Three-dimensional Pharmacophore Model Research Articles

Discovery of octahydropyrrolo [3,2-b] pyridin derivative as a highly selective Type I inhibitor of FGFR3 over VEGFR2 by high-throughput virtual screening.

Although the aberrant activity of fibroblast growth factor receptor 3 (FGFR3) is implicated in various cancers, the reported kinase inhibitors of FGFR3 tend to cause side effects resulting from the inhibitory activity on vascular endothelial growth factor receptor 2 (VEGFR2). Therefore, it is necessary to find a novel high-selective inhibitor of FGFR3 over VEGFR2 from the small-molecule compound database. In this study, integrated virtual screening protocols were established to screen for selective inhibitors of FGFR3 over VEGFR2 in Drugbank and Asinex databases by combining three-dimensional pharmacophore model, molecular docking, molecular dynamics (MD) simulation, and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations. Finally, it is found that Asinex-5082, as an octahydropyrrolo[3,2-b] pyridin derivative, has larger binding free energy with FGFR3 (-39.3 kcal/mol) than reference drug Erdafitinib (-29.9 kcal/mol), while cannot bind with VEGFR2, resulting in considerable inhibitory selectivity. This is because Asinex-5082, unlike Erdafitinib, has not m-dimethoxybenzene with large steric hindrance, thus can enter the larger ATP-binding pocket of FGFR3 with DFG-in conformation to form hydrophobic interaction with residues Met529, Ile539, and Tyr557 as well as hydrogen bond with Ala558. On the other hand, due to the fact that the benzodioxane and N-heterocyclic rings are connected by carbonyl (C=O), Asinex-5082 cannot rotate freely so as to enter the smaller ATPbinding pocket of VEGFR2 on the DFG-out conformation. The lead molecule Asinex-5082 may facilitate the rational design and development of novel selective inhibitors of FGFR3 over VEGFR2 as anticancer drugs.

Mind the Gap-Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence.

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Identification of potential antivirals against 3CLpro enzyme for the treatment of SARS-CoV-2: A multi-step virtual screening study

ABSTRACT The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak is posing a serious public health threat worldwide in the form of COVD-19. Herein, we have performed two-dimensional quantitative structure–activity relationship (2D-QSAR) and three-dimensional pharmacophore modelling analysis employing inhibitors of 3-chymotrypsin-like protease (3CLpro), the leading protease that is crucial for the replication of SARS-CoV-2. The investigation aims to identify the important structural features responsible for the enzyme inhibition and the search for novel 3CLpro enzyme inhibitors as effective therapeutics for treating SARS-CoV-2. Furthermore, we carried out molecular docking studies using the most and least active compounds in the dataset, aiming to validate the contributions of various features as appeared in the QSAR models. Later, the stringently validated 2D-QSAR model was used to estimate the 3CLpro inhibitory activity of compounds from five chemical databases. Compounds with the significant predicted activity were then subjected to pharmacophore-based virtual screening to screen the top-rated compounds, which were then further subjected to molecular docking analysis, absorption, distribution, metabolism, excretion – toxicity (ADMET) profiling, and molecular dynamics (MD) simulation. The multi-step virtual screening analyses suggested that compounds CASAntiV-865453-58-3, CASAntiV-865453-40-3, and CASAntiV-2043031-84-9 could be used as effective therapeutic agents for the treatment of SARS-CoV-2.

Introducing structure-based three-dimensional pharmacophore models for accelerating the discovery of selective BRD9 binders

A well-structured in silico workflow is here reported for disclosing structure-based pharmacophore models against bromodomain-containing protein 9 (BRD9), accelerating virtual screening campaigns and facilitating the identification of novel binders. Specifically, starting from 23 known ligands co-crystallized with BRD9, three-dimensional pharmacophore models, namely placed in a reference protein structure, were developed. Specifically, we here introduce a fragment-related pharmacophore model, useful for the identification of new promising small chemical probes targeting the protein region responsible of the acetyllysine recognition, and two further pharmacophore models useful for the selection of compounds featuring drug-like properties. A pharmacophore-driven virtual screening campaign was then performed to facilitate the selection of new selective BRD9 ligands, starting from a large library of commercially available molecules. The identification of a promising BRD9 binder (7) prompted us to re-iterate this computational workflow on a second focused in-house built library of synthesizable compounds and, eventually, three further novel BRD9 binders were disclosed (8–10). Moreover, all these compounds were tested among a panel comprising other nine bromodomains, showing a high selectivity for BRD9. Preclinical bioscreens for potential anticancer activity highlighted compound 7 as that showing the most promising biological effects, proving the reliability of this in silico pipeline and confirming the applicability of the here introduced structure-based three-dimensional (3D) pharmacophore models as straightforward tools for the selection of new BRD9 ligands.

