Dimensional Quantitative Structure-activity Relationship Research Articles

3D-QSAR, Molecular Docking and Pharmacokinetic Studies: In-Silico Approach to Search Novel Inhibitors of 5-Alpha Reductase for Treatment of Benign Prostatic Hyperplasia

Aim:To identify novel steroidal 5-alpha reductase (5AR) inhibitors using computational approaches.Objectives:To exploit the steroidal nuclei for possible modifications by creating a library of 17-oximino-5-androsten-3-carboxamide derivatives and identify potent 5AR inhibitors based on docking and pharmacokinetic parameters.Background:Benign prostatic hyperplasia (BPH), is a condition of aged men, that is characterized by lower urinary tract symptoms. Excessive production of dihydrostestosterone (DHT) from testosterone has been found to play a major role in its pathophysiology. Studies targeting the 5AR enzyme have so far resulted in the development of two clinical approved 5AR inhibitors.Methods:Atom-based three dimensional-quantitative structure activity relationship (3D-QSAR) models have been developed using a selected series of steroidal derivatives as 5AR inhibitors, to elucidate the structural properties required for 5AR inhibitory activities. Further In‒silico studies (molecular docking and pharmacokinetic properties like adsorption, distribution, metabolism, and excretion) of 17-oximino-5-androsten-3-carboxamide derivatives have also been carried out to identify the binding orientation and protein-ligand interactions responsible for the exhibited activity and drug like properties.Results:The best 3D-QSAR model was generated using Partial Least Square method with an excellent correlation coefficient (R², training set) of 0.882, standard deviation (SD) of 0.09, and a predicted coefficient (Q², test set) of 0.814. Docking analysis indicated that the designed series of compounds have comparable binding affinity from -8.961 to -8.017 to the protein and suggested that hydrophobic and electrostatic moieties can have a key role in the inhibition mechanism.Conclusion:3D-QSAR, molecular docking and pharmacokinetic studies indicated that 17-oximino-5-androsten-3-carboxamide derivatives could be studied further to provide a new strategy for the treatment of BPH.

2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents

ObjectivesNon-small cell lung cancer (NSCLC) is the most common type of lung cancer, with nearly 2 million diagnoses and a 17% 5-year survival rate. The aim of this study was to use computer-aided techniques to identify potential therapeutic agents for NSCLC. MethodsThe two dimensional-quantitative structure–activity relationship (2D-QSAR) modeling was employed on some potential NSCLC therapeutic agents to develop a highly predictive model. Molecular docking-based virtual screening were conducted on the same set of compounds to identify potential hit compounds. The pharmacokinetic features of the best hits were evaluated using SWISSADME and pkCSM online web servers, respectively. ResultsThe model generated via 2D-QSAR modeling was highly predictive with R2= 0.798, R2adj = 0.754, Q2CV = 0.673, R2 test = 0.531, and cRp2 = 0.627 assessment parameters. Molecular docking-based virtual screening identified compounds 25, 32, 15, 21, and 23 with the highest MolDock scores as the best hits, of which compound 25 had the highest MolDock score of −138.329 kcal/mol. All of the identified hits had higher MolDock scores than the standard drug (osimertinib). The best hit compounds were ascertained to be drug-like in nature following the Lipinski’s rule of five. Also, their ADMET features displayed average pharmacokinetic profiles. ConclusionAfter successful preclinical testing, the hit compounds identified in this study may serve as potential NSCLC therapeutic agents due to their safety and efficacy with the exception of compound 23, which was found to be toxic. They can also serve as a template for designing novel NSCLC therapeutic agents.

QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP (QSAR) STUDY OF LIVER TOXIC DRUGS

Drug-induced liver injury (DILI) is one of the most severe adverse effects (AEs) causing life-threatening conditions, such as acute liver failure. t has also been recognized as the single most common cause of safety- related post-market withdrawals or warnings Due to the nature and idiosyncrasy of clinical forms of DILI, attempts to develop new predictive approaches to evaluate the risk of a medication being a hepatotoxicant have been difficult. The FDA Adverse Event Reporting System (AERS) provides post-market data illustrating AE morbidity. A quantitative structure –activity relationship (QSAR) model for DILI prediction with satisfactory output is urgently needed. In this study, we documented a high-quality QSAR model for predicting the hepatotoxicity risk of DILI by integrating the use of eight effective classifiers and molecular descriptors given by the VlifeMds program. For the present QSAR study, data set of 99 compounds (withdrawn and approved drugs) collected from different databases were taken. Multiple linear regression and partial least square analysis methods had developed two dimensional QSAR models, and then validated for internal and external predictions. The 2D QSAR model developed was statistically important, and was highly predictive. The validation methods presented essential statistical parameters that proved the model's predictive ability. The developed 2D QSAR model revealed the significance of SsssCE-index, SsOHcount, SsssNcount and SdssPcount descriptors. These findings will prove to be an important guide for furtherdesigning and developing new hepatotoxicity activity.

3D-QSAR, ADMET and Docking Studies for Design New 5,5-Diphenylimidazolidine-2,4-dione Derivatives Agents Against Cervical Cancer

Cancer is one of the world's causes of death, which requires the discovery of new molecules likely to become anticancer drugs. In this study, a three–dimensional quantitative structure-activity relationship is employed to study eighteen compounds of 5,5-diphenylimidazolidine-2,4-dione derivatives against cancer cell lines HeLa, their pIC50 varied from 3.62 to 5.00. In addition, the 3D-QSAR model was defined on the basis of Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices (CoMSIA) analysis, the model achieved strong predictability with the model CoMFA is (Q 2 =0.70; R 2 =0.94; r 2 test =0.96) and the best one on CoMSIA (Q 2 =0.73; R 2 =0.97; r 2 test = 0.98), respectively. We have proposed four compounds with highly potent anticancer predicted activities, based on successful results obtained by the contour maps formed by the method model. Furthermore, the ADMET properties of these newly designed compounds were in silico evaluated, among which two derivatives have respected these properties. These compounds were further evaluated by molecular docking, showing that two molecules T2 and T4 exhibit favorable interactions with the targeted receptor and a high total score. These findings may afford valuable more information to design compounds anticancer activity against Hela cells. DOI: http://dx.doi.org/10.17807/orbital.v14i1.1659

Molecular structural investigations of quinoxaline derivatives through 3D-QSAR, molecular docking, ADME prediction and pharmacophore modeling studies for the search of novel antimalarial agent

In the present article, a dataset of 63 quinoxaline derivatives were taken for antimalarial activity and pharmacophore were developed. Atom based method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. On comparison of all statistical parameters, model AHRRR23 was found to be the most effective and predictive QSAR model as it satisfied all statistical parameters of a good model. The model AHRRR23 showed an adequate R2 value for the training set 0.9446, good predictive power with Q2 of 0.6409, good F- value, low SD 0.1218 value and outstanding Pearson-R values and low RMSE 0.2779 values of the model. The docking studies also gives very good results with good RMSD values. 3D QSAR, docking and ADME studies exhibits that the developed model could be employed as a potential lead for further study as antimalarial drug.

Discovery of Novel Tetrahydropyrido-Pyrazole based Cathepsin S Inhibitors: Implications of Two Dimensional Quantitative Structure Activity Relationship Analysis

