Affinity chromatography in dynamic combinatorial libraries: one-pot amplification and isolation of a strongly binding receptor

We report the one-pot amplification and isolation of a nanomolar receptor in a multibuilding block aqueous dynamic combinatorial library using a polymer-bound template. By appropriate choice of a poly(N,N-dimethylacrylamide)-based support, unselective ion-exchange type behaviour between the oppositely charged cationic guest and polyanionic hosts was overcome, such that the selective molecular recognition arising in aqueous solution reactions is manifest also in the analogous templated solid phase DCL syntheses. The ability of a polymer bound template to identify and isolate a synthetic receptor via dynamic combinatorial chemistry was not compromised by the large size of the library, consisting of well over 140 theoretical members, demonstrating the practical advantages of a polymer-supported DCL methodology

An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents

Prediction of solubility of active pharmaceutical ingredients (API) in different solvents is one of the main issue for crystallization process design. Experimental determination is not always possible because of the small amount of product available in the early stages of a drug development. Thus, one interesting perspective is the use of thermodynamic models, which are usually employed for predicting the activity coefficients in case of Vapour–Liquid equilibria or Liquid–Liquid equilibria (VLE or LLE). The choice of the best thermodynamic model for Solid–Liquid equilibria (SLE) is not an easy task as most of them are not meant particularly for this. In this paper, several models are tested for the solubility prediction of five drugs or drug-like molecules: Ibuprofen, Acetaminophen, Benzoic acid, Salicylic acid and 4-aminobenzoic acid, and another molecule, anthracene, a rather simple molecule. The performance of predictive (UNIFAC, UNIFAC mod., COSMO-SAC) and semi-predictive (NRTL-SAC) models are compared and discussed according to the functional groups of the molecules and the selected solvents. Moreover, the model errors caused by solid state property uncertainties are taken into account. These errors are indeed not negligible when accurate quantitative predictions want to be performed. It was found that UNIFAC models give the best results and could be an useful method for rapid solubility estimations of an API in various solvents. This model achieves the order of magnitude of the experimental solubility and can predict in which solvents the drug will be very soluble, soluble or not soluble. In addition, predictions obtained with NRTL-SAC model are also in good agreement with the experiments, but in that case the relevance of the results is strongly dependent on the model parameters regressed from solubility data in single and mixed solvents. However, this is a very interesting model for quick estimations like UNIFAC models. Finally, COSMO-SAC needs more developments to increase its accuracy especially when hydrogen bonding is involved. In that case, the predicted solubility is always overestimated from two to three orders of magnitude. Considering the use of the most accurate equilibrium equation involving the ΔCp term, no benefits were found for drug predictions as the models are still too inaccurate. However, in function of the molecules and their solid thermodynamic properties, the ΔCp term can be neglected and will not have a great impact on the results

Generic Strategies for Chemical Space Exploration

Computational approaches to exploring "chemical universes", i.e., very large sets, potentially infinite sets of compounds that can be constructed by a prescribed collection of reaction mechanisms, in practice suffer from a combinatorial explosion. It quickly becomes impossible to test, for all pairs of compounds in a rapidly growing network, whether they can react with each other. More sophisticated and efficient strategies are therefore required to construct very large chemical reaction networks. Undirected labeled graphs and graph rewriting are natural models of chemical compounds and chemical reactions. Borrowing the idea of partial evaluation from functional programming, we introduce partial applications of rewrite rules. Binding substrate to rules increases the number of rules but drastically prunes the substrate sets to which it might match, resulting in dramatically reduced resource requirements. At the same time, exploration strategies can be guided, e.g. based on restrictions on the product molecules to avoid the explicit enumeration of very unlikely compounds. To this end we introduce here a generic framework for the specification of exploration strategies in graph-rewriting systems. Using key examples of complex chemical networks from sugar chemistry and the realm of metabolic networks we demonstrate the feasibility of a high-level strategy framework. The ideas presented here can not only be used for a strategy-based chemical space exploration that has close correspondence of experimental results, but are much more general. In particular, the framework can be used to emulate higher-level transformation models such as illustrated in a small puzzle game

Discovery of a Potent α ‑ Galactosidase Inhibitor by in Situ Analysis of a Library of Pyrrolizidine − (Thio)urea Hybrid Molecules Generated via Click Chemistry

The parallel synthesis of a 26-membered-library of aromatic/aliphatic-(thio)urea-linked pyrrolizidines followed by in situ biological evaluation toward α -galactosidases has been carried out. The combination of the (thio)urea-forming click reaction and the in situ screening is pioneer in the search for glycosidase inhibitors and has allowed the discovery of a potent co ff ee bean α -galactosidase inhibitor (IC 50 = 0.37 μ M, K i = 0.12 μ M) that has also showed inhibition against human lysosomal α -galactosidase ( α -Gal A, IC 50 = 5.3 μ M, K i = 4.2 μ M).Ministerio de Economía y Competitividad (CTQ2016-77270-R)Junta de Andalucía (FQM-345