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The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys® database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19., We cover diverse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.

New iron(II) complexes with 2,6-bis(benzimidazol-2-yl)pyridine (L), in particular, [FeL2]A2·nH2O (A = Br− (I), NO3− (II), C2N3− (III); n = 1 (I), 0.5 (II), 2 (III)) and [NiL2]Br2·1.23H2O·3.33EtOH (IV), have been synthesized and studied using single-crystal and powder X-ray diffraction techniques, UV-vis (diffuse reflection), IR and Mossbauer spectroscopy, as well as static magnetic susceptibility measurements. According to the experimental μeff(T) curves all the studied iron(II) compounds exhibit 1A1 ↔ 5T2 spin-crossover.

New coordination compounds of iron(II) sulfate, hexafluorosilicate, and perrhenate with 2,6-bis(benzimidazole-2-yl)pyridine (L), namely, [FeL2]SO4 ∙ H2O (I), [FeL2]SiF6 ∙ H2O (II), and [FeL2](ReO4)2 ∙ 1.5H2O (III) have been synthesized. The complexes have been studied by static magnetic susceptibility, electronic (diffuse reflectance), IR, and Mossbauer spectroscopy, and X-ray diffraction. The μeff(T) study of dehydrated complexes I–III (1–3) within the range 80–520 K has shown the spin transition 1А1 ⇔ 5Т2. A two-stage transition is observed in complex 1, whereas one-stage transitions occur in complexes 2 and 3. The forward spin transition temperatures (Тс↑) of complexes 1–3 are 423 and 503, 476, 362 K, respectively. Complex 3 sustains an abrupt spin transition with a hysteresis in the μeff(T) curve (ΔT = 21 K).

Background:Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.Objectives:In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).Methods:The CoMPARA list of screened chemicals built on CERAPP’s list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.Results:The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.Discussion:The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program’s Integrated Chemical Environment. https://doi.org/10.1289/EHP5580

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.

Here, we report an application of the multi-instance learning approach to predictive modeling of enantioselectivity of chiral catalysts. Catalysts were represented by ensembles of conformations encoded­ by the pmapper physicochemical descriptors capturing stereoconfiguration of the molecule. Each catalyzed chemical reaction was transformed to a condensed graph of reaction for which ISIDA fragment descriptors were generated. This approach does not require any conformations’ alignment and can potentially be used for a diverse set of catalysts bearing different scaffolds. Its efficiency has been demonstrated in predicting the selectivity of BINOL-derived phosphoric acid catalysts in asymmetric thiol addition to N-acylimines and benchmarked with previously reported models.

International audience; The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain "Big Data" for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data.

In this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules (“fragments”) in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured experimentally using an NMR technique. A Support Vector Classification model was built on the obtained data using the ISIDA fragment descriptors. The analysis revealed 34 outliers: experimental issues were retrospectively identified for 28 of them. The updated model performs well in 5-fold cross-validation (balanced accuracy = 0.78). The datasets are available on the Zenodo platform (DOI:10.5281/zenodo.4767511) and the model is available on the website of the Laboratory of Chemoinformatics.

International audience; Treatment of hydrogen sulfide (H2S) is important in many industrial processes including oil refineries, natural and biogas processing, and coal gasification. The most mature technology for the selective capture of H2S is based on its absorption by chemical or physical solvents. However, only several compounds are currently used as physical (co)solvents in industry, and the search for new ones is an important task. The experimental screening of physical (co)solvents requires much time and many resources, while solubility modeling might enable one to reduce the number of solvents for the experimental evaluation. In this study, a workflow for the in silico discovery of new physical solvents for H2S absorption was suggested and experimentally validated. A data set composed of 99 H2S physical solvents was collected and predictive quantitative structure–property relationships for H2S solubility were built using a random forest algorithm and two types of molecular descriptors: ISIDA fragments and quantum-chemical descriptors. Virtual screening of industrially produced chemicals and their structural analogues enabled identification of the ones with predicted high solubility values. They can be suggested as starting points for further exploration of the H2S physical solvents chemical space. The predicted solubility value for one of the compounds found in virtual screening, 1,3-dimethyl-2-imidazolidinone, was confirmed experimentally.

