The Principle of Minimum Chemical Distance and the Principle of Minimum Structure Change

The principle of minimum chemical distance (PMCD) is derived from the general theory of the BE- and R-matrices, and makes precise the vague classical “principle of minimum structure change”. In fact, the PMCD may be seen as the principle of minimum structure change in mathematical terms. It provides a quantitative measure of chemical similarity of isomeric molecular systems. Its applications lie in the fields of correlations of substructures, elucidation of reaction mechanisms, and evaluation of synthetic pathways as is illustrated by examples. The mathematical foundations of the computer assisted application of the PMCD are presented.

Some solved and unsolved problems of chemoinformatics

The field of chemoinformatics has developed from different roots, starting in the 1960s. These branches have now merged into a scientific discipline of its own, exchanging ideas and methods across different areas of chemistry. In the last 40 years chemoinformatics has achieved a lot. Without access to the databases in chemistry developed with chemoinformatics methods, modern chemical research would not be able to work at its present high level of competence. However, there are quite a few challenges, such as drug design and understanding the effect of chemicals on human health and on the environment, as well as furthering our knowledge of chemistry and of biological systems, that can benefit from a more intensive use of chemoinformatics methods. Approaches to meet these challenges will be briefly outlined. All this emphasizes that chemoinformatics has matured into a scientific discipline of its own that reaches out to many other chemical fields and will increase in attractiveness to students and researchers.

Infrared Spectra Simulation of Substituted Benzene Derivatives on the Basis of a 3D Structure Representation

The identification of chemical compounds from their infrared spectra faces new challenges from novel experimental techniques such as combinatorial chemistry. To rapidly provide estimates for the infrared spectra of candidate structures, an empirical approach to the modeling of the relationships between the 3D structure of a molecule and its infrared spectrum has been developed. This method is based on a novel 3D structure representation and a powerful modeling technique, a counterpropagation neural network. A dataset of 871 mono-, di-, and trisubstitued benzene derivatives is analyzed with this approach.

Decision support systems for chemical structure representation, reaction modeling, and spectra simulation

The choice of an appropriate structure coding scheme is the secret to success in QSAR studies. Depending on the problem at hand, 2D or 3D descriptors have to be chosen; the consideration of electronic effects might be crucial, conformational flexibility has to be of special concern. Artificial neural networks, both with unsupervised and with supervised learning schemes, are powerful tools for establishing relationships between structure and physical, chemical, or biological properties. The EROS system for the simulation of chemical reactions is briefly presented and its application to the degradation of s-triazine herbicides is shown. It is further shown how the simulation of chemical reactions can be combined with the simulation of infrared spectra for the efficient identification of the structure of degradation products.

Internet-assisted exercises in structural analysis

In this report we describe our experience in the analytical chemistry curriculum of teaching spectrometer principles and preparing spectroscopy laboratory exercises by means of virtual instruments. The benefits of the intensified preparation of laboratory exercises by virtual instruments will be evaluated with respect to the subsequent handling of real instruments. The utilization of in-house electronic media with Internet resources for elucidation and verification of a structural assignment will also be considered.

A combined application of reaction prediction and infrared spectra simulation for the identification of degradation products of s-triazine herbicides

Substance identification in analytical chemistry is usually performed by comparing an experimental spectrum with a reference spectrum. Especially in environmental chemistry, reference spectra from databases are only available for a limited number of compounds. The combination of the reaction prediction system EROS and of infrared spectra simulation is a powerful tool for computer-assisted substance identification. First, possible degradation products of a chemical are predicted and then the infrared spectra of all these compounds are simulated. Comparison of the simulated infrared spectra with experimental spectra allows one to identify the structure of compounds. The method is demonstrated with the example of s-triazine herbicides.

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.

Self-organizing neural networks for screening and development of novel artificial sweetener candidates

The use of Kohonen feature maps for the visualization of various aspects of molecular similarity is briefly reviewed and illustrated. It is shown that a specific feature of self-organizing maps (SOM) makes them of special interest for the screening of compounds. In particular, these methods were used to design candidates for new sweeteners, which were then synthesized.

Steroid binding by antibodies and artificial receptors: exploration of theoretical methods to determine the origins of binding affinities and specificities

Binding mode calculations for complexes between an artificial paracyclophane receptor and digoxins, cholic acids as well as cortisone steroids show encapsulation of different ring combinations. Docking experiments were performed between the 26-10 antibody and digoxins. Coordination affinity arises from hydrophobic desolvation and van der Waals interactions rather than from hydrogen bonds. The specificity and affinity arises mainly from shape complementarity. Computed binding free energies and Kohonen neural network computations both point to physicochemical and structural similarities of natural antibodies and artificial receptors.

Rapid access to infrared reference spectra of arbitrary organic compounds: scope and limitations of an approach to the simulation of infrared spectra by neural networks

Substance identification by infrared spectroscopy is performed by comparison of the experimental spectrum with a reference spectrum from a printed compilation or a database. If the analyzed compound can not be found in a database the corresponding reference spectrum has to be simulated. In order to achieve this, several reasonable candidates of structures for the compound at hand have to be conceived and for all these, infrared spectra have to be developed. The simulated spectrum that is most similar to the experimental suggests the correct structure. A rapid spectrum prediction method based on neural networks has been developed that supplies reference spectra for any organic compound. The scope and limitations of this method will be discussed on a test set of 16 compounds representing a broad range of organic chemistry.

