Four-point Pharmacophore Research Articles

A structure-based approach to explore novel COX-2 inhibitors using pharmacophore modelling, 3D-QSAR analysis, virtual screening and dynamics simulation study

Anti-inflammatory medications are one of the most commonly preferred over-the-counter medications because inflammation and pain are closely related and caused by various disease conditions. Extensive research is mandatory to develop an effective anti-inflammatory therapy and good safety profile since both cyclooxygenase (COX-1 and COX-2) inhibition causes severe gastrointestinal irritation and cardiac toxicities. Only 190 molecules published in 2022 had selected for the investigation out of the many chemical architectures reported in the literature as COX-2 inhibitors. Using a four-point pharmacophore hypothesis AAAR2 (A-hydrogen bond acceptor and R- aromatic ring sites) generated from 138 molecules out of 190 with a site score of 0.84, a 3D atom-based QSAR model was built. With an outstanding correlation value of R2 0.92, fisher ratio of F 235.4, strong predictability of Q2 0.73, and Pearson-r 0.87, the 3D-QSAR model displays the highest and lowest efficient ligands reveals the favourable and unfavourable structural domains for the interaction toward COX-2. The selected 3D QSAR model is then utilised to screen compounds from bindingDB like a search engine. The molecules which passed the rule of five were chosen for further analysis after the screened molecules underwent an ADME analysis. Virtual screening (PDB ID-5KIR) was then used to identify 43 hit molecules, with the best docking scores falling between -11.07 and -9.55 kcal/mol, and these results were compared to the standard rofecoxib (docking score-9.479 kcal/mol). To evaluate the stable binding and interaction of the best molecule (Hit1), which is crucial for inhibiting COX-2, the molecular dynamics study was carried out for 100 ns.

Pharmacophore based virtual screening for identification of effective inhibitors to combat HPV 16 E6 driven cervical cancer

Targeting HPV16 E6 has emerged as an effective drug target for the treatment/management of cervical cancer. We utilized pharmacophore-based virtual screening, molecular docking, absorption, distribution, metabolism and excretion (ADME) prediction, and molecular dynamics simulation approach for identifying potential inhibitors of HPV16 E6. Initially, we generated a ligand-based pharmacophore model based on the features of four known HPV16 E6 inhibitors (CA24, CA25, CA26, and CA27) via the PHASE module implanted in the Schrödinger suite. We constructed four-point pharmacophore features viz., three hydrogen bond acceptors (A) and one aromatic ring (R). The common pharmacophore feature further employed as a query for virtual screening against the ASINEX database via Schrödinger suite. The pharmacophore-based virtual screening filtered out top 2000 hits, based on the fitness score. We then applied the high throughput virtual screening (HTVS), standard precision (SP) and extra precision (XP). 1000 compounds were obtained from HTVS docking. Based on the glide score, they were further filtered to 500 hits by employing docking in standard precision mode. Finally, the best four hits and a negative molecule were identified using docking in XP mode. The four lead compounds and a negative molecule were then further subjected to ADME profile prediction by engaging Qikprop module. The ADME properties of the four lead molecules indicate good pharmacokinetic (PK) properties rather than the negative molecule. The binding stability of the HPV16 E6-hit complexes were investigated at a different time scale (100 ns) by using the desmond package and the results were examined using Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) and it revealed the stability of the protein-ligand complex throughout the simulation. Key residues, CYS 51 and GLN 107, also play a crucial role in enhancing the stability of the protein-ligand complex during the simulation. Furthermore, the binding free energy of the HPV16 E6-leads complexes was analyzed by prime which revealed that the ΔGbind coulomb and ΔGbind vdW interactions are crucially contributes to the binding affinity. In order to validate the computational findings, the efficacy of benzoimidazole and benzotriazole were ascertained for regulating ME180 cervical cancer cell survival, migration and ability to release MMP-2.

Ligand-based in silico identification and biological evaluation of potential inhibitors of nicotinamide N-methyltransferase.

Nicotinamide N-methyltransferase (NNMT) is a protein coding gene, which methylates the nicotinamide (NA) (vitamin B3) to produce 1-methylnicotinamide (MNA). Several studies have suggested that the overexpression of NNMT is associated with different metabolic disorders like obesity and type-2 diabetes thereby making it an important therapeutic target for development of anti-diabetic agents. Here we describe aworkflow for identification of new inhibitors of NNMT from a library of small molecules. In this study, we have hypothesized a four-point pharmacophore model based on the pharmacophoric features of reported NNMT inhibitors in the literature. The statistically significant pharmacophore hypothesis was used to explore the Maybridge compound library that resulted in mapping of 1330 hit compounds on the proposed hypothesis. Subsequently, a total of eight high scoring compounds, showing good protein-ligand interactions in the molecular docking study, were selected for biological evaluation of NNMT activity. Eventually, four compounds were found to show significant inhibitory activity for NNMT and can be further explored to design new derivatives around the identified scaffolds with improved activities as NNMT inhibitors.

Combined machine learning and pharmacophore based virtual screening approaches to screen for antibiofilm inhibitors targeting LasR of Pseudomonas aeruginosa

Pseudomonas aeruginosa, a virulent pathogen affects patients with cystic fibrosis and nosocomial infections. Quorum sensing (QS) mechanism plays a crucial role in causing these ailments by mediating biofilm formation and expressing virulent genes. A novel approach to circumvent this bacterial infection is by hindering its QS network. Targeting LasR of las system serves beneficial as it holds the top position in QS system cascade. Here, we have integrated machine learning, pharmacophore based virtual screening, molecular docking and simulation studies to look for new leads as inhibitors for LasR. Support vector machine (SVM) learning algorithm was used to generate QSAR models from 66 antagonist dataset. The top three models resulted in correlation coefficient (R2) values of 0.67, 0.86, and 0.91, respectively. The correlation coefficient (R2 test) values on external test set were found to be 0.62, 0.57, and 0.55, respectively. A four-point pharmacophore model was developed. The pharmacophore hypothesis AAAD_1 was used to screen for potential leads against MolPort database in ZincPharmer. The leads which showed predicted pIC50 value of >8.00 by SVM models were subjected to docking analysis that reranked the compounds based on docking scores. Four top leads namely ZINC3851967 N-[3,5-bis(trifluoromethyl)phenyl]-5-tert-butyl-6-chloropyrazine-2-carboxamide, ZINC4024175 4-Amino-1-[(2R,3S,4S,5S)-3,4-dihydroxy-5-(hydroxymethyl)oxolan-2-yl]-2-oxopyrimidine-5-carbonitrile, ZINC2125703 N-[(5-Methoxy-4,7-dimethyl-2-oxo-2H-chromen-3-yl)acetyl]-beta-alanine, and ZINC3851966 N-[3,5-Bis(trifluoromethyl)phenyl]5-tert-butylpyrazine-2-carboxamide were selected. These compounds were checked for its stability by performing a molecular dynamics simulation for a period of 100 ns. The ADME properties of the leads were also determined. Hence, the compounds identified in this study can be used as possible leads for developing a novel inhibitor for LasR. Communicated by Ramaswamy H. Sarma

PENAPISAN VIRTUAL BERBASIS STRUKTUR DARI DATABASE BAHAN ALAM ZINC SEBAGAI INHIBITOR BRUTON TYROSINE KINASE

Bruton’s tyrosine Kinase (BTK) plays a critical role in many cellular signalling pathways making it a potential target to treat autoimmune diseases and cancer. In this study, we have implemented structure-based virtual screening against natural product ZINC database by using pharmacopore model followed by molecular docking to identified the inhibitor of BTK (PDB ID 6E4F). By using structure based pharmacophore, a four-point pharmacophore hypothesis was derived, with three hydropobic, one aromatic rings, four hydrogen bond acceptor and nine hydrogen bond donor. Screening of 12 natural product ZINC databases (151,837 compounds) against pharmacophore returned 1,345 hits with matching chemical features of 58.81. Docking these hits against the ATP-binding site of the BTK kinase domain through a virtual screening docking-based by using vina wizard and autodock wizard (PyRx 8.0) returned 148 and 75 hits, respectively. Three hit compounds with high affinity towards BTK were identified, and it could be used as a potent lead molecule for designing BTK inhibitor.

Molecular Dynamics Studies on COX-2 Protein-tyrosine Analogue Complex and Ligand-based Computational Analysis of Halo-substituted Tyrosine Analogues

