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.     

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.     

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 pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating "Hypo1", it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC(50)) data thus successfully validating "Hypo1" by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.     

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.     

Rendering Conventional Molecular Fingerprints for Virtual Screening Independent of Molecular Complexity and Size Effects

Molecular complexity and size effects represent a known complication of fingerprint similarity searching and virtual screening that often leads to an increase in false‐positive rates and a decrease in hit rates. In standard fingerprints, differences in the complexity of reference and database molecules lead to different fingerprint bit densities, which negatively affects similarity search calculations, in particular, when fingerprints of reference molecules have higher bit density than corresponding fingerprints of database compounds. In pharmaceutical research, this is the case in many practical virtual screening applications when chemically optimized reference molecules are used. Herein we introduce an intuitive computational method to make standard fingerprints such as structural keys or pharmacophore feature fingerprints independent of molecular complexity and size effects. This is achieved by applying the concept of 'balanced codes' originating in computer science. Following this approach, binary fingerprints are transformed by incorporating the complement of their bit patterns. This straightforward transformation produces fingerprint representations with characteristic bit patterns that have exactly half of their bit positions set on, corresponding to a constant bit density of 50 % for all test compounds, regardless of their complexity and size. In similarity search calculations in the presence of complexity effects of increasing magnitude, transformed structural key and pharmacophore fingerprints display consistently better performance than their unmodified counterparts and recover active compounds in cases where the original fingerprints fail.     

New inhibitors of the Tat-TAR RNA interaction found with a "fuzzy" pharmacophore model

TAR RNA is a potential target for AIDS therapy. Ligand-based virtual screening was performed to retrieve novel scaffolds for RNA-binding molecules capable of inhibiting the Tat-TAR interaction, which is essential for HIV replication. We used a "fuzzy" pharmacophore approach (SQUID) and an alignment-free pharmacophore method (CATS3D) to carry out virtual screening of a vendor database of small molecules and to perform "scaffold-hopping". A small subset of 19 candidate molecules were experimentally tested for TAR RNA binding in a fluorescence resonance energy transfer (FRET) assay. Both methods retrieved molecules that exhibited activities comparable to those of the reference molecules acetylpromazine and chlorpromazine, with the best molecule showing ten times better binding behavior (IC50 = 46 microM). The hits had molecular scaffolds different from those of the reference molecules.     

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods

Owing to several mutations, the oncogene Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) is activated in the majority of cancers, and targeting it has been pharmacologically challenging. In this study, using an in silico approach comprised of pharmacophore modeling, molecular docking, and molecular dynamics simulations, potential KRAS G12D inhibitors were investigated. A ligand-based common feature pharmacophore model was generated to identify the framework necessary for effective KRAS inhibition. The chemical features in the selected pharmacophore model comprised two hydrogen bond donors, one hydrogen bond acceptor, two aromatic rings and one hydrophobic feature. This model was used for screening in excess of 214,000 compounds from InterBioScreen (IBS) and ZINC databases. Eighteen compounds from the IBS and ten from the ZINC database mapped onto the pharmacophore model and were subjected to molecular docking. Molecular docking results highlighted a higher affinity of four hit compounds towards KRAS G12D in comparison to the reference inhibitor, BI-2852. Sequential molecular dynamics (MD) simulation studies revealed all four hit compounds them possess higher KRAS G12D binding free energy and demonstrate stable polar interaction with key residues. Further, Principal Component Analysis (PCA) analysis of the hit compounds in complex with KRAS G12D also indicated stability. Overall, the research undertaken provides strong support for further in vitro testing of these newly identified KRAS G12D inhibitors, particularly Hit1 and Hit2.     

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.     

Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC₅₀ units from measured inhibition affinities and a Pearson’s correlation coefficient R² of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.     

Novel Natural Inhibitors of CYP1A2 Identified by in Silico and in Vitro Screening

Inhibition of cytochrome P450 (CYP) is a major cause of herb-drug interactions. The CYP1A2 enzyme plays a major role in the metabolism of drugs in humans. Its broad substrate specificity, as well as its inhibition by a vast array of structurally diverse herbal active ingredients, has indicated the possibility of metabolic herb-drug interactions. Therefore nowadays searching inhibitors for CYP1A2 from herbal medicines are drawing much more attention by biological, chemical and pharmological scientists. In our work, a pharmacophore model as well as the docking technology is proposed to screen inhibitors from herbal ingredients data. Firstly different pharmaphore models were constructed and then validated and modified by 202 herbal ingredients. Secondly the best pharmaphore model was chosen to virtually screen the herbal data (a curated database of 989 herbal compounds). Then the hits (147 herbal compounds) were continued to be filtered by a docking process, and were tested in vitro successively. Finally, five of eighteen candidate compounds (272, 284, 300, 616 and 817) were found to have inhibition of CYP1A2 activity. The model developed in our study is efficient for in silico screening of large herbal databases in the identification of CYP1A2 inhibitors. It will play an important role to prevent the risk of herb-drug interactions at an early stage of the drug development process.     

