Combined Pharmacophore Modeling,Docking, and 3D QSAR Studies of ABCB1 and ABCC1 Transporter Inhibitors

Quinazolinones, indolo‐ and pyrrolopyrimidines with inhibitory effects toward ABCB1 (P‐gp) and ABCC1 (MRP1) transporters were studied by pharmacophore modeling, docking, and 3D QSAR to describe the binding preferences of the proteins. The pharmacophore overlays between dual and/or highly selective inhibitors point to binding sites of different topology and physiochemical properties for MRP1 and P‐gp. Docking of selective inhibitors into the P‐gp binding cavity by the use of a structural model based on the recently resolved P‐gp structure confirms the P‐gp pharmacophore features identified, and reveals the interactions of some functional groups and atoms in the structures with particular protein residues. The 3D QSAR analysis of the dual‐effect inhibitors allows satisfactory prediction of the selectivity index of the compounds and outlines electrostatics as most important for selectivity. The results from the combined modeling approach complement each other and could improve our understanding of the protein–ligand interactions involved, and could aid in the development of highly selective and potent inhibitors of P‐gp and MRP1.     

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q² = 0.621, r²_pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.     

3D QSAR Studies,Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors

Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q² = 0.636, r²_ncv = 0.988, r²_pred = 0.658; CoMSIA: q² = 0.843, r²_ncv = 0.989, r²_pred = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.     

Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations

Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q² (cross-validated correlation coefficient) = 0.557, R²_ncv (non-cross-validated correlation coefficient) = 0.740, R²_pre (predicted correlation coefficient) = 0.749 and Q² = 0.598, R²_ncv = 0.767, R²_pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.     

Development of Urea and Thiourea Kynurenamine Derivatives: Synthesis,Molecular Modeling,and Biological Evaluation as Nitric Oxide Synthase Inhibitors

Herein we describe the synthesis of a new family of kynurenamine derivatives with a urea or thiourea moiety, together with their in vitro biological evaluation as inhibitors of both neuronal and inducible nitric oxide synthases (nNOS and iNOS, respectively), enzymes responsible for the biogenesis of NO. These compounds were synthesized from a 5‐substituted‐2‐nitrophenyl vinyl ketone scaffold in a five‐step procedure with moderate to high chemical yields. In general, the assayed compounds show greater inhibition of iNOS than of nNOS, with 1‐[3‐(2‐amino‐5‐chlorophenyl)‐3‐oxopropyl]‐3‐ethylurea (compound 5 n ) being the most potent iNOS inhibitor in the series and the most iNOS/nNOS‐selective compound. In this regard, we performed molecular modeling studies to propose a binding mode for this family of compounds to both enzymes and, thereby, to elucidate the differential molecular features that could explain the observed selectivity between iNOS and nNOS.     

Design and Synthesis of DDR1 Inhibitors with a Desired Pharmacophore Using Deep Generative Models

Discoidin domain receptor 1 (DDR1) inhibitors with a desired pharmacophore were designed using deep generative models (DGMs). DDR1 is a receptor tyrosine kinase activated by matrix collagens and implicated in diseases such as cancer, fibrosis and hypoxia. Herein we describe the synthesis and inhibitory activity of compounds generated from DGMs. Three compounds were found to have sub-micromolar inhibitory activity. The most potent of which, compound 3 (N-(4-chloro-3-((pyridin-3-yloxy)methyl)phenyl)-3-(trifluoromethyl)benzamide), had an IC₅₀ value of 92.5 nM. Furthermore, these compounds were predicted to interact with DDR1, which have a desired pharmacophore derived from a known DDR1 inhibitor. The results of synthesis and experiments indicated that our de novo design strategy is practical for hit identification and scaffold hopping.     