Background: The quest for new drug entities and novel structural fragments with applications in therapeutic areas is always at the core of medicinal chemistry. Methods: As part of our efforts to develop novel selective cyclooxygenase-2 (COX-2) inhibitors containing tyrosine scaffold. The objective of this study was to identify potent COX-2 inhibitors by dynamic simulation, pharmacophore and 3D-QSAR methodologies. Dynamics simulation was performed for COX-2/tyrosine derivatives complex to characterise structure validation and binding stability. Certainly, Arg120 and Tyr355 residue of COX-2 protein formed a constant interaction with tyrosine inhibitor throughout the dynamic simulation phase. A four-point pharmacophore with one hydrogen bond acceptor, two hydrophobic and one aromatic ring was developed using the HypoGen algorithm. The generated, statistically significant pharmacophore model, Hypo 1 with a correlation coefficient of r2, 0.941, root mean square deviation, 1.15 and total cost value of 96.85. Results: The QSAR results exhibited good internal (r2, 0.992) and external predictions (r2pred, 0.814). The results of this study concluded the COX-2 docked complex was stable and interactive like experimental protein structure. Also, it offered vital chemical features with geometric constraints responsible for the inhibition of the selective COX-2 enzyme by tyrosine derivatives. Conclusion: In principle, this work offers significant structural understandings to design and develop novel COX-2 inhibitors.

Energetically optimized pharmacophore modeling to identify dual negative allosteric modulators against group I mGluRs in neurodegenerative diseases

Glutamate is the key neurotransmitter in human brain that regulates the important functions such as learning, memory and cognition. Abnormal regulation of glutamate leads to various neurodegenerative disorders. To regulate the glutamate signaling pathway we have employed a computational technique called energetically optimized pharmacophore model. We selected four-point pharmacophore (HRRR) based on the glide energy scores and screened the eMolecules database having more than 10 million compounds to find the potential dual negative allosteric modulators for both mGluR1 and mGluR5. The pharmacokinetic properties were calculated to filter out the unwanted molecules. Further, molecular docking, enrichment calculations and molecular dynamics simulations were executed. Finally, the top two compounds (1080734, 22696638) selected from the screened database could be potent dual negative allosteric modulators for the targeted proteins mGluR1 and mGluR5.AbbreviationsCNScentral nervous systemGPCRG-protein coupled receptormGluRmetabotropic glutamate receptorIFDinduced fit dockingPOPCpalmitoyloleoylphosphatidylcholineVFTVenus flytrap domainRMSDroot mean square deviationRMSFroot mean square fluctuation7TMseven transmembraneCommunicated by Ramaswamy H. Sarma

Synthesis, in vitro assays, molecular docking, theoretical ADMET prediction, and evaluation of 4-methoxy-phenylthiazole-2-amine derivatives as acetylcholinesterase inhibitors

Based on the cholinergic hypothesis of the reported compound, N-(4-(4-methoxy-phenyl)thiazol-2-yl)-3-(pyrrolidin-1-yl)propionamide, which had a good inhibitory activity to acetylcholinesterase (AChE), the new 4-methoxy-phenylthiazole-2-amine derivatives as AChE inhibitors (AChEIs) have been designed and synthesized in this study. Their chemical structures were confirmed by proton nuclear magnetic resonance, carbon-13 nuclear magnetic resonance, mass spectrometry, and infrared. Furthermore, their inhibitory activities against AChE in vitro were also tested by Ellman spectrophotometry, and the inhibitory activity test results showed that most of the compounds of 4-methoxy-phenylthiazole-2-amine derivatives had a certain AChE inhibitory activity in vitro, and the IC50 (half-maximal inhibitory concentration) value of compound 5g was 5.84 μmol/L, which was higher than that of the reference compound, rivastigmine. Moreover, it had almost no inhibitory effect on butyrylcholinesterase. In addition, compound 5g was subjected to enzyme inhibition kinetics experiments, and the result of Lineweaver–Burk’s V−1–[S]−1 double-reciprocal plot showed that the acting type of compound 5g was mixed inhibition type. Furthermore, the AChE inhibitory activity mechanism of compound 5g was explored by the conformational analysis and molecular docking, which was based on the principle of the four-point pharmacophore model necessary for AChE inhibition. Finally, in silico molecular property and ADMET (absorption, distribution, metabolism, excretion, and toxicity) of the synthesized compounds were predicted by using Molinspiration and PreADMET online servers, respectively. It can be concluded that the lead AChEI compound 5g presented satisfactory drug-like characteristics and ADME properties.

Pharmacophore modeling and virtual screening in search of novel Bruton's tyrosine kinase inhibitors.

