Synthesis of Bitopic Ligands as Potent Dopamine D2 Receptor Agonists

In this study, we designed and synthesized twelve bitopic ligands as dopamine D₂ receptor (D₂R) agonists. The forskolin-induced cAMP accumulation assay revealed that all the finial compounds are able to activate D₂R. Furthermore, bitopic ligand N-((trans)-4-(((2,3-dihydro-1H-inden-2-yl)(propyl)amino)methyl)cyclo-hexyl)-1H-pyrrolo[2,3-b]pyridine-2-carboxamide ( 11 b ) showed 21-fold higher potency than lead compound propyl aminoindane ( 2 ) and 17-fold higher subtype selectivity for D₂R over D₄R, indicating that the optimal length of spacer affects the D₂R functionality. Molecular modeling study exhibited that 11 b formed an electrostatic interaction and two H-bonds with amino acid Asp114, which contributes significantly to the D₂R functional activity. Taken together, we discovered a bitopic ligand 11 b as potent D₂R agonist, which may be used as a tool compound for further study.     

Novel High Affinity Sigma-1 Receptor Ligands from Minimal Ensemble Docking-Based Virtual Screening

Sigma-1 receptor (S1R) is an intracellular, multi-functional, ligand operated protein that also acts as a chaperone. It is considered as a pluripotent drug target in several pathologies. The publication of agonist and antagonist bound receptor structures has paved the way for receptor-based in silico drug design. However, recent studies on this subject payed no attention to the structural differences of agonist and antagonist binding. In this work, we have developed a new ensemble docking-based virtual screening protocol utilizing both agonist and antagonist bound S1R structures. This protocol was used to screen our in-house compound library. The S1R binding affinities of the 40 highest ranked compounds were measured in competitive radioligand binding assays and the sigma-2 receptor (S2R) affinities of the best S1R binders were also determined. This way three novel high affinity S1R ligands were identified and one of them exhibited a notable S1R/S2R selectivity.     

Comparison of Structure‐ and Ligand‐Based Virtual Screening Protocols Considering Hit List Complementarity and Enrichment Factors

Structure‐ and ligand‐based virtual‐screening methods (docking, 2D‐ and 3D‐similarity searching) were analysed for their effectiveness in virtual screening against four different targets: angiotensin‐converting enzyme (ACE), cyclooxygenase 2 (COX‐2), thrombin and human immunodeficiency virus 1 (HIV‐1) protease. The relative performance of the tools was compared by examining their ability to recognise known active compounds from a set of actives and nonactives. Furthermore, we investigated whether the application of different virtual‐screening methods in parallel provides complementary or redundant hit lists. Docking was performed with GOLD, Glide, FlexX and Surflex. The obtained docking poses were rescored by using nine different scoring functions in addition to the scoring functions implemented as objective functions in the docking algorithms. Ligand‐based virtual screening was done with ROCS (3D‐similarity searching), Feature Trees and Scitegic Functional Fingerprints (2D‐similarity searching). The results show that structure‐ and ligand‐based virtual‐screening methods provide comparable enrichments in detecting active compounds. Interestingly, the hit lists that are obtained from different virtual‐screening methods are generally highly complementary. These results suggest that a parallel application of different structure‐ and ligand‐based virtual‐screening methods increases the chance of identifying more (and more diverse) active compounds from a virtual‐screening campaign.     

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.     

Discovery of Novel Biased Opioid Receptor Ligands through Structure-Based Pharmacophore Virtual Screening and Experiment

G_i-protein-biased agonists with minimal β-arrestin recruitment represent opportunities to overcome the serious adverse effects of human mu opioid receptor (μ-OR) agonists and developing alternative and safe treatments for pain. In order to discover novel non-morphinan opioid receptor agonists, we applied hierarchical virtual screening of our in-house database against a pharmacophore based on modeling the active conformations of opioid receptors. We discovered an initial hit compound, a novel μ-OR agonist with a pyrazoloisoquinoline scaffold. We applied computational R-group screening to this compound and synthesized 14 derivatives predicted to be the best. Of these, a new G_i-protein-biased compound, 1-{5-(3-chlorophenyl)-7,8-dimethoxy-3-[4-(methylsulfonyl)benzyl]-3H-pyrazolo[3,4-c]isoquinolin-1-yl}-N,N-dimethylmethanamine, showed an EC₅₀ value of 179 nm against the μ-OR. This resulted in significant pain relief for mice in the phase II period of formalin response tests. This study provides a new strategy to identify diverse sets of promising compounds that might prove useful for the development of drugs that target other G-protein-coupled receptors.     