Molecular dynamics simulation, 3D-pharmacophore and scaffold hopping analysis in the design of multi-target drugs to inhibit potential targets of COVID-19

SARS-CoV-2 has posed serious threat to the health and has inflicted huge costs in the world. Discovering potent compounds is a critical step to inhibit coronavirus. 3CLpro and RdRp are the most conserved targets associated with COVID-19. In this study, three-dimensional pharmacophore modeling, scaffold hopping, molecular docking, structure-based virtual screening, QSAR-based ADMET predictions and molecular dynamics analysis were used to identify inhibitors for these targets. Binding free energies estimated by molecular docking for each ligand in different binding sites of RdRp were used to predict the active site. Previously reported active 3CLpro and RdRp inhibitors were used to build a pharmacophore model to develop different scaffolds. Structure-based simulations and pharmacophore modeling based on Hip Hop algorithm converged in a state that suggest hydrogen bond acceptor and donor features have a critical role in the two binding sites. Further validations indicated that the best pharmacophore model has fairly good correlation values compared with approved inhibitors. Structure-based simulation results approved that GLu166 and Gln189 in 3CLpro and Lys551 and Glu811 in RdRp, are critical residues for dual activities. Ten compounds were extracted from pharmacophore-based virtual screening in six databases. The results, gained by repurposing approach, suggest the effectiveness of these ten compounds with different scaffolds as possible inhibitors of the two targets. Some quinoline-based hybrid derivatives also were designed. QSAR descriptors plot predicted that the scaffolds have had accepted pharmacokinetic profiles. Multiple molecular dynamics simulations in 100 ns and MM/PBSA studies of some reference inhibitors and the novel compounds in complex with both targets demonstrated stable complexes and confirmed the interaction modes. Based on different computational methods, COVID-19 multi-target inhibitors are proposed. Communicated by Ramaswamy H. Sarma

Three-Dimensional Pharmacophore Modeling of Betulonic Acid Derivatives as a Strong Inhibitor of Human Coronavirus-229E Replication

These-days, pharmacophore approaches have become one of the foremost tools in drug discovery after the past century’s development. Numerous ligand-based and structure-based strategies are developed for improved pharmacophore modeling with success and extensively applied in virtual screening, de novo design and lead improvement. Till now, there is little information on 3D-pharmacophore studies of 1,2,3-triazolo-fused betulonic acid derivatives as a strong inhibitor for human coronavirus-229E replication. Here, we tend to report the appliance of pharmacophore modeling for betulonic acid derivatives as an inhibitor. This study has been undertaken to realize intuitions into molecular mechanisms and structural necessities crucial for potential inhibition of betulonic acid derivatives. The standard procedure was adapted to develop the pharmacophoric models. It is found that the pharmacophore model of active betulonic acid derivative (compound 5h) unveils the importance of five- and six-member aliphatic cyclic hydrocarbon moiety, aromatic ring, –OH group of carboxylic acid, five-member heterocyclic rings (triazolo) and aliphatic alkene group and their correlation with the biological activity. It may be helpful within the design of novel betulonic acid derivatives inhibitors for human coronavirus-229E replication. Keywords: Pharmacophore Model, Antiviral activity, HCoV-229E replication, Betulonic acid derivatives, nsp15.