Design and synthesis of novel dual-action compounds targeting the adenosine A(2A) receptor and adenosine transporter for neuroprotection

A novel compound, N⁶‐(4‐hydroxybenzyl)adenosine, isolated from Gastrodia elata and which has been shown to be a potential therapeutic agent for preventing and treating neurodegenerative disease, was found to target both the adenosine A_2A receptor (A_2AR) and the equilibrative nucleoside transporter 1 (ENT1). As A_2AR and ENT1 are proximal in the synaptic crevice of striatum, where the mutant huntingtin aggregate is located, the dual‐action compounds that concomitantly target these two membrane proteins may be beneficial for the therapy of Huntington’s disease. To design the desired dual‐action compounds, pharmacophore models of the A_2AR agonists and the ENT1 inhibitors were constructed. Accordingly, potentially active compounds were designed and synthesized by chemical modification of adenosine, particularly at the N⁶ and C^5’ positions, if the predicted activity was within an acceptable range. Indeed, some of the designed compounds exhibit significant dual‐action properties toward both A_2AR and ENT1. Both pharmacophore models exhibit good statistical correlation between predicted and measured activities. In agreement with competitive ligand binding assay results, these compounds also prevent apoptosis in serum‐deprived PC12 cells, rendering a crucial function in neuroprotection and potential utility in the treatment of neurodegenerative diseases.     

Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth

Dysregulated epidermal growth factor receptor (EGFR) expression is frequently observed in non-small cell lung cancer (NSCLC) growth and metastasis. Despite recent successes in the development of tyrosine kinase inhibitors (TKIs), inevitable resistance to TKIs has led to urgent calls for novel EGFR inhibitors. Herein, we report a rational workflow used to identify novel EGFR-TKIs by combining hybrid ligand- and structure-based pharmacophore models. Three types of models were developed in this workflow, including 3D QSAR-, common feature-, and structure-based EGFR-TK domain-containing pharmacophores. A National Cancer Institute (NCI) compound dataset was adopted for multiple-stage pharmacophore-based virtual screening (PBVS) of various pharmacophore models. The six top-scoring compounds were identified through the PBVS pipeline coupled with molecular docking. Among these compounds, NSC609077 exerted a significant inhibitory effect on EGFR activity in gefitinib-resistant H1975 cells, as determined by an enzyme-linked immunosorbent assay (ELISA). Further investigations showed that NSC609077 inhibited the anchorage-dependent growth and migration of lung cancer cells. Furthermore, NSC609077 exerted a suppressive effect on the EGFR/PI3K/AKT pathway in H1975 cells. In conclusion, these findings suggest that hybrid virtual screening may accelerate the development of targeted drugs for lung cancer treatment.     

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.     

Modelling Atypical Small‐Molecule Mimics of an Important Stem Cell Cytokine,Thrombopoietin

We report the first comprehensive 3D QSAR study of a large, structurally diverse set of compounds that act as atypical thrombopoietin (TPO) mimics by interacting with the transmembrane domain of the TPO receptor, c‐MPL. These agonists of c‐MPL were superimposed according to a pharmacophore hypothesis, resulting in 3D QSAR models of high statistical significance. The pharmacophore‐based superimposition and comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to derive the QSAR models relating structure to the published in vitro bioactivities of the TPO mimics. The CoMFA and CoMSIA models gave high correlation coefficients of the bioactivities with the derived fields, resulting in robust prediction of agonist activity of the superimposed compounds. The models have been interpreted in terms of the requirements for binding to the transmembrane domain of the TPO receptor.     

De Novo Design,Synthesis and Evaluation of Benzylpiperazine Derivatives as Highly Selective Binders of Mcl‐1

Considerable efforts have been made to the development of small‐molecule inhibitors of antiapoptotic B‐cell lymphoma 2 (Bcl‐2) family proteins (such as Bcl‐2, Bcl‐x_L, and Mcl‐1) as a new class of anticancer therapies. Unlike general inhibitors of the entire family, selective inhibitors of each member protein can hopefully reduce the adverse side effects in chemotherapy treatments of cancers overexpressing different Bcl‐2 family proteins. In this study, we designed four series of benzylpiperazine derivatives as plausible Bcl‐2 inhibitors based on the outcomes of a computational algorithm. A total of 81 compounds were synthesized, and their binding affinities to Bcl‐2, Bcl‐x_L, and Mcl‐1 measured. Encouragingly, 22 compounds exhibited binding affinities in the micromolar range (K_i<20 μM ) to at least one target protein. Moreover, some compounds were observed to be highly selective binders to Mcl‐1 with no detectable binding to Bcl‐2 or Bcl‐x_L, among which the most potent one has a K_i value of 0.18 μM for Mcl‐1. Binding modes of four selected compounds to Mcl‐1 and Bcl‐x_L were derived through molecular docking and molecular dynamics simulations. It seems that the binding affinity and selectivity of these compounds can be reasonably interpreted with these models. Our study demonstrated the possibility for obtaining selective Mcl‐1 inhibitors with relatively simple chemical scaffolds. The active compounds identified by us could be used as lead compounds for developing even more potent selective Mcl‐1 inhibitors with potential pharmaceutical applications.     