Natural products (NPs), being evolutionary selected over millions of years to bind to biological macromolecules, remain an important source of inspiration for medicinal chemists even after the advent of efficient drug discovery technologies such as combinatorial chemistry and high-throughput screening. Thus, there is a strong demand for efficient and user-friendly computational tools that allow to analyze large libraries of NPs. In this context, we present NP Navigator – a free, intuitive online tool for visualization and navigation through the chemical space of NPs and NP-like molecules[1] (https://infochm.chimie.unistra.fr/npnav/chematlas_userspace ). It is based on the hierarchical ensemble of more than 200 Generative Topographic Maps(GTM)[2], featuring NPs from the COlleCtion of Open NatUral producTs (COCONUT), bioactive compounds from ChEMBL and commercially available molecules from ZINC. NP Navigator allows to efficiently analyze different aspects of NPs - chemotype distribution, physicochemical properties, reported and/or predicted biological activity and commercial availability of NPs. Users are welcome not only to browse through hundreds of thousands of compounds from ZINC, ChEMBL and COCONUT but also project a several external molecules that play the role of “chemical trackers” allowing to trace particular chemotypes in the NP chemical space and detect analogs of the compound of interest. [1] Y. Zabolotna, P. Ertl, D. Horvath, F. Bonachera, G. Marcou, A. Varnek, NP Navigator: a New Look at the Natural Product Chemical Space, 2021.[2] C. M. Bishop, M. Svensen, C. K. I. Williams, Neural Comput. 1998, 10, 215-234.

In this article, we consider cross-validation of the quantitative structure-property relationship models for reactions and show that the conventional k-fold cross-validation (CV) procedure gives an `optimistically' biased assessment of prediction performance. To address this issue, we suggest two strategies of model cross-validation, `transformation-out' CV, and `solvent-out' CV. Unlike the conventional k-fold cross-validation approach that does not consider the nature of objects, the proposed procedures provide an unbiased estimation of the predictive performance of the models for novel types of structural transformations in chemical reactions and reactions going under new conditions. Both the suggested strategies have been applied to predict the rate constants of bimolecular elimination and nucleophilic substitution reactions, and Diels-Alder cycloaddition. All suggested cross-validation methodologies and tutorial are implemented in the open-source software package CIMtools (https://github.com/cimm-kzn/CIMtools).

We report the first direct QSPR modeling of equilibrium constants of tautomeric transformations (logK T ) in different solvents and at different temperatures, which do not require intermediate assessment of acidity (basicity) constants for all tautomeric forms. The key step of the modeling consisted in the merging of two tautomers in one sole molecular graph (“condensed reaction graph”) which enables to compute molecular descriptors characterizing entire equilibrium. The support vector regression method was used to build the models. The training set consisted of 785 transformations belonging to 11 types of tautomeric reactions with equilibrium constants measured in different solvents and at different temperatures. The models obtained perform well both in cross-validation (Q2 = 0.81 RMSE = 0.7 logK T units) and on two external test sets. Benchmarking studies demonstrate that our models outperform results obtained with DFT B3LYP/6-311 ++ G(d,p) and ChemAxon Tautomerizer applicable only in water at room temperature.

Generative topographic mapping (GTM) has been used to visualize and analyze the chemical space of antimalarial compounds as well as to build predictive models linking structure of molecules with their antimalarial activity. For this, a database, including ~3000 molecules tested in one or several of 17 anti-Plasmodium activity assessment protocols, has been compiled by assembling experimental data from in-house and ChEMBL databases. GTM classification models built on subsets corresponding to individual bioassays perform similarly to the earlier reported SVM models. Zones preferentially populated by active and inactive molecules, respectively, clearly emerge in the class landscapes supported by the GTM model. Their analysis resulted in identification of privileged structural motifs of potential antimalarial compounds. Projection of marketed antimalarial drugs on this map allowed us to delineate several areas in the chemical space corresponding to different mechanisms of antimalarial activity. This helped us to make a suggestion about the mode of action of the molecules populating these zones.

In this work we report extensive DFT study of sydnone-alkyne cyclization which included investigation of the reaction mechanism, analysis of different factors affecting sydnone and alkyne reactivity as well as attempt to reproduce quantitatively experimental activation free energy. The calculations were performed for a set of 18 sydnone-alkyne reactions with a help of a semi-automatized workflow involving reagent preparation and generation of starting structures for a plausible transition state. Reconstructed reaction path supported two-step mechanism: cycloaddition followed by retro-Diels-Alder reaction. Since the latter had a tiny barrier, the cycloaddition step was predicted to be the rate-limiting. For the ensemble of reactions, calculations reproduce activation free energies extracted from experimental reaction rates (k) with the accuracy of 2 kcal/mol. Accounting for solvation effects didn't change the overall trend of activation free energies as a function of substituents. A series of statistical model linking logk and sydnones structure was built using Support Vector Regression and Multiple Linear Regression machine-learning methods coupled with different types of molecular descriptors; none of them demonstrated a good performance at cross-validation stage. Detailed analysis of different factors affecting reaction rate variation as a function of substituents revealed particular role of the charge on C3 atom in the sydnone moiety as well as of the size of the substituent at C3.