Superposition of three-dimensional chemical structures allowing for conformational flexibility by a hybrid method

The superposition of three-dimensional structures is the first task in the evaluation of the largest common three-dimensional substructure of a set of molecules. This is an important step in the identification of a pharmacophoric pattern for molecules that bind to the same receptor. The superposition method described here combines a genetic algorithm with a numerical optimization method. A major goal is to adequately address the conformational flexibility of ligand molecules. The genetic algorithm optimizes in a nondeterministic process the size and the geometric fit of the substructures. The geometric fit is further improved by changing torsional angles combining the genetic algorithm and the directed tweak method. This directed tweak method is based on a numerical quasi-Newton optimization method. Only one starting conformation per molecule is necessary. Molecules having several rotatable bonds and quite different initial conformations are modified to find large structural similarities. A set of angiotensin II antagonists is investigated to illustrate the performance of the method.

Checking the projection display of multivariate data with colored graphs

Projection methods such as principal component analysis (PCA), nonlinear mapping (NLM), and the self-organizing map (SOM) are valuable algorithms for visualizing multidimensional data in a two-dimensional plane. Unfortunately, the reduction of the dimensionality involves distortions. In an attempt to graphically localize the distortions of the projected data, we suggest superposing colored graphs onto the 2D plots. The color of the edges of these graphs encodes the original high-dimensional distances between the connected points. The method is applied to a cluster analysis of 37 biologically active compounds and 471 molecules represented by a structural 3D descriptor.

The comparison of geometric and electronic properties of molecular surfaces by neural networks: application to the analysis of corticosteroid-binding globulin activity of steroids

It is shown how a self-organizing neural network such as the one introduced by Kohonen can be used to analyze features of molecular surfaces, such as shape and the molecular electrostatic potential. On the one hand, two-dimensional maps of molecular surface properties can be generated and used for the comparison of a set of molecules. On the other hand, the surface geometry of one molecule can be stored in a network and this network can be used as a template for the analysis of the shape of various other molecules. The application of these techniques to a series of steroids exhibiting a range of binding activities to the corticosteroid-binding globulin receptor allows one to pinpoint the essential features necessary for biological activity.

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.

Comparison of structurally different allosteric modulators of muscarinic receptors by self-organizing neural networks

Similarities in the molecular structure and surface properties of the allosteric modulators of muscarinic receptors, alcuronium, gallamine, tubocurarine, and the hexamethonium compound W84, a well-known pharmacological tool, are explored. The analysis of the molecular electrostatic potential (MEP) as well as of the shape of the molecular surface is performed by self-organizing neural networks. A distorted sandwich conformation of W84 is suggested to be the active form. The importance of the MEP for binding of these compounds could be established.

Beyond the hyperactive molecule: search, salvage and visualization of chemical information from the Internet

The established exchange mechanisms for chemical information are under attack from new information distribution channels on the Internet. Increasingly chemical information is distributed by means of WWW pages and similar media. However, most of this information is still primarily intended for human browsing. The search for chemical information and the reuse of encoded structures and their attached data is complicated and often impossible because of the unorganized structure of the information and the lack of tools for search, display and salvage of chemical information to help with the extraction of reusable information from Webspace. The situation is complicated by the lack of standards and formats powerful enough to encode in computer-readable form sophisticated chemical information and informational relationships. The rapid evolution of information exchange mechanisms on the Internet is another problem. The unsettled situation demands a new generation of intelligent chemistry-aware tools for information retrieval from the Net. These tools must be capable of adapting to new trends and information models as well as new information types without constant redesign and should be themselves extendable and updatable by components distributed via the same network connections as the chemical data they are supposed to deal with. We introduce a set of tools which encapsulate the established Internet (especially WWW) information transfer and visualization methods and extend them to a new level of chemical information handling.

Variation of the oxime function of bispyridinium-type allosteric modulators of M2-cholinoceptors

The bisbenzylether of the bispyridinium oxime, DUO 3-O (4,4'-Bis-[(2,6-dichlorobenzyloxyimino)-methyl]-1,1'-propane-1,3- diyl-bispyridinium dibromide), has been found to stabilize the antagonist binding to the M2-cholinoceptors which is indicative of an allosteric action. The oxygen of the oxime group was replaced with a nitrogen and a CH2-group leading to DUO 3-N and 3-C, respectively. The allosteric potency of the compounds was characterized by the concentrations which retarded the rate of dissociation of [3H]N-methyl-scopolamine from porcine cardiac cholinoceptors by a factor of 2 (EC50). The hydrazone derivative DUO 3-N was found to be the most active compound (ED50 = 2.7 microM) exceeding in activity DUO 3-O by a factor of 1.6 and DUO 3-C by a factor of 5. No correlation was found between the lipophilicity, determined as octanol/water partition coefficient, and the allosteric potency. The distribution of charges in the molecules was calculated by means of PEOE and displayed as Kohonen maps: The calculations exhibit a shift of the positive charge in the pyridinium ring from the nitrogen to the carbon atom in para-position when going from DUO 3-C to 3-O and 3-N. This shift parallels the rank order of the allosteric potency. From these results the conclusion has been drawn that the between distance the terminal ring-system and the positive charge is pivotal for the interaction of the allosteric modulators with the receptor protein.

The beauty of molecular surfaces as revealed by self-organizing neural networks

The Kohonen neural network is a self-organizing network that can be used for the projection of the surface properties of molecules. This allows one to view properties on a molecular surface, like the electrostatic potential in a single picture. These maps are useful for the comparison of molecules and provide a new definition of molecular similarity.