The freedom space - a new set of commercially available molecules for hit discovery.

The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery.

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.

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.

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 (), a new open-source toolkit for de novo library design that allows merging those two chemical spaces into a single synthons space.

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.

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 S 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.

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.

Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents.

This article describes design, virtual screening, synthesis, and biological tests of novel αIIbβ3 antagonists, which inhibit platelet aggregation. Two types of αIIbβ3 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.

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.