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.

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.

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.

Activity of 1-aryl-4-(naphthalimidoalkyl) piperazine derivatives against Leishmania major and Leishmania mexicana.

A series of 1-aryl-4-(phthalimidoalkyl) piperazines and 1-aryl-4-(naphthalimidoalkyl) piperazines were retrieved from a proprietary library based on their high structural similarity to haloperidol, an antipsychotic with antiparasitic activity, and assessed as potential antileishmanial scaffolds. Selected compounds were tested for antileishmanial activity against promastigotes of Leishmania major and Leishmania mexicana in dose-response assays. Two of the 1-aryl-4-(naphthalimidoalkyl) piperazines (compounds 10 and 11) were active against promastigotes of both Leishmania species without being toxic to human fibroblasts. Their activity was found to correlate with the length of their alkyl chains. Further analyses showed that compound 11 was also active against intracellular amastigotes of both Leishmania species. In promastigotes of both Leishmania species, compound 11 induced collapse of the mitochondrial electrochemical potential and increased the intracellular Ca2+ concentration. Therefore, it may serve as a promising lead compound for the development of novel antiparasitic drugs.

An identification of MARK inhibitors using high throughput MALDI-TOF mass spectrometry.

MAP/microtubule affinity-regulating kinases (MARKs) were recently identified as potential drug targets for Alzheimer's disease (AD) due to their role in pathological hyperphosphorylation of tau protein. Hyperphosphorylated tau has decreased affinity for microtubule binding, impairing their stability and associated functions. Destabilization of microtubules in neuronal cells leads to neurodegeneration, and microtubule-unbound tau forms neurofibrillary tangles, one of the primary hallmarks of AD. Many phosphorylation sites of tau protein have been identified, but phosphorylation at Ser262, which occurs in early stages of AD, plays a vital role in the pathological hyperphosphorylation of tau. It has been found that Ser262 is phosphorylated by MARK4, which is currently an intensively studied target for treating Alzheimer's disease and other neurodegenerative diseases. Our present study aimed to develop a high throughput compatible assay to directly detect MARK enzymatic activity using echoacoustic transfer and MALDI-TOF mass spectrometer. We optimized the assay for all four isoforms of MARK and validated its use for identifying potential inhibitors by the screening of 1280 compounds from the LOPAC®1280 International (Library Of Pharmacologically Active Compounds). Six MARK4 inhibitors with IC50 < 1 µM were identified. To demonstrate their therapeutic potential, active compounds were further tested for MARK4 selectivity and ability to cross the blood-brain barrier. Lastly, the molecular docking with the most active inhibitors to predict their interaction with MARK4 was performed.

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.

Multiple Conformer Descriptors for QSAR Modeling.

The most widely used QSAR approaches are mainly based on 2D molecular representation which ignores stereoconfiguration and conformational flexibility of compounds. 3D QSAR uses a single conformer of each compound which is difficult to choose reasonably. 4D QSAR uses multiple conformers to overcome the issues of 2D and 3D methods. However, many of existing 4D QSAR models suffer from the necessity to pre-align conformers, while alignment-independent approaches often ignore stereoconfiguration of compounds. In this study we propose a QSAR modeling approach based on transforming chirality-aware 3D pharmacophore descriptors of individual conformers into a set of latent variables representing the whole conformer set of a molecule. This is achieved by clustering together all conformers of all training set compounds. The final representation of a compound is a bit string encoding cluster membership of its conformers. In our study we used Random Forest, but this representation can be used in combination with any machine learning method. We compared this approach with conventional 2D and 3D approaches using multiple data sets and investigated the sensitivity of the approach proposed to tuning parameters: number of conformers and clusters.

Benchmarks for interpretation of QSAR models.

Interpretation of QSAR models is useful to understand the complex nature of biological or physicochemical processes, guide structural optimization or perform knowledge-based validation of QSAR models. Highly predictive models are usually complex and their interpretation is non-trivial. This is particularly true for modern neural networks. Various approaches to interpretation of these models exist. However, it is difficult to evaluate and compare performance and applicability of these ever-emerging methods. Herein, we developed several benchmark data sets with end-points determined by pre-defined patterns. These data sets are purposed for evaluation of the ability of interpretation approaches to retrieve these patterns. They represent tasks with different complexity levels: from simple atom-based additive properties to pharmacophore hypothesis. We proposed several quantitative metrics of interpretation performance. Applicability of benchmarks and metrics was demonstrated on a set of conventional models and end-to-end graph convolutional neural networks, interpreted by the previously suggested universal ML-agnostic approach for structural interpretation. We anticipate these benchmarks to be useful in evaluation of new interpretation approaches and investigation of decision making of complex "black box" models.

The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds.

Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.

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.

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.

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.

CReM: chemically reasonable mutations framework for structure generation.

Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.

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.

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.

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 [...].

Bimolecular Nucleophilic Substitution Reactions: Predictive Models for Rate Constants and Molecular Reaction Pairs Analysis.

Here, we report the data visualization, analysis and modeling for a large set of 4830 SN 2 reactions the rate constant of which (logk) was measured at different experimental conditions (solvent, temperature). The reactions were encoded by one single molecular graph - Condensed Graph of Reactions, which allowed us to use conventional chemoinformatics techniques developed for individual molecules. Thus, Matched Reaction Pairs approach was suggested and used for the analyses of substituents effects on the substrates and nucleophiles reactivity. The data were visualized with the help of the Generative Topographic Mapping approach. Consensus Support Vector Regression (SVR) model for the rate constant was prepared. Unbiased estimation of the model's performance was made in cross-validation on reactions measured on unique structural transformations. The model's performance in cross-validation (RMSE=0.61 logk units) and on the external test set (RMSE=0.80) is close to the noise in data. Performances of the local models obtained for selected subsets of reactions proceeding in particular solvents or with particular type of nucleophiles were similar to that of the model built on the entire set. Finally, four different definitions of model's applicability domains for reactions were examined.

Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context.

The study focused on QSAR model interpretation. The goal was to develop a workflow for the identification of molecular fragments in different contexts important for the property modelled. Using a previously established approach - Structural and physicochemical interpretation of QSAR models (SPCI) - fragment contributions were calculated and their relative influence on the compounds' properties characterised. Analysis of the distributions of these contributions using Gaussian mixture modelling was performed to identify groups of compounds (clusters) comprising the same fragment, where these fragments had substantially different contributions to the property studied. SMARTSminer was used to detect patterns discriminating groups of compounds from each other and visual inspection if the former did not help. The approach was applied to analyse the toxicity, in terms of 40 hour inhibition of growth, of 1984 compounds to Tetrahymena pyriformis. The results showed that the clustering technique correctly identified known toxicophoric patterns: it detected groups of compounds where fragments have specific molecular context making them contribute substantially more to toxicity. The results show the applicability of the interpretation of QSAR models to retrieve reasonable patterns, even from data sets consisting of compounds having different mechanisms of action, something which is difficult to achieve using conventional pattern/data mining approaches.