João Aires-de-Sousa

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks

Regression trees were built with an initial pool of 1085 molecular descriptors calculated by DRAGON software for 126 pyridinium bromides, to predict the melting point. A single tree was derived with 9 nodes distributed over 5 levels in less than 2 min showing very good correlation between the estimated and experimental values (R2 = 0.933, RMS = 12.61 °C). A number n of new trees were grown sequentially, without the descriptors selected by previous trees, and combination of predictions from the n trees (ensemble of trees) resulted in higher accuracy. A 3-fold cross-validation with the optimum number of trees (n = 4) yielded an R2 value of 0.822. A counterpropagation neural network was trained with the variables selected by the first tree, and reasonable results were achieved (R2 = 0.748). In a test set of 9 new pyridinium bromides, all the low melting point cases were successfully identified.

A NEW ENANTIOSELECTIVE SYNTHESIS OF N-ARYLAZIRIDINES BY PHASE-TRANSFER CATALYSIS

Abstract Chiral N-arylaziridines are obtained from N-acyl-N-arylhydroxylamines using quaternary salts of Cinchona alkaloids as phase-transfer catalysts.

Random forest prediction of mutagenicity from empirical physicochemical descriptors.

Fast-to-calculate empirical physicochemical descriptors were investigated for their ability to predict mutagenicity (positive or negative Ames test) from the molecular structure. Fast methods are highly desired for the screening of large libraries of compounds. Global molecular descriptors and MOLMAP descriptors of bond properties were used to train random forests. Error percentages as low as 15% and 16% were achieved for an external test set with 472 compounds and for the training set with 4083 structures, respectively. High sensitivity and specificity were observed. Random forests were able to associate meaningful probabilities to the predictions and to explain the predictions in terms of similarities between query structures and compounds in the training set.

New description of molecular chirality and its application to the prediction of the preferred enantiomer in stereoselective reactions.

A new representation of molecular chirality as a fixed-length code is introduced. This code describes chiral carbon atoms using atomic properties and geometrical features independent of conformation and is able to distinguish between enantiomers. It was used as input to counterpropagation (CPG) neural networks in two different applications. In the case of a catalytic enantioselective reaction the CPG network established a correlation between the chirality codes of the catalysts and the major enantiomer obtained by the reaction. In the second application-enantioselective reduction of ketones by DIP-chloride-the series of major and minor enantiomers produced from different substrates were clustered by the CPG neural network into separate regions, one characteristic of the minor products and the other characteristic of the major products.

Comparing the chemical spaces of metabolites and available chemicals: models of metabolite-likeness

The chemical space covered by compounds involved in metabolic reactions was compared with that of a random dataset of purchasable compounds by chemoinformatics techniques. The comparison was based on 3D structure, 2D structure, or descriptors of global properties, by means of self-organizing maps, random forests, and classification trees. The overlap between metabolites and non-metabolites was observed to be the least in the space defined by the global descriptors, the most discriminatory features being the number of OH groups, presence of aromatic systems, and molecular weight. Discrimination between the two datasets was achieved with accuracy up to 97%. Models were built to produce a metabolite-likeness parameter. A relationship between metabolite-likeness and ready biodegradability was observed.

Prediction of enantiomeric selectivity in chromatography. Application of conformation-dependent and conformation-independent descriptors of molecular chirality.

In order to process molecular chirality by computational methods and to obtain predictions for properties that are influenced by chirality, a fixed-length conformation-dependent chirality code is introduced. The code consists of a set of molecular descriptors representing the chirality of a 3D molecular structure. It includes information about molecular geometry and atomic properties, and can distinguish between enantiomers, even if chirality does not result from chiral centers. The new molecular transform was applied to two datasets of chiral compounds, each of them containing pairs of enantiomers that had been separated by chiral chromatography. The elution order within each pair of isomers was predicted by means of Kohonen neural networks (NN) using the chirality codes as input. A previously described conformation-independent chirality code was also applied and the results were compared. In both applications clustering of the two classes of enantiomers (first eluted and last eluted enantiomers) could be successfully achieved by NN and accurate predictions could be obtained for independent test sets. The chirality code described here has a potential for a broad range of applications from stereoselective reactions to analytical chemistry and to the study of biological activity of chiral compounds.

Structure-Based Classification of Chemical Reactions without Assignment of Reaction Centers

The automatic classification of chemical reactions is of high importance for the analysis of reaction databases, reaction retrieval, reaction prediction, or synthesis planning. In this work, the classification of photochemical reactions was investigated with no explicit assignment of the reacting centers. Classifications were explored with Random Forests or Kohonen neural networks in three different situations, using different levels of information: (a) pairs of reactants were classified according to the type of reaction they produce, (b) products were classified according to the type of reaction from which they can be synthesized, and (c) reactions were classified from the difference between the descriptors of the product and the descriptors of the reactants. In all cases molecular maps of atom-level properties (MOLMAPs) were used as descriptors. They are generated by a self-organizing map and encode physicochemical properties of the bonds available in a molecule. Correct classification could be achieved for approximately 90% of the 78 reactions in an independent test set.

Asymmetric synthesis of N-aryl aziridines

Abstract The reactions of a variety of N-arylhydroxamates as nitrogen transfer reagents to acryloyl derivatives of (−)-8-phenylmenthol, (−)-quinine and (−)-Oppolzers sultam acting as Michael acceptors was studied. Poor to modest diastereoselection was observed in the formation of aziridines. The absolute structure of one of the pure diastereomers secured from Oppolzers auxiliary was established by X-ray crystallography and hence the absolute configuration of the derived methyl-N-phenylaziridine-2-carboxylate could be assigned. Whilst only poor facial selectivity was observed for chiral hydroxamic acid prepared from dehydroabietic acid, moderate to good enantioselection of aziridines could be achieved with the chiral quaternary salts based on cinchona alkaloids, especially with that of cinchonine. A model is presented to explain the origin of enantioselection and a mechanism is proposed for the aziridination reaction.

Structure-based predictions of 1H NMR chemical shifts using feed-forward neural networks

Feed-forward neural networks were trained for the general prediction of 1H NMR chemical shifts of CH(n) protons in organic compounds in CDCl3. The training set consisted of 744 1H NMR chemical shifts from 120 molecular structures. The method was optimized in terms of selected proton descriptors (selection of variables), the number of hidden neurons, and integration of different networks in ensembles. Predictions were obtained for an independent test set of 952 cases with a mean average error of 0.29 ppm (0.20 ppm for 90% of the cases). The results were significantly better than those obtained with counterpropagation neural networks.