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.     

组合化学及其应用

组合化学是一种快速大量的合成技术。简明地介绍了组合化学的基本原理、化合物库的设计与制备方法、活性分子结构的表征以及组合化学在许多领域里的应用。     

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.     

Potential Dental Biofilm Inhibitors: Dynamic Combinatorial Chemistry Affords Sugar-Based Molecules that Target Bacterial Glucosyltransferase

We applied dynamic combinatorial chemistry (DCC) to find novel ligands of the bacterial virulence factor glucosyltransferase (GTF) 180. GTFs are the major producers of extracellular polysaccharides, which are important factors in the initiation and development of cariogenic dental biofilms. Following a structure-based strategy, we designed a series of 36 glucose- and maltose-based acylhydrazones as substrate mimics. Synthesis of the required mono- and disaccharide-based aldehydes set the stage for DCC experiments. Analysis of the dynamic combinatorial libraries (DCLs) by UPLC-MS revealed major amplification of four compounds in the presence of GTF180. Moreover, we found that derivatives of the glucose-acceptor maltose at the C1-hydroxy group act as glucose-donors and are cleaved by GTF180. The synthesized hits display medium to low binding affinity (K_D values of 0.4–10.0 mm ) according to surface plasmon resonance. In addition, they were investigated for inhibitory activity in GTF-activity assays. The early-stage DCC study reveals that careful design of DCLs opens up easy access to a broad class of novel compounds that can be developed further as potential inhibitors.     

组合化学及其在新农药开发中的应用

结合医药领域的成功经验概述了组合化学的基本概念、化合物库的合成技术与分析、筛选方法，综述了组合化学方法在发现与优化新农药先导化合物中的研究进展，介绍了作者利用互补分子反应活性与分子识别技术优化除草活性原卟啉原氧化酶抑制剂的研究工作，展望了组合化学在新农药创制中的应用前景。     

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.     

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.     

Prospective Virtual Screening in a Sparse Data Scenario: Design of Small‐Molecule TLR2 Antagonists

Toll‐like receptors (TLRs) are critical signaling molecules with roles in various severe clinical conditions such as sepsis and rheumatoid arthritis, and have therefore been advocated as promising drug targets for the treatment of these diseases. The aim of this study was to discover small‐molecule antagonists of TLR2 by computer‐aided drug design. This goal poses several challenges due to the lack of available data on TLR2 modulators. To overcome these hurdles we developed a combined structure‐ and ligand‐based virtual screening approach. First, we calculated molecular interaction fields of the TLR2 binding site to derive a structure‐based 3D pharmacophore, which was then used for virtual screening. We then performed a two‐step shape‐ and feature‐based similarity search using known TLR2 ligands as query structures. A selection of virtual screening hits was biologically tested in a cell‐based assay for TLR2 signaling inhibition, leading to the identification of several compounds with antagonistic activity (IC₅₀ values) in the low‐micromolar range.     

Virtual Screening and Biological Characterization of Novel Histone Arginine Methyltransferase PRMT1 Inhibitors

Lysine and arginine methyltransferases participate in the posttranslational modification of histones and regulate key cellular functions. Protein arginine methyltransferase 1 (PRMT1) has been identified as an essential component of mixed lineage leukemia (MLL) oncogenic complexes, revealing its potential as a novel therapeutic target in human cancer. The first potent arginine methyltransferase inhibitors were recently discovered by random‐ and target‐based screening approaches. Herein we report virtual and biological screening for novel inhibitors of PRMT1. Structure‐based virtual screening (VS) of the Chembridge database composed of 328 000 molecules was performed with a combination of ligand‐ and target‐based in silico approaches. Nine inhibitors were identified from the top‐scored docking solutions; these were experimentally tested using human PRMT1 and an antibody‐based assay with a time‐resolved fluorescence readout. Among several aromatic amines, an aliphatic amine and an amide were also found to be active in the micromolar range.     

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.