Structure‐Based Virtual Screening and Electrophysiological Evaluation of New Chemotypes of Kv1.5 Channel Blockers

Atrial fibrillation (AF) is the most prevalent nonfatal cardiac rhythm disorder associated with an increased risk of heart failure and stroke. Considering the ventricular side effects induced by anti‐arrhythmic agents in current use, K_v1.5 channel blockers have attracted a great deal of deliberation owing to their selective actions on atrial electrophysiology. Herein we report new chemotypes of K_v1.5 channel blockers that were identified through a combination of structure‐based virtual screening and in silico druglike property prediction including six scoring functions, as well as electrophysiological evaluation. Among them, five of the 18 compounds exhibited >50 % blockade ratio at 10 μM , and have structural features different from conventional K_v1.5 channel blockers. These novel scaffolds could serve as hits for further optimization and SAR studies for the discovery of selective agents to treat AF.     

A Series of 18F‐Labelled Pyridinylphenyl Amides as Subtype‐Selective Radioligands for the Dopamine D3 Receptor

Synthesis, biological activity, and structure–selectivity relationship (SSR) studies of a novel series of potential dopamine D3 receptor radioligands as imaging agents for positron emission tomography (PET) are reported. Considering a structurally diverse library of D3 ligands, SSR studies were performed for a new series of fluorinated pyridinylphenyl amides using CoMFA and CoMSIA methods. The in vitro D3 affinities of the predicted series of biphenyl amide ligands 9 a – d revealed single‐digit to sub‐nanomolar potencies (K_i=0.52–1.6 nM ), displaying excellent D3 selectivity over the D2 subtype of 110‐ to 210‐fold for the test compounds 9 a – c . Radiofluorination by nucleophilic substitution of Br or NO₂ by ¹⁸F led to radiochemical yields of 66–92 % for [¹⁸F] 9 a – d . However, the specific activities of [¹⁸F] 9 b and [¹⁸F] 9 d were insufficient, rendering their use for in vivo studies impossible. Biodistribution studies of [¹⁸F] 9 a and [¹⁸F] 9 c using rat brain autoradiography revealed accumulation in the ventricles, thus indicating insufficient biokinetic properties of [¹⁸F] 9 a and [¹⁸F] 9 c for D3 receptor imaging in vivo.     

A Structure‐Based Virtual Screening Approach toward the Discovery of Histone Deacetylase Inhibitors: Identification of Promising Zinc‐Chelating Groups

The inhibitors of histone deacetylases (HDACs) have drawn a great deal of attention due to their promising potential as small‐molecule therapeutics for the treatment of cancer. By means of virtual screening with docking simulations under consideration of the effects of ligand solvation, we were able to identify six novel HDAC inhibitors with IC₅₀ values ranging from 1 to 100 μM . These newly identified inhibitors are structurally diverse and have various chelating groups for the active site zinc ion, including N‐[1,3,4]thiadiazol‐2‐yl sulfonamide, N‐thiazol‐2‐yl sulfonamide, and hydroxamic acid moieties. The former two groups are included in many drugs in current clinical use and have not yet been reported as HDAC inhibitors. Therefore, they can be considered as new inhibitor scaffolds for the development of anticancer drugs by structure–activity relationship studies to improve the inhibitory activities against HDACs. Interactions with the HDAC1 active site residues responsible for stabilizing these new inhibitors are addressed in detail.     

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.     

Synthesis, 3D‐QSAR,and Structural Modeling of Benzolactam Derivatives with Binding Affinity for the D2 and D3 Receptors

A series of 37 benzolactam derivatives were synthesized, and their respective affinities for the dopamine D₂ and D₃ receptors evaluated. The relationships between structures and binding affinities were investigated using both ligand‐based (3D‐QSAR) and receptor‐based methods. The results revealed the importance of diverse structural features in explaining the differences in the observed affinities, such as the location of the benzolactam carbonyl oxygen, or the overall length of the compounds. The optimal values for such ligand properties are slightly different for the D₂ and D₃ receptors, even though the binding sites present a very high degree of homology. We explain these differences by the presence of a hydrogen bond network in the D₂ receptor which is absent in the D₃ receptor and limits the dimensions of the binding pocket, causing residues in helix 7 to become less accessible. The implications of these results for the design of more potent and selective benzolactam derivatives are presented and discussed.     

Scaffold Hopping with Virtual Screening from IP3 to a Drug‐Like Partial Agonist of the Inositol Trisphosphate Receptor

Inositol 1,4,5‐trisphosphate (IP₃) is a universal signalling molecule that releases calcium from stores within cells by activating the IP₃ receptor. Although chemical tools that modulate the IP₃ receptor exist, none is ideal due to trade offs between potency, selectivity and cell permeability, and their chemical properties make them challenging starting points for optimisation. Therefore, to find new leads, we used virtual screening to scaffold hop from IP₃ by using the program ROCS to perform a 3D ligand‐based screen of the ZINC database of purchasable compounds. We then used the program FRED to dock the top‐ranking hits into the IP₃ binding pocket of the receptor. We tested the 12 highest‐scoring hits in a calcium‐release bioassay and identified SI‐9 as a partial agonist. SI‐9 competed with [³H]IP₃ binding, and reduced histamine‐induced calcium signalling in HeLa cells. SI‐9 has a novel 2D scaffold that represents a tractable lead for designing improved IP₃ receptor modulators.     

The Azulene Framework as a Novel Arene Bioisostere: Design of Potent Dopamine D4 Receptor Ligands Inducing Penile Erection

Blue makes it happen : The non‐uniform charge distribution of the blue colored azulene framework is highly suitable for the bioisosteric replacement of bicyclic heteroarene moieties. Showing an analogous binding mode as heterocyclic dopamine D4 receptor‐selective lead compounds, the induction of penile erection in rats over a greater range of doses indicates a putative advantage of the rationally developed azulene derivative 2 b over apomorphine.

     

Maximizing the Performance of Similarity-Based Virtual Screening Methods by Generating Synergy from the Integration of 2D and 3D Approaches

Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach. On test sets for virtual screening, covering 50 pharmaceutically relevant proteins, the integrated approach, using sets of five query molecules, yielded, on average, an area under the receiver operating characteristic curve (AUC) of 0.84, an early enrichment among the top 1% of ranked compounds (EF1%) of 53.82 and a scaffold recovery rate among the top 1% of ranked compounds (SRR1%) of 0.50. In comparison, the 2D and 3D methods on their own (when using a single query molecule) yielded AUC values of 0.68 and 0.54, EF1% values of 19.96 and 17.52, and SRR1% values of 0.20 and 0.17, respectively. In conclusion, based on these results, the integration of 2D and 3D methods, via a (balanced) parallel selection strategy, is recommended, and, in particular, when combined with multi-query screening.     

Similarity‐Based Virtual Screening with a Bayesian Inference Network

An inference network model for molecular similarity searching: The similarity search problem is modeled using inference or evidential reasoning under uncertainty. The inference network model treats similarity searching as an evidential reasoning process in which multiple sources of evidence about compounds and reference structures are combined to estimate resemblance probabilities.

     

Studies of New Fused Benzazepine as Selective Dopamine D3 Receptor Antagonists Using 3D-QSAR, Molecular Docking and Molecular Dynamics

In recent years, great interest has been paid to the development of compounds with high selectivity for central dopamine (DA) D3 receptors, an interesting therapeutic target in the treatment of different neurological disorders. In the present work, based on a dataset of 110 collected benzazepine (BAZ) DA D3 antagonists with diverse kinds of structures, a variety of in silico modeling approaches, including comparative molecular field analysis (CoMFA), comparative similarity indices analysis (CoMSIA), homology modeling, molecular docking and molecular dynamics (MD) were carried out to reveal the requisite 3D structural features for activity. Our results show that both the receptor-based (Q(2) = 0.603, R(2) (ncv) = 0.829, R(2) (pre) = 0.690, SEE = 0.316, SEP = 0.406) and ligand-based 3D-QSAR models (Q(2) = 0.506, R(2) (ncv) =0.838, R(2) (pre) = 0.794, SEE = 0.316, SEP = 0.296) are reliable with proper predictive capacity. In addition, a combined analysis between the CoMFA, CoMSIA contour maps and MD results with a homology DA receptor model shows that: (1) ring-A, position-2 and R(3) substituent in ring-D are crucial in the design of antagonists with higher activity; (2) more bulky R(1) substituents (at position-2 of ring-A) of antagonists may well fit in the binding pocket; (3) hydrophobicity represented by MlogP is important for building satisfactory QSAR models; (4) key amino acids of the binding pocket are CYS101, ILE105, LEU106, VAL151, PHE175, PHE184, PRO254 and ALA251. To our best knowledge, this work is the first report on 3D-QSAR modeling of the new fused BAZs as DA D3 antagonists. These results might provide information for a better understanding of the mechanism of antagonism and thus be helpful in designing new potent DA D3 antagonists.