Click estradiol dimers with novel aromatic bridging units: synthesis and anticancer evaluation.

Estradiol dimers (EDs) possess significant anticancer activity by targeting tubulin dynamics. In this study, we synthesised 12 EDs variants via copper-catalysed azide-alkyne cycloaddition (CuAAC) reaction, focusing on structural modifications within the aromatic bridge connecting two estradiol moieties. In vitro testing of these EDs revealed a marked improvement in selectivity towards cancerous cells, particularly for ED1-8. The most active compounds, ED3 (IC50 = 0.38 µM in CCRF-CEM) and ED5 (IC50 = 0.71 µM in CCRF-CEM) demonstrated cytotoxic effects superior to 2-methoxyestradiol (IC50 = 1.61 µM in CCRF-CEM) and exhibited anti-angiogenic properties in an endothelial cell tube-formation model. Cell-based experiments and in vitro assays revealed that EDs interfere with mitotic spindle assembly. Additionally, we proposed an in silico model illustrating the probable binding modes of ED3 and ED5, suggesting that dimers with a simple linker and a single substituent on the aromatic central ring possess enhanced characteristics compared to more complex dimers.

Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts.

Computational design of chiral organic catalysts for asymmetric synthesis is a promising technology that can significantly reduce the material and human resources required for the preparation of enantiopure compounds. Herein, for the modeling of catalysts' enantioselectivity, we propose to use the multi-instance learning approach accounting for multiple catalyst conformers and requiring neither conformer selection nor their spatial alignment. A catalyst was represented by an ensemble of conformers, each encoded by three-dimesinonal (3D) pmapper descriptors. A catalyzed reactant transformation was converted into a single molecular graph, a condensed graph of reaction, encoded by 2D fragment descriptors. A whole chemical reaction was finally encoded by concatenated 3D catalyst and 2D transformation descriptors. The performance of the proposed method was demonstrated in the modeling of the enantioselectivity of homogeneous and phase-transfer reactions and compared with the state-of-the-art approaches.

Triazole-based estradiol dimers prepared via CuAAC from 17α-ethinyl estradiol with five-atom linkers causing G2/M arrest and tubulin inhibition.

Microtubule dynamic is exceptionally sensitive to modulation by small-molecule ligands. Our previous work presented the preparation of microtubule-targeting estradiol dimer (ED) with anticancer activity. In the present study, we explore the effect of selected linkers on the biological activity of the dimer. The linkers were designed as five-atom chains with carbon, nitrogen or oxygen in their centre. In addition, the central nitrogen was modified by a benzyl group with hydroxy or methoxy substituents and one derivative possessed an extended linker length. Thirteen new dimers were subjected to cytotoxicity assay and cell cycle profiling. Dimers containing linker with benzyl moiety substituted with one or more methoxy groups and longer branched ones were found inactive, whereas other structures had comparable efficacy as the original ED (e.g. D1 with IC50 = 1.53 µM). Cell cycle analysis and immunofluorescence proved the interference of dimers with microtubule assembly and mitosis. The proposed in silico model and calculated binding free energy by the MM-PBSA method were closely correlated with in vitro tubulin assembly assay.

Anticancer 5-arylidene-2-(4-hydroxyphenyl)aminothiazol-4(5H)-ones as tubulin inhibitors.

Studying the anticancer activity of 5-arylidene-2-(4-hydroxyphenyl)aminothiazol-4(5H)-ones towards cell lines of different cancer types allowed the identification of hit-compounds inhibiting the growth of daunorubicin- (CEM-DNR, IC50 = 0.32-1.28 µM) and paclitaxel-resistant (K562-TAX, IC50 = 0.21-1.23 µM) cell lines, with favorable therapeutic indexes. The studied compounds induced apoptosis and cellular proliferation in treated CCRF-CEM cells. The hit compounds were shown to induce mitotic arrest by interacting with tubulin, inhibiting its polymerization by binding to the colchicine binding site.

QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach.

Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.

Assessing different thiazolidine and thiazole based compounds as antileishmanial scaffolds.