The anti-arthritic activity of cathepsin S enzyme has been acknowledged and regarded as an emerging target for the development of novel therapeutic agents for the treatment of various autoimmune disorders and other inflammatory diseases. Two dimensional-quantitative structure-activity relationships have been performed on a series of tetrahydropyrido-pyrazole nucleus using TSAR 3.3. Attempts have been made to derive and comprehend a correlation between biological activity (dependent variable) and descriptors (independent variables). The study was performed using 268 compounds (data set) by division into training and test set by the random selection method. 179 compounds generated a final quantitative structure-activity relationship model with the leave-out one row method of cross-validation to evaluate the predictive ability of the model. The most significant model with n=179, regression coefficient (0.851), correlation coefficient (0.725), cross-validated correlation coefficient (0.709), standard error (0.349), Fischer statistic value (114.706) was developed using multiple linear regression analysis. For partial least squares, a statistical significance value of 0.988 and a fraction of variance explained 0.723 were observed. A comparable partial least squares model with correlation coefficient (0.723) and neural model with correlation coefficient (0.731) indicated good internal predictability of the model. External test set validation provided correlation coefficient values of 0.708 and 0.706 for multiple linear regressions and partial least squares analysis respectively. Quantitative structure-activity relationship model indicated the importance of hydrophobic (log P (substituent 1)), topological (kier chi 5 (path) index (whole molecule)), electronic (bond dipole moment (substituent 5)) and (dipole moment Z component (substituent 1)) descriptors for the activity of Cathepsin S inhibitors. This study will be effective in designing novel and more potent cathepsin S inhibitors.

Studies on molecular mechanism between SHP2 and pyridine derivatives by 3D-QSAR, molecular docking and MD simulations

BackgroundSrc homology 2 (SH2)-containing protein tyrosine phosphatase 2 (SHP2) as a major phosphatase would affect the development of tumors by regulating several cellular processes, and is a significant potential target for cancer treatment. MethodsIn the present work, a series of pyridine derivatives possessing a wide range of inhibitory activity was employed to investigate the structural requirements by developing three dimensional quantitative structure–activity relationship (3D-QSAR) models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The results show that CoMFA (R2cv = 0.646, R2pred = 0.5587) and CoMSIA (R2cv = 0.777, R2pred = 0.7131) have excellent stability and predictability. The relationship between the inhibitory activity and structure of the inhibitors was analyzed by the derived contour maps. Furthermore, the QSAR models were validated by molecular docking and molecular dynamics simulations, which were also applied to reveal the potential molecular mechanism of these inhibitors. FindingsIt was found that Arg110, Asn216, Thr218, Thr252 and Pro490 play a crucial role in stabilizing the inhibitors. Additionally, MM/PBSA calculations provided the binding free energy were also conducted to explain the discrepancy of binding activities. Overall, the outcomes of this work could provide useful information and theoretical guidance for the development of novel and potent SHP2 inhibitors.

3D QSAR analysis on quinoxaline derivatives as anti-malarial using K-nearest neighbour molecular field analysis

In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q2=0.7589 and external predictivity (pred_r2 = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead.

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

Design and Synthesis of New CDK2 Inhibitors Containing Thiazolone and Thiazolthione Scafold with Apoptotic Activity.

Cyclin dependent kinase 2 (CDK2) inhibition is a well-established strategy for treating cancer. Different series of novel thiazolone (1, 7-9) together with fused thiazolthione (2-6, and 10) derivatives were designed, then synthesized and evaluated for their biological inhibitory activity against CDK2. Additionally, the cytotoxicity of the new compounds was explored against breast and colon cancer cell lines. The novel thiazolone and the fused thiazolthione derivatives exhibited potent CDK2/cyclin A2 inhibitory effect of an IC50 values ranging 105.39-742.78 nM. Amongst them compounds 4 and 6 revealed highest IC50 of 105.39 and 139.27 nM, respectively. Most compounds showed significant inhibition on both breast cancer and colon cancer cell lines with IC50 range 0.54-5.26 and 0.83-278 µM, respectively. Further investigations involved flow cytometry analysis on MCF-7 cancer cell line for compounds 5 and 7 which resulted in arrest cell-cycle at two phases Pre G1/G2-M and re-enforced apoptosis via activation of caspase-7. Molecular modeling simulation of the designed compounds revealed that they were well fitted into CDK2 active site and their complexes were stabilized through the essential hydrogen bonding. Three dimensional quantitative structure activity relationship (3D QSAR) pharmacophore, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were also carried out showing proper pharmacokinetic and drug-likeness which aided in the prediction of the structure requirements responsible for the observed antitumor activity.