Combined Structure-Based Pharmacophore and 3D-QSAR Studies on Phenylalanine Series Compounds as TPH1 Inhibitors

Tryptophan hydroxylase-1 (TPH1) is a key enzyme in the synthesis of serotonin. As a neurotransmitter, serotonin plays important physiological roles both peripherally and centrally. In this study, a combination of ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship (QSAR) of known TPH1 inhibitors. A multicomplex-based pharmacophore (MCBP) guided method has been suggested to generate a comprehensive pharmacophore of TPH1 kinase based on three crystal structures of TPH1-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 32 structurally diverse substituted phenylalanine derivatives. The QSAR analyses have been performed on these TPH1 inhibitors based on the MCBP guided alignment. These results may provide important information for further design and virtual screening of novel TPH1 inhibitors.     

The Discovery of Potentially Selective Human Neuronal Nitric Oxide Synthase (nNOS) Inhibitors: A Combination of Pharmacophore Modelling,CoMFA, Virtual Screening and Molecular Docking Studies

Neuronal nitric oxide synthase (nNOS) plays an important role in neurotransmission and smooth muscle relaxation. Selective inhibition of nNOS over its other isozymes is highly desirable for the treatment of neurodegenerative diseases to avoid undesirable effects. In this study, we present a workflow for the identification and prioritization of compounds as potentially selective human nNOS inhibitors. Three-dimensional pharmacophore models were constructed based on a set of known nNOS inhibitors. The pharmacophore models were evaluated by Pareto surface and CoMFA (Comparative Molecular Field Analysis) analyses. The best pharmacophore model, which included 7 pharmacophore features, was used as a search query in the SPECS database (SPECS^®, Delft, The Netherlands). The hit compounds were further filtered by scoring and docking. Ten hits were identified as potential selective nNOS inhibitors.     

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.     

Three-dimensional pharmacophore design and biochemical screening identifies substituted 1,2,4-triazoles as inhibitors of the annexin A2-S100A10 protein interaction

Protein interactions are increasingly appreciated as targets in small‐molecule drug discovery. The interaction between the adapter protein S100A10 and its binding partner annexin A2 is a potentially important drug target. To obtain small‐molecule starting points for inhibitors of this interaction, a three‐dimensional pharmacophore model was constructed from the X‐ray crystal structure of the complex between S100A10 and annexin A2. The pharmacophore model represents the favourable hydrophobic and hydrogen bond interactions between the two partners, as well as spatial and receptor site constraints (excluded volume spheres). Using this pharmacophore model, UNITY flex searches were carried out on a 3D library of 0.7 million commercially available compounds. This resulted in 568 hit compounds. Subsequently, GOLD docking studies were performed on these hits, and a set of 190 compounds were purchased and tested biochemically for inhibition of the protein interaction. Three compounds of similar chemical structure were identified as genuine inhibitors of the binding of annexin A2 to S100A10. The binding modes predicted by GOLD were in good agreement with their UNITY‐generated conformations. We synthesised a series of analogues revealing areas critical for binding. Thus computational predictions and biochemical screening can be used successfully to derive novel chemical classes of protein–protein interaction blockers.     

Virtual Screening of Specific Insulin-Like Growth Factor 1 Receptor (IGF1R) Inhibitors from the National Cancer Institute (NCI) Molecular Database

Insulin-like growth factor 1 receptor (IGF1R) is an attractive drug target for cancer therapy and research on IGF1R inhibitors has had success in clinical trials. A particular challenge in the development of specific IGF1R inhibitors is interference from insulin receptor (IR), which has a nearly identical sequence. A few potent inhibitors that are selective for IGF1R have been discovered experimentally with the aid of computational methods. However, studies on the rapid identification of IGF1R-selective inhibitors using virtual screening and confidence-level inspections of ligands that show different interactions with IGF1R and IR in docking analysis are rare. In this study, we established virtual screening and binding-mode prediction workflows based on benchmark results of IGF1R and several kinase receptors with IGF1R-like structures. We used comprehensive analysis of the known complexes of IGF1R and IR with their binding ligands to screen specific IGF1R inhibitors. Using these workflows, 17 of 139,735 compounds in the NCI (National Cancer Institute) database were identified as potential specific inhibitors of IGF1R. Calculations of the potential of mean force (PMF) with GROMACS were further conducted for three of the identified compounds to assess their binding affinity differences towards IGF1R and IR.     

A Virtual Screening Study of the 18 kDa Translocator Protein using Pharmacophore Models Combined with 3D‐QSAR Studies

In the present study, we considered various pharmacophore hypotheses for TSPO ligands and an optimal one was selected on the basis of 3D‐QSAR studies. This hypothesis was used in a ligand‐based virtual screening study on the Maybridge database with the aim of identifying new TSPO ligands. Binding assays revealed that all selected compounds displayed TSPO affinity at 10 μM , and among them two compounds exhibited sub‐micromolar K_i values. These results validated our applied methodologies, and the two compounds with sub‐micromolar affinity could be used as interesting leads for the development of new active TSPO ligands.     

Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries

There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR⋅uPA, TEAD4⋅Yap1, and Ca_Vα⋅Ca_Vβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR⋅uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4⋅Yap1. Virtual screening of conformationally restricted compounds targeting uPAR⋅uPA and TEAD4⋅Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions.