Amide Hydrolase (FAAH) Inhibitors as a Useful Tool for the Design of New Cannabinoid Ligands.

Fatty Acid Amide Hydrolase (FAAH) is one of the main enzymes responsible for endocannabinoid metabolism. Inhibition of FAAH increases endogenous levels of fatty acid ethanolamides such as anandamide (AEA) and thus consitutes an indirect strategy that can be used to modulate endocannabinoid tone. In the present work, we present a three-dimensional quantitative structure-activity relationships/comparative molecular similarity indices analysis (3D-QSAR/CoMSIA) study on a series of 90 reported irreversible inhibitors of FAAH sharing a piperazine-carboxamide scaffold. The model obtained was extensively validated (q2 = 0.734; r2 = 0.966; r2m = 0.723). Finally, based on the information derived from the contour maps we designed a series of 10 new compounds with high predicted FAAH inhibition (predicted pIC50 of the best-proposed compounds = 12.196; 12.416).

2D-QSAR and 3D-QSAR/CoMSIA Studies on a Series of (R)-2-((2-(1H-Indol-2-yl)ethyl)amino)-1-Phenylethan-1-ol with Human β₃-Adrenergic Activity.

The β3 adrenergic receptor is raising as an important drug target for the treatment of pathologies such as diabetes, obesity, depression, and cardiac diseases among others. Several attempts to obtain selective and high affinity ligands have been made. Currently, Mirabegron is the only available drug on the market that targets this receptor approved for the treatment of overactive bladder. However, the FDA (Food and Drug Administration) in USA and the MHRA (Medicines and Healthcare products Regulatory Agency) in UK have made reports of potentially life-threatening side effects associated with the administration of Mirabegron, casting doubts on the continuity of this compound. Therefore, it is of utmost importance to gather information for the rational design and synthesis of new β3 adrenergic ligands. Herein, we present the first combined 2D-QSAR (two-dimensional Quantitative Structure-Activity Relationship) and 3D-QSAR/CoMSIA (three-dimensional Quantitative Structure-Activity Relationship/Comparative Molecular Similarity Index Analysis) study on a series of potent β3 adrenergic agonists of indole-alkylamine structure. We found a series of changes that can be made in the steric, hydrogen-bond donor and acceptor, lipophilicity and molar refractivity properties of the compounds to generate new promising molecules. Finally, based on our analysis, a summary and a regiospecific description of the requirements for improving β3 adrenergic activity is given.

The 3D-QSAR and pharmacophore studies of pyrimidine derivatives as HCV replication (replicase) inhibitor

Three-dimensional quantitative structure activity relationship has been carried out on a series of 211 pyrimidine derivatives as HCV replication (replicase) inhibitors to identify the chemical structural impacting the biological activities. The QSAR models based on 157 compounds screened as the training set and the rest as the test set were established by comparative molecular field analysis and molecular similarity indices analysis (CoMSIA) methods. The optimal CoMSIA model exhibits both satisfying internal and external prediction performance with q2 = 0.501, \( R^{ 2}_{\text{ncv}} \; = \; 0. 9 6 2 \), \( R^{ 2}_{\text{pred}} \; = \; 0. 8 8 3 \). In addition, the 3D contour map analysis and the pharmacophoric features identified from corresponding pharmacophore study point out the key structural features that affect the inhibition activity of the pyrimidine analogs that (1) bulky substituents at position 11 and 23 are unfavorable; (2) minor positive 16 charges and H-bond donor groups at this position are benefit while H-bond acceptor near this position are detrimental; (3) substituents near Ring B: minor H-bond donor, H-bond acceptor groups are favorable; (4) electro-negative charges at position 7 and 9 are favorable while H-bond donor at position 9 are unfavorable; (5) two H-bond donor sites (DS1, DS2), two H-bond donor atoms (DA1, DA2); two H-bond acceptor atoms (AA1, AA2), hydrophobic and aromatic, and aromatic centers account for the pharmacophore features of the model.

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.

3D-QSAR Study on Toxicities of Substituted Phenols against Vibrio Qinghaiensis(Q67)

To investigate the relationships between structures and toxicities of 16 substituted phenols against vibrio qinghaiensis (Q67), 3D-QSAR models were proposed by using comparative molecular field analysis (CoMFA) and molecular similarity index analysis (CoMSIA). The results suggest that the steric field of substituted group is the dominating factor for the toxicity. Two obtained models show fine stabilities and predictive abilities. Comaprably, the prediction ability of CoMFA model is slightly more advantageous than that of CoMSIA, which both can be used to predict the toxicity of these kinds of compounds, even to provide further theoretical guide about biological toxic mechanism of substituted phenols.