Here, we describe a concept of conjugated models for several properties (activities) linked by a strict mathematical relationship. This relationship can be directly integrated analytically into the ridge regression (RR) algorithm or accounted for in a special case of "twin" neural networks (NN). Developed approaches were applied to the modeling of the logarithm of the prototropic tautomeric constant (logK

International audience; We report the building, validation and release of QSPR (Quantitative Structure Property Relationship) models aiming to guide the design of new solvents for the next generation of Li-ion batteries. The dataset compiled from the literature included oxidation potentials (E ox), specific ionic conductivities (k), melting points (T m) and boiling points (T b) for 103 electrolytes. Each of the resulting consensus models assembled 9-19 individual Support Vector Machine models built on different sets of ISIDA fragment descriptors. [1] They were implemented in the ISIDA/Predictor software. Developed models were used to screen a virtual library of 9965 esters and sulfones. The most promising compounds prioritized according to theoretically estimated properties were synthesized and experimentally tested. Despite the tremendous success in the development of new materials for positive electrodes of Lithium-ion batteries, so far little attention was paid to the design of suitable electrolytes. In principle, new electrodes support high voltage, but their application is limited by the poor electrolyte stability to oxidation in high-voltage lithium-ion batteries. Besides, security issues have to be managed due to possible exothermic reactions between the positive electrode and electrolytes, especially at high-voltage. In particular, classical electrolytes such as ethylene carbonate (EC)-dimethyl carbonate (DMC) + lithium hexafluorophos-phate (LiPF 6) can hardly be used at voltage greater than 4.2 V. Higher voltage may lead to dramatic decrease of the cycle ability of the battery and may increase the risk of explosion, fire and release of toxic substances. [2] Thus, increase of flash point, thermal stability and anodic stability of the solvent while minimizing electrolyte viscosity and maximizing ionic conductivity are important goals of electrolyte optimization. In this work, a QSPR approach has been used to guide the design of a new generation of electrolytic solvents. Particular attention was paid to molecules belonging to two different chemotypes: sulfones and esters/ethers. Although, esters and ethers were largely investigated in the literature, their physicochemical and electrochemical properties still need to be optimized for their use in electrolyte for Lithium-ion batteries. Sulfones remain liquid within a large range of temperature, their thermal behavior is very interesting as they are generally non-flammable and have very high flash points (for instance, the flash point of dimethylsulfone is 145°C whereas dimethyl carbonate and a mixture of ethylene carbonate:dimethylcarbonate (1 : 1) exhibit flash points equal to 16 and 25°C, respectively [3]). In this work, 4 key properties [3-4] were considered: ionic conductivities (k) and oxidation potentials (E ox) of lithium-based electrolytes as well as melting points (T m) and boiling points (T b) of dipolar aprotic organic solvents usually considered for Lithium-ion batteries. The electrolyte should have advantageous transport properties in order to reduce the ohmic-drop caused by the internal resistance of the battery cell (high ionic conductivity). The electrolyte must be in liquid state for a large range of temperatures (ideally between À 40°C and 120°C, considering an operating range between À 30°C and 60°C). Simultaneously, the electro-chemical window should be as wide as possible. Currently, oxidation potential of electrolytes reaches about 4-4.2 V at active cathode materials. At high voltage a massive electro-lyte oxidation may occur, leading to a steep increase of the current density with rapid loss of battery cycling performance. Thus, it is highly desirable to design new solvents exhibiting high oxidation potential in the presence of lithium salts. At a first stage, a dataset of 103 dipolar aprotic organic solvents was collected from the literature. This included 155 oxidation potential values measured at 5 mV/s in sulfone or ester solvents in the presence of 1 M LiPF 6 or Lithium bis [a] G.

Here, we report the data visualization, analysis and modeling for a large set of 4830 S(N)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.