Caspase-3 Inhibition Prediction of Pyrrolo[3,4-c] Quinoline-1,3-Diones Derivatives Using Computational Tools

In the present work, two dimensional quantitative structure activity relationship, molecular docking and absorption, distribution, metabolism, excretion and toxicity analyses were performed to pyrrolo[3,4-c] quinoline-1,3-diones derivatives, previously reported as caspase-3 inhibitors. A total of one hundred fifteen compounds were used to build linear multiple linear regression (multiple linear regression) and non-linear (artificial neural networks) quantitative structure activity relationship models, using genetic algorithm as a feature selection method. Both models were thoroughly validated following Organization for economic cooperation and development principles by internal and external validation as well as the domain of application (antiphase domain). Both Genetic algorithm-multiple linear regression (Rtrain=0.88, Rtest=0.94, mapetest=5.3 and rmsetest=0.41) and Genetic algorithm-artificial neural network (Rtrain=0.9, Rtest=0.93, mapetest=4.5 and rmsetest=0.4) models are statistically robust with high external predictive ability. Molecular docking simulations were performed on selected inhibitors revealed that binding energy values are in accordance with inhibitory activity values against caspase-3, which is modulated by hydrogen bondings, Pi stacking and hydrophobic interactions. The docking studies suggest that the inhibitors bind with an allosteric site of the enzyme formed by ARG207B, SER251B, PHE250 and PHE256 of the B chain. Besides, in silico, absorption, distribution, metabolism, excretion and toxicity profiles of selected inhibitors were checked to evaluate the key pharmacokinetic, physiochemical and druglikeness features.

Three Dimensional Quantitative Structure Activity Relationship and Molecular Docking Studies of Flavonoids as Reverse Transcriptase Inhibitors

A set of 29 flavonoid molecules are used to generate comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. The best CoMFA model showed a cross-validated correlation coefficient (q2) = 0.762, noncross- validated correlation coefficient (r2) = 0.939, standard error of estimate (S) = 0.038 and F = 396. And that for CoMSIA model were q2 = 0.758, r2 = 0.957, S = 0.063 and F = 236. The models show a high predictive ability, validated by 11 favonoid molecules. The docking studies shows the hydrogen bonding interaction is mostly responsible for binding of the flavonoids molecules in the binding pocket of HIV 1-RT protein (3HVT.pdb).

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors.

Alzheimer's disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer's disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

In silico design novel (5-imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine derivatives as inhibitors for glycogen synthase kinase 3 based on 3D-QSAR, molecular docking and molecular dynamics simulation

Glycogen Synthase Kinase 3 (GSK-3) is a member of cellular kinase with various functions, such as glucose regulation, cellular differentiation, neuronal function and cell apoptosis. It has been proved as an important therapeutic target in type 2 diabetes mellitus and Alzheimer's disease. To better understand their structure–activity relationships and mechanism of action, an integrated computational study, including three dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD), was performed on 79 (5-Imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine GSK-3 inhibitors. In this paper, we constructed 3D-QSAR using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) method. The results showed that the CoMFA model (q2 = 0.743, r2 = 0.980) and the CoMSIA model (q2 = 0.813, r2 = 0.976) had stable and reliable predictive ability. The electrostatic and H-bond donor fields play important roles in the models. The contour maps of the model visually showed the relationship between the activity of compounds and their three-dimensional structure. Molecular docking was used to identify the key amino acid residues at the active site of GSK-3 and explore its binding mode with ligands. Based on 3D-QSAR models, contour maps and the binding feature between GSK-3 and inhibitor, we designed 10 novel compounds with good potential activity and ADME/T profile. Molecular dynamics simulation results validated that Ile62, Val70 and Lys85 located in the active site play a key role for GSK-3 complexed with inhibitors. These results might provide important information for designing GSK-3 inhibitors with high activity.

3D-QSAR and Molecular Docking Studies on Design Anti-Prostate Cancer Curcumin Analogues.

