Chemspace Atlas: Multiscale Chemography of Ultralarge Libraries for Drug Discovery.

Nowadays, drug discovery is inevitably intertwined with the usage of large compound collections. Understanding of their chemotype composition and physicochemical property profiles is of the highest importance for successful hit identification. Efficient polyfunctional tools allowing multifaceted analysis of constantly growing chemical libraries must be Big Data-compatible. Here, we present the freely accessible ChemSpace Atlas (https://chematlas.chimie.unistra.fr), which includes almost 40K hierarchically organized Generative Topographic Maps (GTM) accommodating up to 500 M compounds covering fragment-like, lead-like, drug-like, PPI-like, and NP-like chemical subspaces. They allow users to navigate and analyze ZINC, ChEMBL, and COCONUT from multiple perspectives on different scales: from a bird's eye view of the entire library to structural pattern detection in small clusters. Around 20 physicochemical properties and almost 750 biological activities can be visualized (associated with map zones), supporting activity profiling and analogue search. Moreover, ChemScape Atlas will be extended toward new chemical subspaces (e.g., DNA-encoded libraries and synthons) and functionalities (ADMETox profiling and property-guided de novo compound generation).

In Vitro Evaluation of In Silico Screening Approaches in Search for Selective ACE2 Binding Chemical Probes.

New models for ACE2 receptor binding, based on QSAR and docking algorithms were developed, using XRD structural data and ChEMBL 26 database hits as training sets. The selectivity of the potential ACE2-binding ligands towards Neprilysin (NEP) and ACE was evaluated. The Enamine screening collection (3.2 million compounds) was virtually screened according to the above models, in order to find possible ACE2-chemical probes, useful for the study of SARS-CoV2-induced neurological disorders. An enzymology inhibition assay for ACE2 was optimized, and the combined diversified set of predicted selective ACE2-binding molecules from QSAR modeling, docking, and ultrafast docking was screened in vitro. The in vitro hits included two novel chemotypes suitable for further optimization.

SynthI: A New Open-Source Tool for Synthon-Based Library Design.

Most of the existing computational tools for de novo library design are focused on the generation, rational selection, and combination of promising structural motifs to form members of the new library. However, the absence of a direct link between the chemical space of the retrosynthetically generated fragments and the pool of available reagents makes such approaches appear as rather theoretical and reality-disconnected. In this context, here we present Synthons Interpreter (SynthI), a new open-source toolkit for de novo library design that allows merging those two chemical spaces into a single synthons space. Here synthons are defined as actual fragments with valid valences and special labels, specifying the position and the nature of reactive centers. They can be issued from either the "breakup" of reference compounds according to 38 retrosynthetic rules or real reagents, after leaving group withdrawal or transformation. Such an approach not only enables the design of synthetically accessible libraries and analog generation but also facilitates reagents (building blocks) analysis in the medicinal chemistry context. SynthI code is publicly available at https://github.com/Laboratoire-de-Chemoinformatique/SynthI.

Discovery of novel chemical reactions by deep generative recurrent neural network.

The "creativity" of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding. Here we show that "creative" AI may be as successfully taught to enumerate novel chemical reactions that are stoichiometrically coherent. Furthermore, when coupled to reaction space cartography, de novo reaction design may be focused on the desired reaction class. A sequence-to-sequence autoencoder with bidirectional Long Short-Term Memory layers was trained on on-purpose developed "SMILES/CGR" strings, encoding reactions of the USPTO database. The autoencoder latent space was visualized on a generative topographic map. Novel latent space points were sampled around a map area populated by Suzuki reactions and decoded to corresponding reactions. These can be critically analyzed by the expert, cleaned of irrelevant functional groups and eventually experimentally attempted, herewith enlarging the synthetic purpose of popular synthetic pathways.

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.

Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis.

We report a new method to assess protective groups (PGs) reactivity as a function of reaction conditions (catalyst, solvent) using raw reaction data. It is based on an intuitive similarity principle for chemical reactions: similar reactions proceed under similar conditions. Technically, reaction similarity can be assessed using the Condensed Graph of Reaction (CGR) approach representing an ensemble of reactants and products as a single molecular graph, i.e., as a pseudomolecule for which molecular descriptors or fingerprints can be calculated. CGR-based in-house tools were used to process data for 142,111 catalytic hydrogenation reactions extracted from the Reaxys database. Our results reveal some contradictions with famous Greene's Reactivity Charts based on manual expert analysis. Models developed in this study show high accuracy (ca. 90%) for predicting optimal experimental conditions of protective group deprotection.