In this work, we report QSPR modeling of the free energy ΔG of 1 : 1 hydrogen bond complexes of different H-bond acceptors and donors. The modeling was performed on a large and structurally diverse set of 3373 complexes featuring a single hydrogen bond, for which ΔG was measured at 298 K in CCl4. The models were prepared using Support Vector Machine and Multiple Linear Regression, with ISIDA fragment descriptors. The marked atoms strategy was applied at fragmentation stage, in order to capture the location of H-bond donor and acceptor centers. Different strategies of model validation have been suggested, including the targeted omission of individual H-bond acceptors and donors from the training set, in order to check whether the predictive ability of the model is not limited to the interpolation of H-bond strength between two already encountered partners. Successfully cross-validating individual models were combined into a consensus model, and challenged to predict external test sets of 629 and 12 complexes, in which donor and acceptor formed single and cooperative H-bonds, respectively. In all cases, SVM models outperform MLR. The SVM consensus model performs well both in 3-fold cross-validation (RMSE=1.50 kJ/mol), and on the external test sets containing complexes with single (RMSE=3.20 kJ/mol) and cooperative H-bonds (RMSE=1.63 kJ/mol).

This paper reports SVR (Support Vector Regression) and GTM (Generative Topographic Mapping) modeling of three kinetic properties of cycloaddition reactions: rate constant (logk), activation energy (Ea) and pre-exponential factor (logA). A data set of 1849 reactions, comprising (4+2), (3+2) and (2+2) cycloadditions (CA) were studied in different solvents and at different temperatures. The reactions were encoded by the ISIDA fragment descriptors generated for Condensed Graph of Reaction (CGR). For a given reaction, a CGR condenses structures of all the reactants and products into one single molecular graph, described both by conventional chemical bonds and "dynamical" bonds characterizing chemical transformations. Different scenarios of logk assessment were exploited: direct modeling, application of the Arrhenius equation and temperature-scaled GTM landscapes. The logk models with optimal cross-validated statistics (Q2 =0.78-0.94 RMSE=0.45-0.86) have been challenged to predict rates for the external test set of 200 reactions, comprising both reactions that were not present in the training set, and training set transformations performed under different reaction conditions. The models are freely available on our web-server: http://cimm.kpfu.ru/models.

Over the past few decades, hit identification has been greatly facilitated by advances in high-throughput and fragment-based screenings. One major hurdle remaining in drug discovery is process automation of hit-to-lead (H2L) optimization. Here, we report a time- and cost-efficient integrated strategy for H2L optimization as well as a partially automated design of potent chemical probes consisting of a focused-chemical-library design and virtual screening coupled with robotic diversity-oriented de novo synthesis and automated in vitro evaluation. The virtual library is generated by combining an activated fragment, corresponding to the substructure binding to the target, with a collection of functionalized building blocks using in silico encoded chemical reactions carefully chosen from a list of one-step organic transformations relevant in medicinal chemistry. The proof of concept was demonstrated using the optimization of bromodomain inhibitors as a test case, leading to the validation of several compounds with improved affinity by several orders of magnitude.

Synthesis procedures for new coordination compounds of iron(II) with tris(pyrazol-1-yl)methane (HC(pz)3), containing cluster anions in the outer sphere, of the composition [Fe{HC(pz)3}2][Mo6Cl14]•2H2O (I), [Fe{HC(pz)3}2][Mo6Br14]•H2O (II), and [Fe{HC(pz)3}2]2[Re6S8(CN)6]•2H2O (III) are developed. The compounds are studied by static magnetic susceptibility, electronic, IR, and Mossbauer spectroscopic methods. The magnetochemical study shows that in the polycrystalline phases of all compounds the spincrossover 1 А 1 ⇔ 5 Т 2 is observed which is accompanied by thermochromism.

Each oil reservoir could be characterized by a set of parameters such as temperature, pressure, oil composition, and brine salinity, etc. In the context of the chemical enhanced oil recovery (EOR), the selection of high performance surfactants is a challenging and time-consuming task since this strongly depends on the reservoir's conditions. The situation becomes even more complicated if the surfactant formulation is a blend of two or more surfactants. In the present work, we report quantitative structure-property relationships (QSPR) correlating surfactants'structures and their composition in a mixture with optimal salinity (Sopt), corresponding to minimal interfacial tension in the reference brine/surfactants/n-dodecane system, at T = 313 K and P = 0.1 MPa. Particular attention was paid to selected families of surfactants: α-olefin sulfonate (AOS), internal olefin sulfonate (IOS), alkyl ether sulfate (AES), and alkyl glyceryl ether sulfonate (AGES). The models were built and validated on the database containing Sopt values for 75 surfactants' formulations. Molecular structures of amphiphilic molecules were encoded by functional group count descriptors (FGCD), ISIDA substructural molecular fragment (SMF) descriptors, and CODESSA molecular descriptors (CMD). For mixtures, descriptors were calculated as linear combinations of descriptors of individual compounds weighted by their mass fractions in mixtures. Different machine-learning methods-support vector machine (SVM), partial least-squares (PLS) regression, and random subspace (RS)-have been used for the modeling. Both global (on the entire database) and local (on individual families) models have been built. Models display reasonable accuracy (about 0.2 log Sopt units) which is comparable with the experimental error of measured Sopt. Our results show that the suggested approach can be successfully used to build predictive models for relatively small data sets of mixtures of chemical compounds. © 2015 American Chemical Society.