Prostate cancer is one of the most common tumors in the world and the fifth leading cause of male cancer death. Although the treatment of localized androgen-dependent prostate cancer has been successful, the efficacy of androgen-independent metastatic disease is limited. Curcumin, a natural product, has been found to inhibit the proliferation of prostate cancer cells. To design curcumin analogs with higher biological activity and lower toxicity and side effects for the treatment of prostate cancer. In this study, the three dimensional-quantitative structure activity relationship (3DQSAR) and molecular docking studies were performed on 34 curcumin analogs as anti-prostate cancer compounds. We introduced OSIRIS Property Explorer to predict drug-related properties of newly designed compounds. The optimum CoMSIA model exhibited statistically significant results: the cross-validated correlation coefficient q2 is 0.540 and non-cross-validated R2 value is 0.984. The external predictive correlation coefficient Rext 2 is 0.792. The information of structure-activity relationship can be obtained from the CoMSIA contour maps. In addition, the molecular docking study of the compounds for 3ZK6 as the protein target revealed important interactions between active compounds and amino acids. Compound 28i may be a new type of anti-prostate cancer drug with higher biological activity and more promising development.

Three Dimensional Quantitative Structure Activity Relationship and Pharmacophore Modeling of Tacrine Derivatives as Acetylcholinesterase Inhibitors in Alzheimer's Treatment.

Three dimensional quantitative structure activity relationship and pharmacophore modeling are studied for tacrine derivatives as acetylcholinesterase inhibitors. The three dimensional quantitative structure-activity relationship and pharmacophore methods were used to model the 68 derivatives of tacrine as human acetylcholinesterase inhibitors. The effect of the docked conformer of each molecule in the enzyme cavity was investigated on the predictive ability and statistical quality of the produced models. The whole data set was divided into two training and test sets using hierarchical clustering method. 3D-QSAR model, based on the comparative molecular field analysis has good statistical parameters as indicated by q2 =0.613, r2 =0.876, and r2pred =0.75. In the case of comparative molecular similarity index analysis, q2, r2 and r2pred values were 0.807, 0.96, and 0.865 respectively. The statistical parameters of the models proved that the inhibition data are well fitted and they have satisfactory predictive abilities. The results from this study illustrate the reliability of using techniques in exploring the likely bonded conformations of the ligands in the active site of the protein target and improve the understanding over the structural and chemical features of AChE.

Prospective computational design and in vitro bio-analytical tests of new chemical entities as potential selective CYP17A1 lyase inhibitors.

The development and advancement of prostate cancer (PCa) into stage 4, where it metastasize, is a major problem mostly in elder males. The growth of PCa cells is stirred up by androgens and androgen receptor (AR). Therefore, therapeutic strategies such as blocking androgens synthesis and inhibiting AR binding have been explored in recent years. However, recently approved drugs (or in clinical phase) failed in improving the expected survival rates for this metastatic-castration resistant prostate cancer (mCRPC) patients. The selective CYP17A1 inhibition of 17,20-lyase route has emerged as a novel strategy. Such inhibition blocks the production of androgens everywhere they are found in the body. In this work, a three dimensional-quantitative structure activity relationship (3D-QSAR) pharmacophore model is developed on a diverse set of non-steroidal inhibitors of CYP17A1 enzyme. Highly active compounds are selected to define a six-point pharmacophore hypothesis with a unique geometrical arrangement fitting the following description: two hydrogen bond acceptors (A), two hydrogen bond donors (D) and two aromatic rings (R). The QSAR model showed adequate predictive statistics. The 3D-QSAR model is further used for database virtual screening of potential inhibitory hit structures. Density functional theory (DFT) optimization provides the electronic properties explaining the reactivity of the hits. Docking simulations discovers hydrogen bonding and hydrophobic interactions as responsible for the binding affinities of hits to the CYP17A1 Protein Data Bank structure. 13 hits from the database search (including five derivatives) are then synthesized in the laboratory as different scaffolds. Ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) in vitro experiments reveals three new chemical entities (NCEs) with half maximal inhibitory concentration (IC50) values against the lyase route at mid-micromolar range with favorable selectivity to the lyase over the hydroxylase route (one of them with null hydroxylase inhibition). Thus, prospective computational design has enabled the design of potential lead lyase-selective inhibitors for further studies.