Design, Virtual Screening, and Synthesis of Antagonists of αIIbß3 as Antiplatelet Agents.

This article describes design, virtual screening, synthesis, and biological tests of novel αIIbß3 antagonists, which inhibit platelet aggregation. Two types of αIIbß3 antagonists were developed: those binding either closed or open form of the protein. At the first step, available experimental data were used to build QSAR models and ligand- and structure-based pharmacophore models and to select the most appropriate tool for ligand-to-protein docking. Virtual screening of publicly available databases (BioinfoDB, ZINC, Enamine data sets) with developed models resulted in no hits. Therefore, small focused libraries for two types of ligands were prepared on the basis of pharmacophore models. Their screening resulted in four potential ligands for open form of αIIbß3 and four ligands for its closed form followed by their synthesis and in vitro tests. Experimental measurements of affinity for αIIbß3 and ability to inhibit ADP-induced platelet aggregation (IC50) showed that two designed ligands for the open form 4c and 4d (IC50 = 6.2 nM and 25 nM, respectively) and one for the closed form 12b (IC50 = 11 nM) were more potent than commercial antithrombotic Tirofiban (IC50 = 32 nM).

Evidence for the formation of UO2(NO3)4(2-) in an ionic liquid by EXAFS.

The complexation between uranium(vi) and nitrate ions in a hydrophobic ionic liquid (IL), namely [BMI][NO(3)] (BMI = 1-butyl-3-methylimidazolium(+)), is investigated by EXAFS spectroscopy. It was performed by dissolution of uranyl nitrate UO(2)(NO(3))(2)·6H(2)O or UO(2)(Tf(2)N)(2) (Tf(2)N = bis(trifluoromethylsulfonyl)imide (CF(3)SO(2))(2)N(-)). The formation of the complex UO(2)(NO(3))(4)(2-) is evidenced.

Perrhenate complexation by uranyl in traditional solvents and in ionic liquids: a joint molecular dynamics/spectroscopic study.

The complexation of perrhenate (ReO(4)(-)) anions by the uranyl (UO(2)(2+)) cation has been investigated by joint molecular dynamics simulations and spectroscopic (UV-vis, TRLFS, and EXAFS) studies in aqueous solution, acetonitrile, and three ionic liquids (ILs), namely, [Bmi][Tf(2)N], [Me(3)BuN][Tf(2)N], and [Bu(3)MeN][Tf(2)N] that are based on the same Tf(2)N(-) anion (bis(trifluoromethylsulfonyl)imide) and either Bmi(+) (1-butyl,3-methylimidazolium), Me(3)BuN(+), or Bu(3)MeN(+) cations. They show that ReO(4)(-) behaves as a weak ligand in aqueous solution and as a strong ligand in acetonitrile and in the ILs. According to MD simulations in aqueous solution, the UO(2)(ReO(4))(2) complex quickly dissociates to form UO(2)(H(2)O)(5)(2+), while in acetonitrile, a stable UO(2)(ReO(4))(5)(3-) species forms from dissociated ions. In the ILs, the UO(2)(ReO(4))(n)(2-n) complexes (n = 1 to 5) remained stable along the dynamics, and to assess their relative stabilities, we computed the free energy profiles for stepwise ReO(4)(-) complexation to uranyl. In the two studied ILs, complexation is favored, leading to the UO(2)(ReO(4))(5)(3-) species in [Bmi][Tf(2)N] and to UO(2)(ReO(4))(4)(2-) in [Bu(3)MeN][Tf(2)N]. Furthermore, in both acetonitrile and [Bmi][Tf(2)N] solutions, MD and PMF simulations support the formation of dimeric uranyl complexes [UO(2)(ReO(4))(4)](2)(4-) with two bridging ReO(4)(-) ligands. The simulation results are qualitatively consistent with spectroscopic observations in the different solvents, without firmly concluding, however, on the precise composition and structure of the complexes in the solutions.