A group of cationic amphiphilic drugs activates MRGPRX2 and induces scratching behavior in mice

Mas gene-related G protein-coupled receptors (MRGPRs) are a G protein-coupled receptor family responsive to various exogenous and endogenous agonists, playing a fundamental role in pain and itch sensation. The primate-specific family member MRGPRX2 and its murine orthologue MRGPRB2 are expressed by mast cells mediating IgE-independent signaling and pseudoallergic drug reactions. Our aim was to increase knowledge about the function and regulation of MRGPRX2/MRGPRB2, which is of major importance in prevention of drug hypersensitivity reactions and drug-induced pruritus. To identify novel MRGPR (ant)agonists, we screened a library of pharmacologically active compounds by utilizing a high-throughput calcium mobilization assay. The identified hit compounds were analyzed for their pseudoallergic and pruritogenic effects in mice and human. We found a class of commonly used drugs activating MRGPRX2 that, to a large extent, consists of antidepressants, antiallergic drugs, and antipsychotics. Three-dimensional pharmacophore modeling revealed structural similarities of the identified agonists, classifying them as cationic amphiphilic drugs. Mast cell activation was investigated by using the 3 representatively selected antidepressants clomipramine, paroxetine, and desipramine. Indeed, we were able to show a concentration-dependent activation and MRGPRX2-dependent degranulation of the human mast cell line LAD2 (Laboratory of Allergic Diseases-2). Furthermore, clomipramine, paroxetine, and desipramine were able to induce degranulation of human skin and murine peritoneal mast cells. These substances elicited dose-dependent scratching behavior following intradermal injection into C57BL/6 mice but less so in MRGPRB2-mutant mice, as well as wheal-and-flare reactions following intradermal injections in humans. Our results contribute to the characterization of structure-activity relationships and functionality of MRGPRX2 ligands and facilitate prediction of adverse reactions such as drug-induced pruritus to prevent severe drug hypersensitivity reactions.

Exploiting Water Dynamics for Pharmacophore Screening.

Three-dimensional pharmacophore models have been proven extremely valuable in exploring novel chemical space through virtual screening. However, traditional pharmacophore-based approaches need ligand information and rely on static snapshots of highly dynamic systems. In this chapter, we describe PyRod, a novel tool to generate three-dimensional pharmacophore models based on water traces of a molecular dynamics simulation of an apo-protein.The protocol described herein was successfully applied for the discovery of novel drug-like inhibitors of West Nile virus NS2B-NS3 protease. By using this recent example, we highlight the key steps of the generation and validation of PyRod-derived pharmacophore models and their application for virtual screening.

Identification of 1-phenyl-4-cyano-5-aminopyrazoles as novel ecdysone receptor ligands by virtual screening, structural optimization, and biological evaluations.

Ecdysteroids initiate the molting process in insects by binding to the ecdysone receptor (EcR), which is a promising target for identifying insect growth regulators. This paper presents an in silico/in vitro screening procedure for identifying new EcR ligands. The three-step virtual screening procedure uses a three-dimensional pharmacophore model, docking and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) rescoring routine. A novel hit (VS14) with good binding activity against Plutella xylostella EcR was identified from a library of over 200,000 chemicals. Subsequently, the 1-phenyl-4-cyano-5-aminopyrazole scaffold and twelve EcR ligands were synthesized. Their IC50 values against Plutella xylostella EcR ranged from 0.64 to 23.21μm. Furthermore, a preliminary analysis of the structure-activity relationship for novel scaffolds provided a basis for designing new ligands with improved activity.

Structure-based virtual screening for novel chymase inhibitors by in silico fragment mapping

The term chymase refers to a family of chymotrypsin-like serine proteases stored within the secretory granules of mast cells. Recently, a variety of small molecule inhibitors for chymase have been developed with a primary focus on the treatment of cardiovascular diseases. Despite the expected therapeutic benefit of these chymase inhibitors, they have not been used clinically. Here, we attempted to identify new chymase inhibitors using a multistep structure-based virtual screening protocol combined with our knowledge-based in silico fragment mapping technique. The mapping procedure identified fragments with novel modes of interaction at the oxyanion hole of chymase. Next, we constructed a three-dimensional (3D) pharmacophore model and retrieved eight candidate chymase inhibitors from a commercial database that included approximately five million compounds. This selection was achieved using a multistep virtual screening protocol, which combined a 3D pharmacophore-based search, docking calculations, and analyses of binding free energy. One of the eight compounds exhibited concentration-dependent chymase inhibitory activity, which could be further optimized to develop more potent chymase inhibitors.

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning.