A direct one‐pot synthesis of asymmetric dehydrobenzopyrido[12]annulenes 2 and 3 containing one or two pyridine rings is reported that employs a Stephens–Castro mediated cross‐coupling of mixtures of ethynylcuprate precursors. The spectroscopic and theoretical properties of 2 and 3 are compared to those of the threefold symmetric dehydrotribenzo[12]annulene 1, and dehydrotripyrido[12]annulenes 4 and 5 and dehydrodibenzodipyrido[16]annulene byproduct 6, and showed 1–5 to be essentially isoelectronic band gap materials whose electron accepting ability increases with increasing nitrogen incorporation. The structures of 2, 4 and 6 were also unambiguously characterized by X‐ray crystallography. The results highlight the potential dehydroaryl[12]annulenes incorporating pyridines offer for the construction of high carbon content electronic materials. Acknowledgement: The authors would to thank European Journal of Organic Chemistry for the opportunity to publish this article.

By the structural representation of a chemical reaction in the form of a condensed graph a model allowing the prediction of rate constants (logk) of Diels–Alder reactions performed in different solvents and at different temperatures is constructed for the first time. The model demonstrates good agreement between the predicted and experimental logk values: the mean squared error is less than 0.75 log units. Erroneous predictions correspond to reactions in which reagents contain rarely occurring structural fragments. The model is available for users at https://cimm.kpfu.ru/predictor/.

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.

In the context of the investigation of drug-induced oxidative stress in parasitic cells, electrochemical properties of a focused library of polysubstituted menadione derivatives were studied by cyclic voltammetry. These values were used, together with compatible measurements from literature (quinones and related compounds), to build and evaluate a predictive structure-redox potential model (quantitative structure-property relationship, QSPR). Able to provide an online evaluation (through Web interface) of the oxidant character of quinones, the model is aimed to help chemists targeting their synthetic efforts towards analogues of desired redox properties.

This paper reports a predictive model for the rate constant of the bimolecular nucleophilic substitution involving the azide moiety. It predicts reaction rate constants in different solvents, including organic mixtures, and with different organic and inorganic azides as reactants. The optimal descriptors describing solvent effects and a cation type in the azide salt were suggested. A reasonably good predictive performance of the model in cross-validation has been demonstrated. The model was applied to predict the rates of the reactions between sodium azide with two conformers of calixarenes as well as 3-bromopropyl phenyl ester. For sterically non-hindered molecules, a good agreement between predicted and experimental reaction rates was observed. On the other hand, the model poorly reproduces the results for sterically hindered molecules.

We describe SILIRID (Simple Ligand–Receptor Interaction Descriptor), a novel fixed size descriptor characterizing protein–ligand interactions. SILIRID can be obtained from the binary interaction fingerprints (IFPs) by summing up the bits corresponding to identical amino acids. This results in a vector of 168 integer numbers corresponding to the product of the number of entries (20 amino acids and one cofactor) and 8 interaction types per amino acid (hydrophobic, aromatic face to face, aromatic edge to face, H-bond donated by the protein, H-bond donated by the ligand, ionic bond with protein cation and protein anion, and interaction with metal ion). Efficiency of SILIRID to distinguish different protein binding sites has been examined in similarity search in sc-PDB database, a druggable portion of the Protein Data Bank, using various protein–ligand complexes as queries. The performance of retrieval of structurally and evolutionary related classes of proteins was comparable to that of state-of-the-art approaches (ROC AUC≈0.91). SILIRID can efficiently be used to visualize chemogenomic space covered by sc-PDB using Generative Topographic Mapping (GTM): sc-PDB SILIRID data form clusters corresponding to different protein types.