Computational study of paroxetine-like inhibitors reveals new molecular insight to inhibit GRK2 with selectivity over ROCK1

The G-protein coupled receptor kinase 2 (GRK2) regulates the desensitization of beta-adrenergic receptors (β-AR), and its overexpression has been implicated in heart failure. Hence, the inhibition of GRK2 is considered to be an important drug target for the treatment of heart failure. Due to the high sequence similarity of GRK2 with the A, G, and C family (AGC family) of kinases, the inhibition of GRK2 also leads to the inhibition of AGC kinases such as Rho-associated coiled-coil kinase 1 (ROCK1). Therefore, unraveling the mechanisms to selectively inhibit GRK2 poses an important challenge. We have performed molecular docking, three dimensional quantitative structure activity relationship (3D-QSAR), molecular dynamics (MD) simulation, and free energy calculations techniques on a series of 53 paroxetine-like compounds to understand the structural properties desirable for enhancing the inhibitory activity for GRK2 with selectivity over ROCK1. The formation of stable hydrogen bond interactions with the residues Phe202 and Lys220 of GRK2 seems to be important for selective inhibition of GRK2. Electropositive substituents at the piperidine ring and electronegative substituents near the amide linker between the benzene ring and pyrazole ring showed a higher inhibitory preference for GRK2 over ROCK1. This study may be used in designing more potent and selective GRK2 inhibitors for therapeutic intervention of heart failure.

In silico prediction of estrogen receptor subtype binding affinity and selectivity using 3D-QSAR and molecular docking

Estrogen receptors (ERs) are significant protein targets that can alter normal homeostatic transcription and signaling pathways. And the development of selective ER ligands is involved in the pharmacology of anticancer drugs, leading to the synthesis of numerous ER selective ligands. In the present work, three dimensional quantitative structure–activity relationship (3D-QSAR) models, including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) in combination with molecular docking were performed on a series of ER ligands to investigate the potential relationship between the structural features of the compounds and its activity and selectivity. Good statistical significance was achieved for the CoMFA (ERα: R2cv = 0.676, R2pred = 0.631. ERβ: R2cv = 0.578, R2pred = 0.5341) and CoMISA (ERα: R2cv = 0.693, R2pred = 0.5758. ERβ: R2cv = 0.653, R2pred = 0.615) models. In addition, molecular docking results confirmed that the substituents at ring A, ring D and ring E were crucial for interacting with receptors ERα and ERβ, respectively, which were in agreement with the QSAR results. The substituents in ring A and ring F for ERα and residues Thr347/Ala302, Met388/Leu343, Leu391/Arg346, Met421/Ile376, Gly521/Leu476 for ERα and ERβ, respectively, contribute the most to the subtype selectivity. These results obtained from this work provide valuable information for the design of novel selective ER ligands with enhanced activity.

Docking, Interaction Fingerprint, and Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) of Sigma1 Receptor Ligands, Analogs of the Neuroprotective Agent RC-33.

The human Sigma1 receptor (S1R), which has been identified as a target with an important role in neuropsychological disorders, was first crystallized 3 years ago. Since S1R structure has no relation with another previous crystallized structures, the presence of the new crystal is an important hallmark for the design of agonists and antagonists against this important target. Some years ago, our group identified RC-33, a potent and selective S1R agonist, endowed with neuroprotective properties. In this work, drawing on new structural information, we studied the interactions of RC-33 and its analogs with the S1R binding site by using computational methods such as docking, interaction fingerprints, and receptor-guided alignment three dimensional quantitative structure–activity relationship (3D-QSAR). We found that RC-33 and its analogs adopted similar orientations within S1R binding site, with high similitude with orientations of the crystallized ligands; such information was used for identifying the residues involved in chemical interactions with ligands. Furthermore, the structure-activity relationship of the studied ligands was adequately described considering classical QSAR tests. All relevant aspects of the interactions between the studied compounds and S1R were covered here, through descriptions of orientations, binding interactions, and features that influence differential affinities. In this sense, the present results could be useful in the future design of novel S1R modulators.