Invited for this month's cover picture is the group of Prof. Dr. Gisbert Schneider from the Swiss Federal Institute of Technology (ETH) Zurich (Switzerland). The cover picture illustrates the application of machine‐learning methods to expand the chemical space of farnesoid X receptor (FXR)‐targeting small molecules, by employing an ensemble of three complementary machine‐learning approaches (counter‐propagation artificial neural network, k‐nearest neighbor learner, and three‐dimensional pharmacophore model). Read the full text of their Full Paper at 10.1002/open.201800156.

Identification of influenza PA-Nter endonuclease inhibitors using pharmacophore- and docking-based virtual screening

Searching for new antiviral agents, we focused our interest on the influenza PA-Nter endonuclease. Therefore, we developed a three-dimensional pharmacophore model which contains the binding features addressed to the metal-chelating active site. The obtained hypothesis has been fruitfully employed to select three “hit compounds” through an in silico screening campaign on our in-house database of small molecules. We studied the binding poses of these hit compounds using molecular docking, and subjected them to an enzymatic assay with recombinant PA-Nter endonuclease. Compound 20 proved the most active inhibitor of the endonucleolytic cleavage reaction, with an IC50 value of 12 μM.

Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition

The bacterial ribosome is an established target for anti-bacterial therapy since decades. Several inhibitors have already been developed targeting both defined subunits (50S and 30S) of the ribosome. Aminoglycosides and tetracyclines are two classes of antibiotics that bind to the 30S ribosomal subunit. These inhibitors can target multiple active sites on ribosome that have a complex structure. To screen putative inhibitors against 30S subunit of the ribosome, the crystal structures in complex with various known inhibitors were analyzed using pharmacophore modeling approach. Multiple active sites were considered for building energy-based three-dimensional (3D) pharmacophore models. The generated models were validated using enrichment factor on decoy data-set. Virtual screening was performed using the developed 3D pharmacophore models and molecular interaction towards the 30S ribosomal unit was analyzed using the hits obtained for each pharmacophore model. The hits that were common to both streptomycin and paromomycin binding sites were identified. Further, to predict the activity of these hits a robust 2D-QSAR model with good predictive ability was developed using 16 streptomycin analogs. Hence, the developed models were able to identify novel inhibitors that are capable of binding to multiple active sites present on 30S ribosomal subunit.

Hyphenated 3D-QSAR statistical model-drug repurposing analysis for the identification of potent neuraminidase inhibitor.

The Influenza A virus is one of the principle causes of respiratory illness in human. The surface glycoprotein of the influenza virus, neuraminidase (NA), has a vital role in the release of new viral particle and spreads infection in the respiratory tract. It has been long recognized as a valid drug target for influenza A virus infection. Oseltamivir is used as a standard drug of choice for the treatment of influenza. However, the emergence of mutants with novel mutations has increased the resistance to potent NA inhibitor. In the present investigation, we have employed computer-assisted combinatorial techniques in the screening of 8621 molecules from Drug Bank to find potent NA inhibitors. A three-dimensional pharmacophore model was generated from the previously reported 28 carbocylic influenza NA inhibitors along with oseltamivir using PHASE module of Schrödinger Suite. The model generated consists of one hydrogen bond acceptor (A), one hydrogen bond donors (D), one hydrophobic group (H), and one positively charged group (P), ADHP. The hypothesis was further validated for its integrity and significance using enrichment analysis. Subsequently, an atom-based 3D-QSAR model was built using the common pharmacophore hypothesis (CPH). The developed 3D-QSAR model was found to be statistically significant with R2 value of 0.9866 and Q2 value of 0.7629. Further screening was accomplished using three-stage docking process using the Glide algorithm. The resultant lead molecules were examined for its drug-like properties using the Qikprop algorithm. Finally, the calculated pIC50 values of the lead compounds were validated by the AutoQSAR algorithm. Overall, the results from our analysis highlights that lisinopril (DB00722) is predicted to bind better with NA than currently approved drug. In addition, it has the best match in binding geometry conformations with the existing NA inhibitor. Note that the antiviral activity of lisinopril is reported in the literature. However, our paper is the first report on lisinopril activity against influenza A virus infection. These results are envisioned to help design the novel NA inhibitors with an increased antiviral efficacy.