High-throughput assays challenge us to extract knowledge from multi-ligand, multi-target activity data. In QSAR, weights are statically fitted to each ligand descriptor with respect to a single endpoint or target. However, computational chemogenomics (CG) has demonstrated benefits of learning from entire grids of data at once, rather than building target-specific QSARs. A possible reason for this is the emergence of inductive knowledge transfer (IT) between targets, providing statistical robustness to the model, with no assumption about the structure of the targets. Relevant protein descriptors in CG should allow one to learn how to dynamically adjust ligand attribute weights with respect to protein structure. Hence, models built through explicit learning (EL) by including protein information, while benefitting from IT enhancement, should provide additional predictive capability, notably for protein deorphanization. This interplay between IT and EL in CG modeling is not sufficiently studied. While IT is likely to occur irrespective of the injected target information, it is not clear whether and when boosting due to EL may occur. EL is only possible if protein description is appropriate to the target set under investigation. The key issue here is the search for evidence of genuine EL exceeding expectations based on pure IT. We explore the problem in the context of Support Vector Regression, using more than 9,400 pKi values of 31 GPCRs, where compound-protein interactions are represented by the concatenation of vectorial descriptions of compounds and proteins. This provides a unified framework to generate both IT-enhanced and potentially EL-enabled models, where the difference is toggled by supplied protein information. For EL-enabled models, protein information includes genuine protein descriptors such as typical sequence-based terms, but also the experimentally determined affinity cross-correlation fingerprints. These latter benchmark the expected behavior of a quasi-ideal descriptor capturing the actual functional protein-protein relatedness, and therefore thought to be the most likely to enable EL. EL- and IT-based methods were benchmarked alongside classical QSAR, with respect to cross-validation and deorphanization challenges. A rational method for projecting benchmarked methodologies into a strategy space is given, in the aims that the projection will provide directions for the types of molecule designs possible using a given methodology. While EL-enabled strategies outperform classical QSARs and favorably compare to similar published results, they are, in all respects evaluated herein, not strongly distinguished from IT-enhanced models. Moreover, EL-enabled strategies failed to prove superior in deorphanization challenges. Therefore, this paper raises caution that, contrary to common belief and intuitive expectation, the benefits of chemogenomics models over classical QSAR are quite possibly due less to the injection of protein-related information, and rather impacted more by the effect of inductive transfer, due to simultaneous learning from all of the modeled endpoints. These results show that the field of protein descriptor research needs further improvements to truly realize the expected benefit of EL.

Quantitative structure–property relationship (QSPR) modeling of stability constants for the metal:ligand ratio 1:1 (logK) and 1:2 (logβ 2) complexes of 3 transition metal ions with diverse organic ligands in aqueous solution was performed using ensemble multiple linear regression analysis and substructural molecular fragment descriptors. The modeling was performed on the sets containing 396 and 132 (Co2+), 613 and 233 (Ni2+), 883 and 257 (Cu2+) logK and logβ 2 values, respectively. The models have been validated in external fivefold cross-validations procedure as well as on the external test set containing new ligands recently reported in the literature. Predicted logK and logβ 2 values were calculated as arithmetic means of several hundred individual models (consensus models) using their applicability domains in averaging. The root mean squared error of predictions varies from 0.94 to 1.2 (logK) and from 1.2 to 1.4 (logβ 2) which is close to observed experimental systematic errors. Linear correlations between experimental logK values for pair of metal ions were evaluated. For all metal ions and ligands forming both 1:1 and 1:2 complexes the following ratio is observed: logβ 2/logK = 1.8 ± 0.1, n = 492.

Evaluation of important pharmacokinetic properties such as hydrophobicity by high-throughput screening (HTS) methods is a major issue in drug discovery. In this paper, we present measurements of the chromatographic hydrophobicity index (CHI) on a subset of the French chemical library Chimiotheque Nationale (CN). The data were used in quantitative structure-property relationship (QSPR) modeling in order to annotate the CN. An algorithm is proposed to detect problematic molecules with large prediction errors, called outliers. In order to find an explanation for these large discrepancies between predicted and experimental values, these compounds were reanalyzed experimentally. As the first selected outliers indeed had experimental problems, including hydrolysis or sheer absence of expected structure, we herewith propose the use of QSPR as a support tool for quality control of screening data and encourage cooperation between experimental and theoretical teams to improve results. The corrected data were used to produce a model, which is freely available on our web server at http://infochim.u-strasbg.fr/webserv/VSEngine.html .