Abbildung elektrostatischer Potentiale muscarinischer und nicotinischer Agonisten mit künstlichen neuronalen Netzen

Structural activity analysis, spectroscopic investigation, biological and chemical properties interpretation on Beta Carboline using quantum computational methods

In this methodological work, the structural activity analysis have been carried out on β-Carboline to study the anti cancer activity and the way of improving the biological activity. The molecular spectroscopic tools were used to evaluate all the experimental data like spectral results and data were validated by the computational, HyperChem and Osiris tools. The structural, biological and physico-chemical related analyses have been performed to interpret the properties. The GPCR ligand calculated to be 0.11 for generating pharmacokinetic process, Specified drug information for the compound, was congregated from all types of structural activity which was drawn by spectral and HyperChem data. The σ and π interaction band gap (6.18 eV) ensured the drug consistency. The Mulliken charge process distribution was mapped, the charge orientation assignment was checked; the acquired negative charge potential consignment for the cause of antibiotic impact was verified. The molecular orbital interaction study was carried out to identify the origination of degeneracy of interaction causing drug mechanism. Using NMR spectral pattern, the chemical reaction path was recognized and the nodal region dislocation was distinguished on chemical shift. The Electronegativity (χ) and Electrophilicity charge transfer found to be 3.83 and 0.215, confirmed charge complex transfer for activating drug process in the compound. The molecular nonbonding section was thoroughly observed in order to find the occupancy energy, was the key process to initiate drug activity. The bathochromic electronic shift was observed and the existence of CT complex was discussed. The hindering of toxicity was inspected on inevitable chirality of the compound by specifying VCD spectrum.

Supervised self-organizing maps in drug discovery. 2. Improvements in descriptor selection and model validation

The modeling of nonlinear descriptor-target relationships is a topic of considerable interest in drug discovery. We, herein, continue reporting the use of the self-organizing map-a nonlinear, topology-preserving pattern recognition technique that exhibits considerable promise in modeling and decoding these relationships. Since simulated annealing is an efficient tool for solving optimization problems, we combined the supervised self-organizing map with simulated annealing to build high-quality, highly predictive quantitative structure-activity/property relationship models. This technique was applied to six data sets representing a variety of biological endpoints. Since a high statistical correlation in the training set does not indicate a highly predictive model, the quality of all the models was confirmed by withholding a portion of each data set for external validation. Finally, we introduce new cross-validation and dynamic partitioning techniques to address model overfitting and assessment.

2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors

The inhibitory activity towards p34(cdc2)/cyclin b kinase (CBK) enzyme of 30 cytokinin-derived compounds has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multi-linear regression analysis (MRA) and artificial neural network (ANN) approaches respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training. The best ANN with three input variables was able to explain about 87% data variance in comparison with 80% by the linear equation using the same number of descriptors. Similarly, the neural network had higher predictive power. The MRA model showed a linear dependence between the inhibitory activities and the spatial distributions of masses, electronegativities and van der Waals volumes on the inhibitors molecules. Meanwhile, ANN model evidenced the occurrence of non-linear relationships between the inhibitory activity and the mass distribution at different topological distance on the cytokinin-derived compounds. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (SOM) built using the input variables of the best neural network.

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.

The non-grid technique for modeling 3D QSAR using self-organizing neural network (SOM) and PLS analysis: application to steroids and colchicinoids

A novel method for modeling 3D QSAR has been developed. The method involves a multiple training of a series of self-organizing networks (SOM). The obtained networks have been used for processing the data of one reference molecule. A scheme for the analysis of such data with the PLS analysis has been proposed and tested using the steroids data with corticosteroid binding globulin (CBG) affinity. The predictivity of the CBG models measured with the SDEP parameter is among the best one reported. Although 3-D QSAR models for colchicinoid series is far less predictive, it allows for a discussion on the relative influence of the structural motifs of these compounds.

The receptor-like neural network for modeling corticosteroid and testosterone binding globulins

A neural-net method for simulation of corticosteroid and testosterone binding globulin (CBG, TBG)-ligand interactions is presented. Molecular modeling provides the geometry and partial atomic charges of 31 steroid molecules. The atomic coordinates within the molecule of the compound of the highest affinity are then used to train a self-organizing map (SOM) that forms a template for the comparison to other molecules. Comparison is done using a series of normalized patterns produced by the SOM. The template SOM, after overlaying on the set of random vectors, mimics the topology of the receptor site and is used to train unsupervisedly a neuron capable of recognizing the degree of similarity between the reference and tested patterns. A good correlation is observed for signals generated by the neuron plotted against the experimental CBG affinities. For TBG affinity modeling a modified procedure is designed which is capable of separating electrostatic and shape effects. The high predictive power of the model is achieved by keeping close analogy to the processes taking place at the real receptor sites.

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.