Markus Wagener

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.

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.

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

SummaryIt 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.

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.

Evaluation of Molecular Surface Properties Using a Kohonen Neural Network

In this chapter the use of a Kohonen map in structure–activity relationship (SAR) and drug design is described. A transformation of 3D-molecular surfaces into 2D-Kohonen maps is realized on the basis of an application of a Kohonen neural network. The trained neurons of the Kohonen maps are then colored according to the molecular electrostatic potential (MEP) values on the van der Waals surfaces. A template approach is presented for the comparison of the shape and the MEP values of a molecule with that of a reference structure. Applications of these methods are illustrated with datasets of ryanodines, cardiac glycosides, and steroids. The results indicate that the Kohonen neural network may be a useful tool for the investigation of large datasets of molecules and for the fast and accurate comparison of molecular electrostatic potentials and shapes within a series of compounds.

Determination of maximum common 3D substructures using a genetic algorithm

With a genetic algorithm, an optimization method that imitates the adaptation methods of nature, the maximum three‐dimensional substructure common to two molecules can be found. Crossover and mutation operators that are tailored to this problem are introduced. The comparison of two bioactive compounds shows the merits of the method.

3D Structure Descriptors for Biological Activity

Novel ways of coding the structure of chemical compounds are presented and their use for correlating biological activity is explored. These structure codes take account of the three-dimensional arrangement of the atoms in a molecule, or consider molecular surface properties. These molecular representations have been studied with large datasets; various applications to biological activity studies and the definition of chemical diversity will be presented.

A Systemsatic Approach to Finding New Lead Structures Having Biological Activity

The development of a new drug is an enormously largescale and expensive process. Thus, computer simulation methods become to play an increasing role in the development of new pharmacologically active compounds. Most of the commercial software presently used, comes from the U.S.; their deficits have become more and more obvious during the last years. Several methods have been developed in our project to alleviate these problems. The search for new lead structures starts with analyzing large databases of compounds (several hundreds of thousands up to several millions of compounds) zeroing into a few promising structures by increasing sophistication of structure representation. Due to the large number of chemical compounds, a systematic scheme for representing structures was developed: The starting point is the constitution, followed by calculation of the 3D structure, then including conformational flexibility. At each step, a variety of chemical properties can be taken into consideration. In addition, new programs have been developed for the treatment of conformational flexibility. The methods presented are also useful for other areas of application dealing with chemical information. Thus, it was shown that one of these new structure representations is suitable for treating problems in combinatorial synthesis. Neural networks and genetic algorithms are highly important for the investigation of the correlation between structure and biological activity. Complex relationships and huge amounts of data can be processed by these methods. Implementation of these procedures on highly parallel computers has proved that datasets of several hundreds of thousands of structures can be treated with acceptable computation times.

Implementation of Synthesis Strategies in PROLOG

The program STRATOS is presented, that generates synthesis plans for aliphatic compounds. They are made without the help of a reaction database, but solely by examination of the relationships between functional groups in the molecules. Implementation of the program in PROLOG and FORTRAN77 is discussed.