Arthur F. Duprat

Calixarene‐Based Copper(I) Complexes as Models for Monocopper Sites in Enzymes

A cavity that acts as a molecular funnel is formed from calix[6]arene 1 and [CuI (NCCH3 )4 ]PF6 [Eq. (a)]. An exchange of the well-protected acetonitrile ligand for other nitriles RCN is only possible with small R groups. The protection of the copper ions precludes oxidative dimerization; thus, the complexes mimic the mononuclear site of copper enzymes.

Biomimetic Copper(I)–CO Complexes: A Structural and Dynamic Study of a Calix[6]arene-Based Supramolecular System

Four novel calix[6]arene-based cuprous complexes are described. They present a biomimetic tris(imidazole) coordination core associated with a hydrophobic cavity that wraps the apical binding site. Each differs from the other by the methyl or ethyl substituents present on the phenoxyl groups (OR1) and on the imidazole arms (NR2) of the calix[6]arene structure. In solution, stable CO complexes were obtained. We have investigated their geometrical and dynamic properties with respect to the steric demand. IR and NMR studies revealed that, in solution, these complexes adopted two distinct conformations. The preferred conformation was dictated only by the size of the OR1 group. When R1 was an ethyl group, the complex preferentially adopted a flattened C3-symmetrical structure. The corresponding helical enantiomers were in conformational equilibrium, which, however, was slow on the 1H NMR time scale at -80 degrees C. When R1 was a methyl group, the low-temperature NMR spectra revealed the partial inclusion of one tBu group. The complex wobbled between three dissymmetric but equivalent conformations. Hence, small differences in the steric demand of the calixarenes skeleton changed the geometry and dynamics of the system. Indeed, this supramolecular control was promoted by the strong conformational coupling between the metal center and the host structure. Interestingly, this was not only the result of a covalent preorganization, but also stemmed from weak interactions within the hydrophobic pocket. The vibrational spectra of the bound CO were revealed to be a sensitive gauge of this supramolecular behavior, similar to copper proteins in which allosteric effects are common.

Toward a principled methodology for neural network design and performance evaluation in QSAR. Application to the prediction of logP.

The prediction of properties of molecules from their structure (QSAR) is basically a nonlinear regression problem. Neural networks are proven to be parsimonious universal approximators of nonlinear functions; therefore, they are excellent candidates for performing the nonlinear regression tasks involved in QSAR. However, their full potential can be exploited only in the framework of a rigorous approach. In the present paper, we describe a principled methodology for designing neural networks for QSAR and estimating their performances, and we apply this approach to the prediction of logP. We compare our results to those obtained on the same molecules by other methods.

Predicting activities without computing descriptors: graph machines for QSAR.

We describe graph machines, an alternative approach to traditional machine-learning-based QSAR, which circumvents the problem of designing, computing and selecting molecular descriptors. In that approach, which is similar in spirit to recursive networks, molecules are considered as structured data, represented as graphs. For each example of the data set, a mathematical function (graph machine) is built, whose structure reflects the structure of the molecule under consideration; it is the combination of identical parameterised functions, called “node functions” (e.g. a feedforward neural network). The parameters of the node functions, shared both within and across the graph machines, are adjusted during training with the “shared weights” technique. Model selection is then performed by traditional cross-validation. Therefore, the designers main task consists in finding the optimal complexity for the node function. The efficiency of this new approach has been demonstrated in many QSAR or QSPR tasks, as well as in modelling the activities of complex chemicals (e.g. the toxicity of a family of phenols or the anti-HIV activities of HEPT derivatives). It generally outperforms traditional techniques without requiring the selection and computation of descriptors. §Presented at the 12th International Workshop on Quantitative Structure-Activity Relationships in Environmental Toxicology (QSAR2006), 8–12 May 2006, Lyon, France.

A novel calix[6]arene-based mononuclear copper(I) complex that exhibits chirality at low temperature

A calix[6]arene functionalized in alternate positions at the lower rim with three picolyl groups acts as a biomimetic N3 ligand with cuprous ion. The stable C3 symmetrical complex derived from CuCl exists as a pair of helical enantiomers. A low temperature 1H NMR study revealed that the chirality is transmitted to the calixarene skeleton, thereby providing a chiral cavity around the apical binding site of the metal center.

QSPR Prediction of the Stability Constants of Gadolinium(III) Complexes for Magnetic Resonance Imaging

Gadolinium(III) complexes constitute the largest class of compounds used as contrast agents for Magnetic Resonance Imaging (MRI). A quantitative structure-property relationship (QSPR) machine-learning based method is applied to predict the thermodynamic stability constants of these complexes (log KGdL), a property commonly associated with the toxicity of such organometallic pharmaceuticals. In this approach, the log KGdL value of each complex is predicted by a graph machine, a combination of parametrized functions that encodes the 2D structure of the ligand. The efficiency of the predictive model is estimated on an independent test set; in addition, the method is shown to be effective (i) for estimating the stability constants of uncharacterized, newly synthesized polyamino-polycarboxylic compounds and (ii) for providing independent log KGdL estimations for complexants for which conflicting or questionable experimental data were reported. The exhaustive database of log KGdL values for 158 complexants, reported for potential application as contrast agents for MRI and used in the present study, is available in the Supporting Information (122 primary literature sources).

Graph machines and their applications to computer-aided drug design: a new approach to learning from structured data

The recent developments of statistical learning focused on vector machines, which learn from examples that are described by vectors of features. However, there are many fields where structured data must be handled; therefore, it would be desirable to learn from examples described by graphs.Graph machines learn real numbers from graphs. Basically, for each graph, a separate learning machine is built, whose algebraic structure contains the same information as the graph. We describe the training of such machines, and show that virtual leave-one-out, a powerful method for assessing the generalization capabilities of conventional vector machines, can be extended to graph machines. Academic examples are described, together with applications to the prediction of pharmaceutical activities of molecules and to the classification of properties; the potential of graph machines for computer-aided drug design are highlighted.

Calix[6]arene-based models for mono-copper enzymes: a promising supramolecular system for oxidation catalysis

Abstract A novel supramolecular system that mimics the mono-copper site of enzymes is described. It is based on a calix[6]arene presenting either three pyridine (Py) or three imidazole (Im) groups that coordinate the copper center. Whereas Cu(I) is tetrahedral, Cu(II) is 5-coordinate. In both cases however, the Cu complexes possess a labile site located inside the calixarene cavity. A comparative study of the Py- and Im-based systems is presented in terms of chemical behaviour, electrochemistry and reactivity in the presence of hydrogen peroxide. Hence, the Py-based Cu(II) complex appears as the most interesting catalyst for the oxidation of aromatics and alcohols

Aromatic hydroxylation by a new iron(0)–acetic acid–dioxygen system

A new and simple system performs the hydroxylation of a number of aromatic compounds with moderate yields; a mechanism involving transitory formation of electrophilic FeIV oxo (ferryl) species is proposed and discussed.

Toward big data in QSAR/QSPR

We investigate a prospective path to processing “big data” in the field of computer-aided drug design, motivated by the expected increase of the size of available databases. We argue that graph machines, which exempt the designer of a predictive model from handcrafting, selecting and computing ad hoc molecular descriptors, may open a way toward efficient model design procedures. We recall the principle of graph machines, which perform predictions directly from the molecular structure described as a graph, without resorting to descriptors. We discuss scalability issues in the present implementation of graph machines, and we describe an application to the prediction of an important thermodynamic property of contrast agents for MRI imaging.