The compounds from eight different thiazolidine and thiazole series were assessed as potential antileishmanial scaffolds. They were tested for antileishmanial activity against promastigotes of Leishmania major using in vitro primary screen and dose response assays. The compounds from six thiazolidine and thiazole series were identified as the hits with antileishmanial activity against L. major. However, the analyses of structure-activity relations (SARs) showed that the interpretable SARs were obtained only for phenyl-indole hybrids (compounds C1, C2, C3 and C5) as the most effective compounds against L. major promastigotes (IC50 < 10 µM) with low toxicity to human fibroblasts. For the scaffold of these compounds, the most significant SAR patterns were: free N3 position of thiazolidinone core, absence of big fragments at the C5 position of thiazolidinone core and presence of halogen atoms or nitro group in the phenyl ring of phenyl-indole fragment. As previous studies showed that these compounds also have activity against the two Trypanosoma species, Trypanosoma brucei and Trypanosoma gambiense, their scaffold could be associated with a broader antiparasitic activity.

Control of Synthetic Feasibility of Compounds Generated with CReM.

Synthetic feasibility of compounds generated with de novo approaches is one of the main issues, which may limit their applicability. Many of the de novo generation approaches do not address this issue. Here, we studied the recently implemented chemically reasonable mutations approach (CReM) and the ways how one could indirectly control synthetic complexity of generated compounds and how this affected the target scores for Guacamol benchmark tasks. We found a clear trade-off between synthetic complexity and target scores and demonstrated that CReM-based solutions were competitive to reference approaches, which were explicitly biased by synthetic feasibility of generated compounds.

Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores.

Pharmacophore modeling is usually considered as a special type of virtual screening without probabilistic nature. Correspondence of at least one conformation of a molecule to pharmacophore is considered as evidence of its bioactivity. We show that pharmacophores can be treated as one-class machine learning models, and the probability the reflecting model's confidence can be assigned to a pharmacophore on the basis of their precision of active compounds identification on a calibration set. Two schemes (Max and Mean) of probability calculation for consensus prediction based on individual pharmacophore models were proposed. Both approaches to some extent correspond to commonly used consensus approaches like the common hit approach or the one based on a logical OR operation uniting hit lists of individual models. Unlike some known approaches, the proposed ones can rank compounds retrieved by multiple models. These approaches were benchmarked on multiple ChEMBL datasets used for ligand-based pharmacophore modeling and externally validated on corresponding DUD-E datasets. The influence of complexity of pharmacophores and their performance on a calibration set on results of virtual screening was analyzed. It was shown that Max and Mean approaches have superior early enrichment to the commonly used approaches. Thus, a well-performing, easy-to-implement, and probabilistic alternative to existing approaches for pharmacophore-based virtual screening was proposed.

Synthesis and biological evaluation of cis-restrained carbocyclic combretastatin A-4 analogs: Influence of the ring size and saturation on cytotoxic properties.

Combretastatin A-4 (CA-4) is a highly cytotoxic natural product and several derivatives have been prepared which underwent clinical trial. These investigations revealed that the cis-stilbene moiety of the natural product is prone to undergo cis/trans isomerization under physiological conditions, reducing the overall activity of the drug candidates. Herein, we report the preparation of cis-restrained carbocyclic analogs of CA-4. The compounds, which differ by the size and hybridization of the carbocyclic ring have been evaluated for their cytotoxic properties and their ability to inhibit tubulin polymerization. Biological data, supported by molecular docking studies, identified cyclobutenyl and cyclobutyl derivatives of the natural product as highly promising drug candidates.

Virtual Screening Using Pharmacophore Models Retrieved from Molecular Dynamic Simulations.

Pharmacophore models are widely used for the identification of promising primary hits in compound large libraries. Recent studies have demonstrated that pharmacophores retrieved from protein-ligand molecular dynamic trajectories outperform pharmacophores retrieved from a single crystal complex structure. However, the number of retrieved pharmacophores can be enormous, thus, making it computationally inefficient to use all of them for virtual screening. In this study, we proposed selection of distinct representative pharmacophores by the removal of pharmacophores with identical three-dimensional (3D) pharmacophore hashes. We also proposed a new conformer coverage approach in order to rank compounds using all representative pharmacophores. Our results for four cyclin-dependent kinase 2 (CDK2) complexes with different ligands demonstrated that the proposed selection and ranking approaches outperformed the previously described common hits approach. We also demonstrated that ranking, based on averaged predicted scores obtained from different complexes, can outperform ranking based on scores from an individual complex. All developments were implemented in open-source software pharmd.

Correction: Kutlushina, A., et al. Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures. Molecules, 2018, 23, 3094.

The authors would like to add the funding number to the published article [...].

Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures.

Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.

Virtual screening, synthesis and biological evaluation of DNA intercalating antiviral agents.

