Wilfried Langenaeker

Computational Medicinal Chemistry for Drug Discovery

Observing computational chemistrys proven value to the introduction of new medicines, this reference offers the techniques most frequently utilized by industry and academia for ligand design. Featuring contributions from more than fifty pre-eminent scientists, Computational Medicinal Chemistry for Drug Discovery surveys molecular structure computation, intermolecular behavior, ligand-receptor interaction, and modeling responding to market demands in its selection and authoritative treatment of topics. The book examines molecular mechanics, semi-empirical methods, wave function-based quantum chemistry, density functional theory, 3-D structure generation, and hybrid methods.

Atomic charges, dipole moments, and Fukui functions using the Hirshfeld partitioning of the electron density

In the Hirshfeld partitioning of the electron density, the molecular electron density is decomposed in atomic contributions, proportional to the weight of the isolated atom density in the promolecule density, constructed by superimposing the isolated atom electron densities placed on the positions the atoms have in the molecule. A maximal conservation of the information of the isolated atoms in the atoms‐in‐molecules is thereby secured. Atomic charges, atomic dipole moments, and Fukui functions resulting from the Hirshfeld partitioning of the electron density are computed for a large series of molecules. In a representative set of organic and hypervalent molecules, they are compared with other commonly used population analysis methods. The expected bond polarities are recovered, but the charges are much smaller compared to other methods. Condensed Fukui functions for a large number of molecules, undergoing an electrophilic or a nucleophilic attack, are computed and compared with the HOMO and LUMO densities, integrated over the Hirshfeld atoms in molecules.

Negative Fukui functions: new insights based on electronegativity equalization

Fukui functions have been calculated for large numbers of organic molecules, and were found to always be positive. Numeric and algebraic considerations allowed the identification of several boundary conditions for negative values for Fukui functions. Negative Fukui functions are found to be very unlikely, except when very short interatomic distances are present. Recent hypotheses concerning the occurrence of negative Fukui functions are strongly supported by the present approach.

Fast calculation of quantum chemical molecular descriptors from the Electronegativity Equalization Method

The use of the Electronegativity Equalization Method (EEM) is presented for high performance calculation of molecular electrostatic descriptors, giving quite similar results to those obtained through Density Functional Theory (B3LYP/6-31G*) calculations. Molecular descriptors include atomic charges and different related descriptors as well as Fukui functions, hardness and softness.

Determination of absolute configuration via vibrational circular dichroism

Vibrational circular dichroism (VCD) provides a growing and promising technology for the determination of the absolute configuration of molecules in solution, including drug molecules. The practical application of VCD spectroscopy consists of the experimental determination and comparison to quantum chemically calculated data. The key features of the VCD technology are presented and an example of an application of the technique is discussed.:

Prediction of blood-brain partitioning: A model based on ab initio calculated quantum chemical descriptors

A new model for the prediction of log BB, a penetration measure through the blood-brain barrier, based on a molecular set of 82 diverse molecules is developed. The majority of the descriptors are derived from quantum chemical ab initio calculations, augmented with a number of classical descriptors. The quantum chemical information enables one to compute fundamental properties of the molecules. The best set of descriptors was selected by sequential selection and multiple linear regression was used to develop the QSAR model. The predictive capability of the model was tested using internal and external test procedures and the domain of applicability was determined to identify reliable predictions. The selected set of descriptors shows a significant correlation with the experimental log BB. The proposed model could reproduce the data with an error approaching the experimental uncertainty and satisfies the available validation procedures. The obtained results indicate that the use of quantum chemical information in describing molecules improves the behavior of the model.

Application of spectrophores™ to map vendor chemical space using self-organising maps

A Spectrophore™ is a one-dimensional descriptor that describes the three-dimensional molecular field surrounding a molecule generated by a given set of atomic properties. In a typical application, Spectrophores™ are calculated from the molecular shape in combination with the electrostatic, lipophilic, softness, and hardness potential surrounding the molecules. Given that molecules with similar three-dimensional properties have similar Spectrophores™ and the real-valued nature of Spectrophores™, they are very well suited to deploy in various virtual screening models. Spectrophores™ have been recently released in the open source domain and have been added as a descriptor in the Open Babel C++ API. To illustrate the capabilities of Spectrophores™ for virtual screening and clustering, a large self-organising map (SOM) was trained from 8.6 million commercially available compounds as a representation of the vendor chemical space. To train a SOM from such a large collection of compounds, a specially tailored algorithm was developed. The resulting SOM clearly shows that the vendor chemical space can be mapped in a meaningful organization based on its Spectrophores™ representation and that it handles compounds beyond their topological similarity. The SOM has been applied in three different problems, including the overlay of vendor databases, the comparison of compounds with different pharmacological profiles, and to sample drug-like screening libraries.

Spectrophores as one-dimensional descriptors calculated from three-dimensional atomic properties: applications ranging from scaffold hopping to multi-target virtual screening

Spectrophores are novel descriptors that are calculated from the three-dimensional atomic properties of molecules. In our current implementation, the atomic properties that were used to calculate spectrophores include atomic partial charges, atomic lipophilicity indices, atomic shape deviations and atomic softness properties. This approach can easily be widened to also include additional atomic properties. Our novel methodology finds its roots in the experimental affinity fingerprinting technology developed in the 1990’s by Terrapin Technologies. Here we have translated it into a purely virtual approach using artificial affinity cages and a simplified metric to calculate the interaction between these cages and the atomic properties. A typical spectrophore consists of a vector of 48 real numbers. This makes it highly suitable for the calculation of a wide range of similarity measures for use in virtual screening and for the investigation of quantitative structure–activity relationships in combination with advanced statistical approaches such as self-organizing maps, support vector machines and neural networks. In our present report we demonstrate the applicability of our novel methodology for scaffold hopping as well as virtual screening.