Computationally Empowered Workflow Identifies Novel Covalent Allosteric Binders for KRASG12C

Due to its frequent mutations in multiple lethal cancers, KRAS is one of the most-studied anticancer targets nowadays. Since the discovery of the druggable allosteric binding site containing a G12C mutation, KRAS^G12C has been the focus of attention in oncology research. We report here a computationally driven approach aimed at identifying novel and selective KRAS^G12C covalent inhibitors. The workflow involved initial enumeration of virtual molecules tailored for the KRAS allosteric binding site. Tools such as pharmacophore modeling, docking, and free-energy perturbations were deployed to prioritize the compounds with the best profiles. The synthesized naphthyridinone scaffold showed the ability to react with G12C and inhibit KRAS^G12C. Analogues were prepared to establish structure-activity relationships, while molecular dynamics simulations and crystallization of the inhibitor-KRAS^G12C complex highlighted an unprecedented binding mode.     

A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease

Among a group of 310 natural antiviral natural metabolites, our team identified three compounds as the most potent natural inhibitors against the SARS-CoV-2 main protease (PDB ID: 5R84), M^pro. The identified compounds are sattazolin and caprolactin A and B. A validated multistage in silico study was conducted using several techniques. First, the molecular structures of the selected metabolites were compared with that of GWS, the co-crystallized ligand of M^pro, in a structural similarity study. The aim of this study was to determine the thirty most similar metabolites (10%) that may bind to the M^pro similar to GWS. Then, molecular docking against M^pro and pharmacophore studies led to the choice of five metabolites that exhibited good binding modes against the M^pro and good fit values against the generated pharmacophore model. Among them, three metabolites were chosen according to ADMET studies. The most promising M^pro inhibitor was determined by toxicity and DFT studies to be caprolactin A (292). Finally, molecular dynamics (MD) simulation studies were performed for caprolactin A to confirm the obtained results and understand the thermodynamic characteristics of the binding. It is hoped that the accomplished results could represent a positive step in the battle against COVID-19 through further in vitro and in vivo studies on the selected compounds.     

Computer‐Guided Design,Synthesis, and Biological Evaluation of Quinoxalinebisarylureas as FLT3 Inhibitors

Activating mutations of FMS‐like tyrosine kinase 3 (FLT3) are present in ～30 % of patients with acute myeloid leukemia (AML) and are associated with poor prognosis. Point mutations in the tyrosine kinase domain (TKD) are observed as primary mutations or are acquired as secondary mutations in FLT3 with internal tandem duplications (ITDs) after treatment with tyrosine kinase inhibitors (TKIs). Although dozens of potent inhibitors against FLT3 ITD have been reported, activating TKD point mutations, especially at residues F691 and D835, remain the leading cause for therapy resistance, highlighting the consistent need for new potent inhibitors. Herein we report the identification and characterization of novel quinoxaline‐based FLT3 inhibitors. We used the pharmacophore features of diverse known inhibitors as a starting point for a new optimization algorithm for type II TKIs, starting from an in silico library pharmacophore search and induced‐fit docking in the known FLT3 structure. This led to the design of a set of diverse quinoxalinebisarylureas, which were profiled in an FLT3 kinase activity assay. The most promising compounds were further evaluated in a zebrafish embryo phenotype assay.     

Combined pharmacophore modeling, docking, and 3D-QSAR studies of PLK1 inhibitors

Polo-like kinase 1, an important enzyme with diverse biological actions in cell mitosis, is a promising target for developing novel anticancer drugs. A combined molecular docking, structure-based pharmacophore modeling and three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a set of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as PLK1 inhibitors. The common substructure, molecular docking and pharmacophore-based alignment were used to develop different 3D-QSAR models. The comparative molecular field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) models gave statistically significant results. These models showed good q(2) and r(2) (pred) values and revealed a good response to test set validation. All of the structural insights obtained from the 3D-QSAR contour maps are consistent with the available crystal structure of PLK1. The contour maps obtained from the 3D-QSAR models in combination with the structure based pharmacophore model help to better interpret the structure-activity relationship. These satisfactory results may aid the design of novel PLK1 inhibitors. This is the first report on 3D-QSAR study of PLK1 inhibitors.     