This paper describes computer-aided design of new anti-viral agents against Vaccinia virus (VACV) potentially acting as nucleic acid intercalators. Earlier obtained experimental data for DNA intercalation affinities and activities against Vesicular stomatitis virus (VSV) have been used to build, respectively, pharmacophore and QSAR models. These models were used for virtual screening of a database of 245 molecules generated around typical scaffolds of known DNA intercalators. This resulted in 12 hits which then were synthesized and tested for antiviral activity against VaV together with 43 compounds earlier studied against VSV. Two compounds displaying high antiviral activity against VaV and low cytotoxicity were selected for further antiviral activity investigations.

Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future.

This paper is an overview of the most significant and impactful interpretation approaches of quantitative structure-activity relationship (QSAR) models, their development, and application. The evolution of the interpretation paradigm from "model → descriptors → (structure)" to "model → structure" is indicated. The latter makes all models interpretable regardless of machine learning methods or descriptors used for modeling. This opens wide prospects for application of corresponding interpretation approaches to retrieve structure-property relationships captured by any models. Issues of separate approaches are discussed as well as general issues and prospects of QSAR model interpretation.

Structure-reactivity modeling using mixture-based representation of chemical reactions.

We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility.

A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure-property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation lgSw=kT+c, where Sw is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high-efficiency QSPR model. The performance of the model is assessed using cross-validation and external test set prediction. Predictive capacity of developed model is compared with COSMO-RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.

Synthesis, biological evaluation and molecular docking studies of 2-piperazin-1-yl-quinazolines as platelet aggregation inhibitors and ligands of integrin αIIbß3.

A series of 2-piperazin-1-yl-quinazolines were synthesized and evaluated for their antiaggregative activity. The synthesized small molecule compounds have potently inhibited platelet aggregation in vitro and blocked FITC-Fg binding to αIIbß3 integrin in a suspension of washed human platelets. The key αIIbß3 protein-ligand interactions were determined in docking experiments and some correlations have been observed between values of the affinity and docking scores.

Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis.

This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach, which is an extension of a recently reported method for interpretation of quantitative structure-activity relationship (QSAR) models. This approach can efficiently be used to reveal structural motifs and the major physicochemical factors affecting the investigated properties. Its efficacy was demonstrated both on the classical Free-Wilson data set and on several data sets with different end points (permeability of the blood-brain barrier, fibrinogen receptor antagonists, acute oral toxicity). Structure-activity patterns extracted from QSAR models with SPCI were in good correspondence with experimentally observed relationships and molecular docking, regardless of the machine learning method used. Comparison of SPCI with the matched molecular pair (MMP) method clearly shows an advantage of our approach over MMP, especially for small or structurally diverse data sets. The developed approach has been implemented in the SPCI software tool with a graphical user interface, which is publicly available at http://qsar4u.com/pages/sirms_qsar.php .

Synthesis, biological evaluation, X-ray molecular structure and molecular docking studies of RGD mimetics containing 6-amino-2,3-dihydroisoindolin-1-one fragment as ligands of integrin αIIbß3.

A series of novel RGD mimetics containing phthalimidine fragment was designed and synthesized. Their antiaggregative activity determined by Born's method was shown to be due to inhibition of fibrinogen binding to αIIbß3. Molecular docking of RGD mimetics to αIIbß3 receptor showed the key interactions in this complex, and also some correlations have been observed between values of biological activity and docking scores. The single crystal X-ray data were obtained for five mimetics.

EasyDock: customizable and scalable docking tool.

Docking of large compound collections becomes an important procedure to discover new chemical entities. Screening of large sets of compounds may also occur in de novo design projects guided by molecular docking. To facilitate these processes, there is a need for automated tools capable of efficiently docking a large number of molecules using multiple computational nodes within a reasonable timeframe. These tools should also allow for easy integration of new docking programs and provide a user-friendly program interface to support the development of further approaches utilizing docking as a foundation. Currently available tools have certain limitations, such as lacking a convenient program interface or lacking support for distributed computations. In response to these limitations, we have developed a module called EasyDock. It can be deployed over a network of computational nodes using the Dask library, without requiring a specific cluster scheduler. Furthermore, we have proposed and implemented a simple model that predicts the runtime of docking experiments and applied it to minimize overall docking time. The current version of EasyDock supports popular docking programs, namely Autodock Vina, gnina, and smina. Additionally, we implemented a supplementary feature to enable docking of boron-containing compounds, which are not inherently supported by Vina and smina, and demonstrated its applicability on a set of 55 PDB protein-ligand complexes.