Pharmacophore modelling, virtual screening and molecular docking studies on PLD1 inhibitors

Lipid metabolism plays a significant role in influenza virus replication and subsequent infection. The regulatory mechanism governing lipid metabolism and viral replication is not properly understood to date, but both Phospholipase D (PLD1 and PLD2) activities are stimulated in viral infection. In vitro studies indicate that chemical inhibition of PLD1 delays viral entry and reduction of viral loads. The current study reports a three-dimensional pharmacophore model based on 35 known PLD1 inhibitors. A sub-set of 25 compounds was selected as the training set and the remaining 10 compounds were kept in the test set. One hundred and twelve pharmacophore models were generated; a six-featured pharmacophore model (AADDHR.57) with survival score (2.69) produced a statistically significant three-dimensional quantitative structure–activity relationship model with r2 = 0.97 (internal training set), r2 = 0.71 (internal test set) and Q2 = 0.64. The predictive power of the pharmacophore model was validated with an external test set (r2 = 0.73) and a systematic virtual screening work-flow was employed showing an enrichment factor of 23.68 at the top 2% of the dataset (active and decoys). Finally, the model was used for screening of the filtered PubChem database to fetch molecules which can be proposed as potential PLD1 inhibitors for blocking influenza infection.

Molecular Hybridization of Potent and Selective γ-Hydroxybutyric Acid (GHB) Ligands: Design, Synthesis, Binding Studies, and Molecular Modeling of Novel 3-Hydroxycyclopent-1-enecarboxylic Acid (HOCPCA) and trans-γ-Hydroxycrotonic Acid (T-HCA) Analogs.

γ-Hydroxybutyric acid (GHB) is a neuroactive substance with specific high-affinity binding sites. To facilitate target identification and ligand optimization, we herein report a comprehensive structure-affinity relationship study for novel ligands targeting these binding sites. A molecular hybridization strategy was used based on the conformationally restricted 3-hydroxycyclopent-1-enecarboxylic acid (HOCPCA) and the linear GHB analog trans-4-hydroxycrotonic acid (T-HCA). In general, all structural modifications performed on HOCPCA led to reduced affinity. In contrast, introduction of diaromatic substituents into the 4-position of T-HCA led to high-affinity analogs (medium nanomolar Ki) for the GHB high-affinity binding sites as the most high-affinity analogs reported to date. The SAR data formed the basis for a three-dimensional pharmacophore model for GHB ligands, which identified molecular features important for high-affinity binding, with high predictive validity. These findings will be valuable in the further processes of both target characterization and ligand identification for the high-affinity GHB binding sites.

Multi-step virtual screening to develop selective DYRK1A inhibitors

Developing selective inhibitors for a particular kinase remains a major challenge in kinase-targeted drug discovery. Here we performed a multi-step virtual screening for dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors by focusing on the selectivity for DYRK1A over cyclin-dependent kinase 5 (CDK5). To examine the key factors contributing to the selectivity, we constructed logistic regression models to discriminate between actives and inactives for DYRK1A and CDK5, respectively, using residue-based binding free energies. The residue-based parameters were calculated by molecular mechanics-generalized Born surface area (MM-GBSA) decomposition methods for kinase–ligand complexes modeled by computer ligand docking. Based on the findings from the logistic regression models, we built a three-dimensional (3D) pharmacophore model and chose filter criteria for the multi-step virtual screening. The virtual hit compounds obtained from the screening were assessed for their inhibitory activities against DYRK1A and CDK5 by in vitro assay. Our screening identified two novel selective DYRK1A inhibitors with IC50 values of several μM for DYRK1A and >100μM for CDK5, which can be further optimized to develop more potent selective DYRK1A inhibitors.

