Johann Gasteiger

Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges

Abstract A method is presented for the rapid calculation of atomic charges in σ-bonded and nonconjugated π-systems. Atoms are characterized by their orbital electronegativities. In the calculation only the connectivities of the atoms are considered. Thus only the topology of a molecule is of importance. Through an iterative procedure partial equalization of orbital electronegativity is obtained. Excellent correlations of the atomic charges with core electron binding energies and with acidity constants are observed. This establishes their value in predicting experimental data.

Virtual computational chemistry laboratory - design and description

Internet technology offers an excellent opportunity for the development of tools by the cooperative effort of various groups and institutions. We have developed a multi-platform software system, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis. The implemented software is based on a three-tier architecture that is one of the standard technologies to provide client-server services on the Internet. The developed software includes several popular programs, including the indices generation program, DRAGON, a 3D structure generator, CORINA, a program to predict lipophilicity and aqueous solubility of chemicals, ALOGPS and others. All these programs are running at the host institutes located in five countries over Europe. In this article we review the main features and statistics of the developed system that can be used as a prototype for academic and industry models.

Neural networks : a new method for solving chemical problems or just a passing phase ?

Abstract Recent work on neural networks in chemistry is reviewed and essential background to this fast-spreading method is given. Emphasis is placed on the back-propagation algorithm, because of the extensive use of this form of learning. Hopfield networks, adaptive bidirectional associative memory, and Kohonen learning are briefly described and discussed. Applications in spectroscopy (mass, infrared, ultraviolet, NMR), potentiometry, structure/activity relationships, protein structure, process control and chemical reactivity are summarized.

Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures

Several criteria were defined to select a dataset of high-quality X-ray structures from the Cambridge file resulting in 639 molecules. Six currently available programs for automatic 3D structure generation were compared by converting the connectivity tables including appropriate stereodescriptors from this dataset of 639 molecular structures into 3D geometries: CONCORD, ALCOGEN, Chem-X, MOLGEO, COBRA, and CORINA. The geometries produced by the different programs were evaluated in terms of several quality criteria and are discussed in detail. These criteria measure how well the different programs reproduce the X-ray geometries of the 639 input structures. Accordingly, the major strengths and weaknesses of the programs are indicated.

QSAR Modeling: Where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Automatic generation of 3D-atomic coordinates for organic molecules

A system has been developed that can automatically generate three-dimensional atomic coordinates from the constitution of a molecule as expressed by a connection table. The program, CORINA, is applicable to the entire range of organic chemistry. It can also handle structures that are beyond the scope of some other programs, e.g., macrocyclic and polymacrocyclic molecules. Computation times are short and the results compare favorably with data from X-ray crystallography and with those of molecular mechanics calculations.

The Coding of the Three-Dimensional Structure of Molecules by Molecular Transforms and Its Application to Structure-Spectra Correlations and Studies of Biological Activity

A molecular transform, derived from an equation used in electron diffraction studies, is developed that allows the representation of the three-dimensional structure of a molecule by a fixed number of values. Various atomic properties can be taken into account giving high flexibility to this representation of a molecule. This 3D-MoRSE (Molecule Representation of Structures based on Electron diffraction) code retains important structural features such as the mass (see ref 35) and the amount of branching as evidenced by an investigation of monosubstituted benzene derivatives. Furthermore, this molecular representation was able to distinguish between benzene, cyclohexane, and naphthalene derivatives in a dataset of great structural variety. This molecular representation was used in counterpropagation neural networks to distinguish between dopamine D1 and D2 agonists and to group 31 steroids binding to the corticosteroid binding globulin receptor into compounds of high, medium, and low activity. Great promise ...

DERIVING THE 3D STRUCTURE OF ORGANIC MOLECULES FROM THEIR INFRARED SPECTRA

Abstract The representation of the 3D structure of a molecule by a radial distribution function (RDF) code is described. The use of the RDF code for the simulation of an infrared spectrum by a counterpropagation (CPG) neural network is shown. Furthermore, a CPG network can also be operated in reverse mode: on input of an infrared spectrum an RDF code is obtained for which a 3D structure can be searched in a database. An empirical modelling process is used to refine this 3D structure to obtain a three-dimensional model of the molecular structure that corresponds to the infrared spectrum.

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.

Locating Biologically Active Compounds in Medium-Sized Heterogeneous Datasets by Topological Autocorrelation Vectors: Dopamine and Benzodiazepine Agonists

Electronic properties located on the atoms of a molecule such as partial atomic charges as well as electronegativity and polarizability values are encoded by an autocorrelation vector accounting for the constitution of a molecule. This encoding procedure is able to distinguish between compounds being dopamine agonists and those being benzodiazepine receptor agonists even after projection into a two-dimensional self-organizing network. The two types of compounds can still be distinguished if they are buried in a dataset of 8323 compounds of a chemical supplier catalog comprising a wide structural variety. The maps obtained by this sequence of events, calculation of empirical physicochemical effects, encoding in a topological autocorrelation vector, and projection by a self-organizing neural network, can thus be used for searching for structural similarity, and, in particular, for finding new lead structures with biological activity.