Bruton's tyrosine kinase (BTK) is a known drug target for B cell malignancies and autoimmune diseases like rheumatoid arthritis. Consequently, efforts to develop BTK inhibitors have gained momentum in the last decade, resulting in a number of potential inhibitory molecules. However, to date, there are only two FDA approved drugs for B cell malignancies (Ibrutinib and Acalabrutinib), thus continued efforts are warranted. A large number of molecular scaffolds with potential BTK inhibitory activity are already available from these studies, and therefore we employed a ligand-based approach towards computer-aided drug design to develop a pharmacophore model for BTK inhibitors. Using over 400 molecules with known half maximal inhibitory concentrations (IC50) for BTK, a four-point pharmacophore hypothesis was derived, with two aromatic rings (R), one hydrogen bond acceptor (A) and one hydrogen bond donor (D). Screening of two small-molecule databases against this pharmacophore returned 620 hits with matching chemical features. Docking these against the ATP-binding site of the BTK kinase domain through a virtual screening workflow yielded 30 hits from which ultimately two natural compounds (two best scoring poses for each) were prioritized. Molecular dynamics simulations of these four docked complexes confirmed the stability of protein-ligand binding over a 200ns time period, and thus their suitability for lead molecule development with further optimization and experimental testing. Of note, the pharmacophore model developed in this study would also be further useful for de novo drug design and virtual screening efforts on a larger scale. Graphical abstract Pharmacophore modeling and virtual screening in search of novel Bruton's tyrosine kinase inhibitors.

Neonicotinoids Activity Against Cowpea Aphids by Computational Estimation

In this study, the insecticidal activity against Cowpea aphids (Aphis craccivora) of a series of 23 phenylazo, pyrrole-, dihydropyrrole-fused and chain-opening nitromethyleneneonicotinoids was evaluated by using the multiple linear regression (MLR) and pharmacophore modelling. Conformer insecticide ensembles were modeled using the MMFF94s force field. Minimum energy conformers were employed to calculate structural parameters, which were related to the experimental pLC50 values. Several statistical criteria of goodness of fit and predictivity were checked to validate the models. Robust and predictable MLR models were obtained. Further, the Phase module from Schrodinger suite was engaged in the generation of the ligand-based pharmacophore models. The atom-based 3D-QSAR module from the aforementioned software was used for the validation of a best four-point pharmacophore model. The obtained significant statistical parameters attested thepharmacophore model validity. The MLR and pharmacophore models are useful for the prediction of new insecticides with activity against Cowpea aphids.

QSAR Studies of New Pyrido[3,4-b]indole Derivatives as Inhibitors of Colon and Pancreatic Cancer Cell Proliferation.

We have discovered a new class of pyrido[b]bindole derivatives that show potent and broad spectrum anticancer activity with IC50 values down to submicromolar levels. Structure-activity relationship data acquired with the compounds as antiproliferative agents against several cancer cell lines, i.e. human HCT116 colon cancer cell line, and HPAC, Mia-PaCa2 and Panc-1 pancreatic cancer cell lines, were subjected to two different QSAR modeling methods. A kernel-based partial least squares (KPLS) regression analysis with chemical 2D fingerprint descriptors, and a PHASE pharmacophore alignment with 3D-QSAR study. The KPLS method afforded successful predictive QSAR models for antiproliferative activity of the HCT116 colon cell line and on two of the pancreatic cancer cell lines HPAC and Mia-PaCa2, with the following statistics: R 2s of 0.99, 0.99 and 0.98, for training set coefficients of determination, and external test set predictive r 2s of 0.70, 0.58 and 0.70, respectively. The best 2D fingerprint descriptor for both the HCT116 and HPAC data out of the eight finger prints utilized was the atom triplet fingerprint; whereas the one that worked best for the Mia-PaCa2 data was the linear fingerprint descriptor. The PHASE pharmacophore based 3D-QSAR study afforded a four-point pharmacophore model comprising one hydrogen bond donor (D) and three ring (R) elements, which yielded a successful 3D-QSAR model only with the HCT116 cell line data with training set R 2 of 0.683, and an external test set predictive r 2 of 0.562. With the PHASE 3D-QSAR, the influence of electronic effects and hydrophobicity were visualized, and were in agreement with the observed SAR of substitutions, while the KPLS method the relative extent of contribution of each atom in a compound to the activity. These models will foster the lead optimization process for this potent series of anticancer pyrido [3,4-b]indole compounds.

Probing the structural requirements for angiotensin II receptor: molecular modeling studies

The present study describes the development of 3D-QSAR studies on angiotensin II receptor based on the selected pharmacophore model. A four-point pharmacophore with one hydrogen bond acceptor (A), one hydrophobic (H), and two aromatic ring features (R) as pharmacophore features was developed by PHASE module. The pharmacophore hypothesis yielded a statistically significant model with good partial least-squares results. Thus, obtained 3D-QSAR model with partial least-squares (PLS) factor regression coefficient value (r2 = 0.9547) for training set and high value of cross-validated correlation coefficient Q2 = 0.7418, root-mean-squared error RMSE = 0.1257. This model was found to yield reliable clues for the further optimization of benzimidazole derivatives in the dataset, which plays an important role in molecular drug design approach.