Prediction of improved antimicrobial mastoparan derivatives by 3D-QSAR-CoMSIA/CoMFA and computational mutagenesis

Antimicrobial peptides are an important class of therapeutic agents used against a wide range of pathogens such as gram-negative and -positive bacteria, fungi, and viruses. The minimal inhibitory concentration at the level of the pathogen membrane is a major determinant of the pharmacokinetic behavior and, consequently, it can affect their antimicrobial activity. Here we generated quantitative structure-activity relationship models (3D-QSAR—comparative molecular field analysis/comparative molecular similarity indices analysis) using a database of 33 mastoparan analogs, antimicrobial peptides with known experimental activity, and further used these models to predict the minimal inhibitory concentration for 18 new mastoparan analogs, obtained by computational mutagenesis. We discuss two options for structural alignment of mastoparan analogs: superposition of Cα trace atoms or superposition of all backbone atoms. Significant values of the cross-validated correlation q 2 (higher than 0.60) and the fitted correlation r 2 (higher than 0.90) of our models indicate that they are reliable enough for activity prediction in the case of new derivatives. This allows us to identify compounds with possibly enhanced antimicrobial activity against Bacillus subtilis, which are suggested for further experimental studies.

Three-Dimensional Quantitative Structure-Activity Relationships of flavonoids and estrogen receptors based on docking

Flavonoids are endocrine disrupting compounds that occur ubiquitously in foods of plant origin. The Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) model based on ligand-receptor docking is established between 20 flavonoids and estrogen receptor alpha (ERα), which may provide further theoretical basis for research on the relationship between flavones and estrogen. Comparative molecular field analysis (CoMFA) was employed and the best results of cross-validation and non cross validation were 0.845 and 0.988, respectively. Correspondingly, molecular similarity index analysis (CoMSIA) was employed and the results of cross-validation and non cross validation were 0.670 and 0.990, respectively. The CoMFA/CoMSIA and docking results reveal the structural features for estrogen activity and key amino acid residues in binding pocket, and provide an insight into the interaction between the ligands and these amino acid residues.

黄酮类化合物与雌激素受体作用的三维定量构效关系

Flavonoids are endocrine disrupting compounds that occur ubiquitously in foods of plant origin. The 3D-QSAR model based on ligand-receptor docking is established between 20 flavonoids and estrogen receptor alpha (ERα), which may provide further theoretical basis for researching relationship between flavones and estrogen. Comparative molecular field analysis (CoMFA) was employed and the best result of cross-validation and non cross validation were 0.845 and 0.988, respectively. Correspondingly, molecular similarity index analysis (CoMSIA) was employed and the result of cross-validation and non cross validation were 0.670 and 0.990, respectively. The CoMFA /CoMSIA and docking results revealed the structural features for estrogen activity and key amino acid residues in binding pocket, and provided an insight into the interaction between the ligands and these amino acid results.

3D-QSAR studies of NAIMS analogs as anti-HIV agents

Human immunodeÞ ciency virus (HIV) is the causative agent of acquired immunodeÞ ciency syndrome (AIDS), a disease which has claimed twenty million lives worldwide in the last two decades. Recently imidazole derivatives, such as N-aminoimidazoles (NAIMS), have been discovered as novel anti-HIV agents. Herein, the results of comparative molecular Þ eld analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) analysis on these inhibitors have been reported. The CoMFA model produced statistically signiÞ cant results with crossvalidated, conventional, and predictive correlation coefÞ cients of 0.517, 0.965, and 0.47, respectively. The combination of electrostatic hydrophobic and donor Þ elds in CoMSIA gave the best results with cross-validated, conventional and predictive correlation coefÞ cients of 0.532, 0.930, and 0.80, respectively. The information obtained from CoMFA and CoMSIA models may be utilized in the design of more potent NAIMS analogs and anti-HIV agents. Key words: Acquired immune deÞ ciency syndrome, comparative molecular Þ eld analysis, comparative molecular similarity indices, human immunodeÞ ciency virus, N-aminoimidazoles, 3D-QSAR.

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.