Modification of PBDEs (BDE-15, BDE-47, BDE-85 and BDE-126) biological toxicity, bio-concentration, persistence and atmospheric long-range transport potential based on the pharmacophore modeling assistant with the full factor experimental design

In this study, the properties of AhR binding affinity, bio-concentration factor, half-life and vapor pressure were selected as the typical indicators of biological toxicity, bio-concentration, persistence and atmospheric long-range transport potential for polybrominated diphenyl ethers (PBDEs), respectively. A three-dimensional pharmacophore modeling assistant with a full factor experimental design for each property was used to reveal the significant pharmacophore features and the substituent effects to obtain reasonable modified schemes for the selected target PBDEs. Finally, the performances of the persistent organic pollutant (POP) properties, the synthesis feasibility and the fire resistance of the modified compounds were evaluated. The most influential pharmacophore feature for all POP properties was the hydrophobic group, especially the vinyl and propyl groups. Modified compounds with two additional hydrophobic groups exhibited a better regulatory performance. The average reduction in the proportions of the four POP properties for the modified compounds (except for 3-phenyl-BDE-15) was 70.60%, 52.44%, 47.04% and 70.88%. In addition, the energy and the C-Br bond dissociation enthalpy of the four typical PBDEs were higher than those of the modified compounds (except for 3-phenyl-BDE-15), indicating the synthesis feasibility and the lower energy barrier of the modified compounds to release Br free radicals to provide fire resistance.

In Silico Discovery and In Vitro Validation of Catechol-Containing Sulfonohydrazide Compounds as Potent Inhibitors of the Diguanylate Cyclase PleD.

Biofilm formation is responsible for increased antibiotic tolerance in pathogenic bacteria. Cyclic di-GMP (c-di-GMP) is a widely used second-messenger signal that plays a key role in bacterial biofilm formation. c-di-GMP is synthesized by diguanylate cyclases (DGCs), a conserved class of enzymes absent in mammals and hence considered attractive molecular targets for the development of antibiofilm agents. Here, the results of a virtual screening approach aimed at identifying small-molecule inhibitors of the DGC PleD from Caulobacter crescentus are described. A three-dimensional (3D) pharmacophore model, derived from the mode of binding of GTP to the active site of PleD, was exploited to screen the ZINC database of compounds. Seven virtual hits were tested in vitro for their ability to inhibit the activity of purified PleD by using circular dichroism spectroscopy. Two drug-like molecules with a catechol moiety and a sulfonohydrazide scaffold were shown to competitively inhibit PleD at the low-micromolar range (50% inhibitory concentration [IC50] of ∼11 μM). Their predicted binding mode highlighted key structural features presumably responsible for the efficient inhibition of PleD by both hits. These molecules represent the most potent in vitro inhibitors of PleD identified so far and could therefore result in useful leads for the development of novel classes of antimicrobials able to hamper biofilm formation. Biofilm-mediated infections are difficult to eradicate, posing a threatening health issue worldwide. The capability of bacteria to form biofilms is almost universally stimulated by the second messenger c-di-GMP. This evidence has boosted research in the last decade for the development of new antibiofilm strategies interfering with c-di-GMP metabolism. Here, two potent inhibitors of c-di-GMP synthesis have been identified in silico and characterized in vitro by using the well-characterized DGC enzyme PleD from C. crescentus as a structural template and molecular target. Given that the protein residues implied as crucial for enzyme inhibition are found to be highly conserved among DGCs, the outcome of this study could pave the way for the future development of broad-spectrum antibiofilm compounds.

Discovery of Novel Selective ERα/ERβ Ligands by Multi-pharmacophore Modeling and Virtual Screening.

Estrogen receptor α (ERα) and estrogen receptor β (ERβ) regulate different sets of gene expression, and have different ligand responses, which make the estrogen tissue-specific. Thus, the estrogen receptor (ER) subtype-selective ligands can improve the target-site selectivity and decrease the off-target effect. In order to discover the selective ER subtype ligands with novel scaffolds, in this work three-dimensional (3D) pharmacophore models of the ERα ligands (Hypo 1) and the ERβ ligands (Hypo 2) were established (correlation coefficients were 0.959 and 0.966) and validated (R=0.936 and 0.879; enrichment factors (EFs) at 2% were 16.2 and 8.4; areas under the concentration-time curve (AUC) of the receiver operating curve (ROC) were 0.88 and 0.91) using the Discovery Studio 4.0 software package. Hypo 1 and Hypo 2 were then employed for virtual screening and ten hits were found as potential candidate leads. Based on their ERα/ERβ binding affinity results by fluorescence polarization technology, two of these leads, AH-262/34334025 (AH) and AG-670/08803023 (AG) with novel scaffolds were identified as selective ERα ligands. A molecular docking study was also performed, which provided the explanation for the ER subtype preferences for AH and AG.