Common structural and pharmacophoric features of mPGES-1 and LTC4S.

Prostaglandins and leukotrienes are produced in the COX and 5-LOX pathways of the inflammatory process. The current drugs target the upstream enzymes of either of the two pathways, leading to side effects. We have attempted to target the downstream enzymes simultaneously. Two compounds 2 and 3 (10μM), identified by virtual screening, inhibited mPGES-1 activity by 53.4±4.0 and 53.9±8.1%, respectively. Structural and pharmacophore studies revealed a set of common residues between LTC4S and mPGES-1 as well as four-point pharmacophore mapping onto the inhibitors of both these enzymes as well as 2 and 3. These structural and pharmacophoric features may be exploited for ligand- and structure-based screening of inhibitors and designing of dual inhibitors.

Rational design and synthesis of 1,5-disubstituted tetrazoles as potent inhibitors of the MDM2-p53 interaction

Using the computational pharmacophore-based ANCHOR.QUERY platform a new scaffold was discovered. Potent compounds evolved inhibiting the protein-protein interaction p53-MDM2. An extensive SAR study was performed based on our four-point pharmacophore model, yielding derivatives with affinity to MDM2 in the nanomolar range. Their binding affinity with MDM2 was evaluated using both fluorescence polarization (FP) assay and 2D-NMR-HSQC experiments.

A combination of pharmacophore modeling, atom-based 3D-QSAR, molecular docking and molecular dynamics simulation studies on PDE4 enzyme inhibitors

Phosphodiesterases 4 enzyme is an attractive target for the design of anti-inflammatory and bronchodilator agents. In the present study, pharmacophore and atom-based 3D-QSAR studies were carried out for pyrazolopyridine and quinoline derivatives using Schrödinger suite 2014-3. A four-point pharmacophore model was developed using 74 molecules having pIC50 ranging from 10.1 to 4.5. The best four feature model consists of one hydrogen bond acceptor, two aromatic rings, and one hydrophobic group. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R2 = .9949), cross validation coefficient (Q2 = .7291), and Pearson-r (.9107) at six component partial least square factor. The external validation indicated that our QSAR model possessed high predictive power with R2 value of .88. The generated model was further validated by enrichment studies using the decoy test. Molecular docking, free energy calculation, and molecular dynamics (MD) simulation studies have been performed to explore the putative binding modes of these ligands. A 10-ns MD simulation confirmed the docking results of both stability of the 1XMU–ligand complex and the presumed active conformation. Outcomes of the present study provide insight in designing novel molecules with better PDE4 inhibitory activity.

Relationships Between Pharmacovigilance, Molecular, Structural, and Pathway Data: Revealing Mechanisms for Immune-Mediated Drug-Induced Liver Injury.

Immune-mediated drug-induced liver injury (IMDILI) can be devastating, irreversible, and fatal in the absence of successful transplantation surgery. We present a novel approach that combines the methods of pharmacoepidemiology with in silico molecular modeling to identify specific features in toxic ligands that are associated with clinical features of IMDILI. Specifically, from pharmacovigilance data multivariate logistic regression identified 18 drugs associated with IMDILI (P < 0.00015). Eleven of these drugs, along with their known and proposed metabolites, constituted a training set used to develop a four-point pharmacophore model (sensitivity 75%; specificity 85%). Subsequently, this information was combined with information from immune-pathway reviews and genetic-association studies and complemented with ligand-protein docking simulations to support a hypothesis implicating two putative targets within separate, possibly interacting, immune-system pathways: the major histocompatibility complex within the adaptive immune system and Toll-like receptors (TLRs), in particular TLR-7, which represent pattern recognition receptors of the innate immune system.