A Search for Cyclin-Dependent Kinase 4/6 Inhibitors by Pharmacophore-Based Virtual Screening,Molecular Docking,and Molecular Dynamic Simulations

The G1 phase of cell cycle progression is regulated by Cyclin-Dependent Kinase 4 (CDK4) as well as Cyclin-Dependent Kinase 6 (CDK6), and the acivities of these enzymes are regulated by the catalytic subunit, cyclin D. Cell cycle control through selective pharmacological inhibition of CDK4/6 has proven to be beneficial in the treatment of estrogen receptor-positive (ER-positive) breast cancer, particularly improving the progression-free survival of patients. Thus, targeting specific inhibition on CDK4/6 is bound to increase therapeutic efficiency. This study aimed to obtain CDK4/6 inhibitors through a pharmacophore-based virtual screening of the ZINC15 purchasable compound database using the in silico method. The pharmacophore model was designed based on the FDA-approved cdk4/6 inhibitor structures, and molecular docking was performed to further screen the hit compounds obtained. A total of eight compounds were selected based on docking results and interactions with CDK4 and CDK6, using palbociclib as the reference drug. According to the results, the compounds of ZINC585292724 and ZINC585291674 were the best compounds based on free binding energy, as well as hydrogen bond stability, and, therefore, exhibit potential as starting points in the development of CDK4/6 inhibitors.     

Exploration of Novel Xanthine Oxidase Inhibitors Based on 1,6-Dihydropyrimidine-5-Carboxylic Acids by an Integrated in Silico Study

Xanthine oxidase (XO) is an important target for the effective treatment of hyperuricemia-associated diseases. A series of novel 2-substituted 6-oxo-1,6-dihydropyrimidine-5-carboxylic acids (ODCs) as XO inhibitors (XOIs) with remarkable activities have been reported recently. To better understand the key pharmacological characteristics of these XOIs and explore more hit compounds, in the present study, the three-dimensional quantitative structure–activity relationship (3D-QSAR), molecular docking, pharmacophore modeling, and molecular dynamics (MD) studies were performed on 46 ODCs. The constructed 3D-QSAR models exhibited reliable predictability with satisfactory validation parameters, including q² = 0.897, R² = 0.983, r_pred² = 0.948 in a CoMFA model, and q² = 0.922, R² = 0.990, r_pred² = 0.840 in a CoMSIA model. Docking and MD simulations further gave insights into the binding modes of these ODCs with the XO protein. The results indicated that key residues Glu802, Arg880, Asn768, Thr1010, Phe914, and Phe1009 could interact with ODCs by hydrogen bonds, π-π stackings, or hydrophobic interactions, which might be significant for the activity of these XOIs. Four potential hits were virtually screened out using the constructed pharmacophore model in combination with molecular dockings and ADME predictions. The four hits were also found to be relatively stable in the binding pocket by MD simulations. The results in this study might provide effective information for the design and development of novel XOIs.     

Discovery of Novel Focal Adhesion Kinase Inhibitors Using a Hybrid Protocol of Virtual Screening Approach Based on Multicomplex-Based Pharmacophore and Molecular Docking

Focal adhesion kinase (FAK) is a tyrosine kinase that functions as a key orchestrator of signals leading to invasion and metastasis. In the current study, the multicomplex-based pharmacophore (MCBP)-guided method has been suggested to generate a comprehensive pharmacophore of FAK kinase based on seven crystal structures of FAK-inhibitor complexes. In this investigation, a hybrid protocol of virtual screening methods, comprising of pharmacophore model-based virtual screening (PB-VS) and docking-based virtual screening (DB-VS), is used for retrieving new FAK inhibitors from commercially available chemical databases. This hybrid virtual screening approach was then applied to screen several chemical databases, including the Specs (202,408 compounds) database. Thirty-five compounds were selected from the final hits and should be shifted to experimental studies. These results may provide important information for further research of novel FAK inhibitors.