Assessment of dual inhibition property of newly discovered inhibitors against PCAF and GCN5 through in silico screening, molecular dynamics simulation and DFT approach

p300/CBP-associated factor (PCAF) is one among the histone acetyltransferase (HAT) family enzymes. It is involved in the regulation of transcription by modifying the chromatin structure indirectly through the acetylation of histones. It has been emerged as a promising drug target for various types of cancer. A four-point pharmacophore with two hydrogen bond acceptor, one aromatic ring and one hydrophobic feature, was generated for six highly active isothiazolone derivatives as PCAF inhibitors in order to elucidate their anticancer activity. The generated pharmacophore was used for screening three different databases such as Maybridge, Life Chemicals and Chembridge databases. The screened compounds were further filtered through docking studies. Then the compounds were further carried for ADME prediction. The best three compounds BTB09406, F1418-0051 and F1880-1727 were docked to GCN5 to explore the dual inhibitory properties. The conformational stability of the protein–ligand complexes were analyzed through molecular dynamics simulation. Three best compounds were finally went through electronic structure analysis using density functional theory (DFT) at B3LYP/6-31**G level to understand their molecular reactivity. The results obtained from this study exploit that the three best compounds (BTB09406, F1418-0051 and F1880-1727) were found to have more potent and dual inhibitory properties.

Pharmacophore modeling and atom-based 3D-QSAR studies on amino derivatives of indole as potent isoprenylcysteine carboxyl methyltransferase (Icmt) inhibitors

Icmt enzymes are of particular importance in the post-translational modification of proteins that are involved in the regulation of cell growth. Thus, effective Icmt inhibitors may be of significant therapeutic importance in oncogenesis. To determine the structural requirements responsible for high affinity of previously reported amino derivatives of indole as Icmt inhibitors, a successful pharmacophore generation and atom-based 3D-QSAR analysis have been carried out. The best four-point pharmacophore model with four features HHRR: two hydrophobic groups (H) and two aromatic rings (R) as pharmacophore features was developed by PHASE module of Schrodinger suite. In this study, highly predictive 3D-QSAR models have been developed for Icmt inhibition using HHRR.191 hypothesis. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square (PLS) statistics results. The validation of the PHASE model was done by dividing the dataset into training and test set. The statistically significant the four-point pharmacophore hypothesis yielded a 3D-QSAR model with good PLS statistics results (R2=0.9387, Q2=0.8132, F=114.8, SD=0.1567, RMSE=0.2682, Pearson-R=0.9147). The generated model showed excellent predictive power, with a correlation coefficient of Q2=0.8132. The results of ligand-based pharmacophore hypothesis and atom-based 3D-QSAR provide detailed structural insights as well as highlights important binding features of novel amino derivatives of indole as Icmt inhibitors which can afford guidance for the rational drug design of novel, potent and promising Icmt inhibitors with enhanced potencies and may prove helpful for further lead optimization and virtual screening.

Pharmacophore and Virtual Screening of JAK3 inhibitors.

Janus kinase 3 (JAK3) is a non-receptor tyrosine kinases family of protein which is comprised of JAK1, JAK2, JAK3 and TYK2. It plays an important role in immune function and lymphoid development and it only resides in the hematopoietic system. Therefore, selective targeting JAK3 is a rational approach in developing new therapeutic molecule. In this study, about 116 JAK3 inhibitors were collected from the literature and were used to build four-point pharmacophore model using Phase (Schrodinger module). The statistically significant pharmacophore hypothesis of AAHR.92 with r2 value of 0.942 was used as 3D query to search against 3D database namely Zincpharmer. A total of 2, 27,483 compounds obtained as hit were subjected to high throughput virtual screening (HTVS module of Schrodinger). Among the hits, ten compounds with good G-score ranging from -12.96 to -11.18 with good binding energy to JAK3 were identified.

Pharmacophore Based 3D-QSAR Study of Biphenyl Derivatives as Nonsteroidal Aromatase Inhibitors in JEG-3 Cell Lines

Breast cancer is one of the most high-profile malignant diseases in modern society. Among postmenopausal women affected by the disease, a substantial portion has breast tumors that are estrogen-receptor positive. A common therapeutic intervention for this type of cancer is through endocrine therapy. Endocrine agents can act by either diminishing the availability or inhibiting the binding of estrogens to ER. Aromatase catalyzes the conversion of androgens to estrogens in the final step of the biosynthesis of estrogens and is therefore an attractive therapeutic target for inhibition. 3DQSAR pharmacophore modeling studies were undertaken for biphenyl derivatives as aromatase inhibitors in JEG-3 cell lines. A four-point pharmacophore with two H-bond acceptors and two aromatic rings as pharmacophoric features was developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R² = 0.977 for training set molecules. The generated model showed excellent predictive power, with a correlation coefficient of Q² = 0.946 for an external test set. The 3D-QSAR plots illustrated insights into the structure activity relationship of these compounds which may help in the design and development of potent biphenyl derivatives as new aromatase inhibitors.