1. Endothelin antagonists: Search for surrogates of methylendioxyphenyl by means of a kohonen neural network

Anzali, Soheila; Mederski, Werner W. K. R.; Osswald, M.; Dorsch, Dieter

doi:10.1016/s0960-894x(97)10150-0

Cited by 36 publications

(25 citation statements)

References 11 publications

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“…As a result, 10,000-30,000 points sampled at the surface are mapped to about 400-2500 (20x20 to 50x50) neurons arranged into a two-dimensional map of electrostatic potential or another molecular feature. This technique has been used for the comparison of individual maps of bioactive compounds [12] or bioisosteric moieties [22,23]. Although the robust neuron technique is considered to be an efficient comparison tool, the rules for this comparison are actually very unclear.…”

Section: Feature Maps Of the Molecular Electrostatic Potentialmentioning

confidence: 99%

Comparative Molecular Surface Analysis (CoMSA) for Virtual Combinatorial Library Screening of Styrylquinoline HIV-1 Blocking Agents

Niedbala

Polański²,

Gieleciak³

et al. 2006

CCHTS

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We used comparative molecular surface analysis to design molecules for the synthesis as part of the search for new HIV-1 integrase inhibitors. We analyzed the virtual combinatorial library (VCL) constituted from various moieties of styrylquinoline and styrylquinazoline inhibitors. Since imines can be applied in a strategy of dynamic combinatorial chemistry (DCC), we also tested similar compounds in which the -C=N- or -N=C- linker connected the heteroaromatic and aromatic moieties. We then used principal component analysis (PCA) or self-organizing maps (SOM), namely, the Kohonen neural networks to obtain a clustering plot analyzing the diversity of the VCL formed. Previously synthesized compounds of known activity, used as molecular probes, were projected onto this plot, which provided a set of promising virtual drugs. Moreover, we further modified the above mentioned VCL to include the single bond linker -C-N- or -N-C-. This allowed increasing compound stability but expanded also the diversity between the available molecular probes and virtual targets. The application of the CoMSA with SOM indicated important differences between such compounds and active molecular probes. We synthesized such compounds to verify the computational predictions.

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Section: Feature Maps Of the Molecular Electrostatic Potentialmentioning

confidence: 99%

Comparative Molecular Surface Analysis (CoMSA) for Virtual Combinatorial Library Screening of Styrylquinoline HIV-1 Blocking Agents

Niedbala

Polański²,

Gieleciak³

et al. 2006

CCHTS

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“…In the field of pharmaceutical sciences, the SOM was applied for searching useful drugs. [13][14][15] When we apply the method of neural networks to some problems encountered in medical and pharmaceutical fields, it is necessary to analyze each problem in a variety of viewpoints under various conditions. It is expected that appropriate disposal and total unified cognition of an individual problem would be possible through those accumulation.…”

Section: )mentioning

confidence: 99%

An Analysis of Thyroid Function Diagnosis Using Bayesian-Type and SOM-Type Neural Networks

Hoshi

Kawakami

Kumagai

et al. 2005

CHEMICAL & PHARMACEUTICAL BULLETIN

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Both classification problems and QSAR analyses of drugs have been better studied by the multi-layer feedforward neural networks with back-propagation learning than the classical methods in pattern recognition such as linear multiple regression or adaptive least square method, since those problems often involve nonlinear relationships between descriptors and the class (/activity). [1][2][3] Recently Bayesian regularized neural networks (BRNN), [4][5][6][7] which extends back-propagation learning algorithm in order to overcome its defects such as the problems of local trapping, overfitting etc. by introducing probabilistic treatment of the Bayesian inference technique for the synaptic weights, has been successfully applied for QSAR studies 8,9) even for massive sample data. 10)Analytical chemistry is often concerned with the classification of samples into groups on the basis of chemical results while such classification process is called diagnosis if these groups are pathological cases. Zhang et al. studied classification problems of thyroid diagnosis using back-propagation neural networks and showed its usefulness. 11)Kohonen developed the unsupervised learning algorithm that simplified the mapping mechanism of the relatively homogeneous structures found in mammalian brains associated with the processing of sensory data, 12) and showed it possible to generate self-organizing map (SOM) of data. In the field of pharmaceutical sciences, the SOM was applied for searching useful drugs. 13-15)When we apply the method of neural networks to some problems encountered in medical and pharmaceutical fields, it is necessary to analyze each problem in a variety of viewpoints under various conditions. It is expected that appropriate disposal and total unified cognition of an individual problem would be possible through those accumulation.Thyroid Disease Diagnosis The correct diagnosis of thyroid dysfunctions based on clinical and laboratory tests is important work and neural networks would be helpful. Coomans et al. analyzed the thyroid data which takes five hormons related to thyroid and includes 215 patients divided into 3 classes as hyperthyroid, hypothyroid and normal by using the method of linear discriminant analysis. 16)Now, with radioimmunoassay techniques it is possible to measure hormones T3, T4 and TSH in the blood very accurately, and the onset of thyroid disease can be surely confirmed.17) It is recently usual to measure two laboratory tests TSH and free T4 in diagnosing thyroid diseases. Then applying neural networks to such case seems to be not necessary now. Another various applications of the notable neural networks for a diagnosis support, however, would be possible and then to make reanalysis of these laboratory tests data should be important.In the present work we make reanalysis of the thyroid data, which was once analyzed by Coomans et al., using the recent two notable neural networks. The BRNN has a powerful soft pruning method called automatic relevance determination (ARD) 5,6) in addition to the importa...

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“…One of the newest approaches that appears to provide some measure of bioisosterism is the application of the Kohonen maps of the molecular electrostatic potential for socalled bioisosteric design. This was used successfully by the Merck group to design and obtain the endothelin antagonist [10,11]. Therefore, we simulated such maps for some molecules related to arylsulfonylalkanoic acids [12].…”

Section: Synthetic Targetsmentioning

confidence: 99%

Search for new sulfonyl-containing glucophores

Polański

Jarzembek

2002

Pure and Applied Chemistry

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A number of sweeteners contain the sulfur atom and a sulfonyl group. In our current search for new glucophores, several new compounds containing a sulfonyl group were obtained. Synthetic targets have been found both by intuitive modifications of the structural motifs of the known sweet compounds and by the use of molecular modeling techniques. We developed the efficient synthetic routes yielding these compounds and obtained a number of these analogs. We have evaluated the taste quality of the amides and sulfonylated amino acids, finding out that almost all of them are bitter actives. We are now performing basic toxicological tests preceding the systematic taste study within the tetrazoles obtained.

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1. Endothelin antagonists: Search for surrogates of methylendioxyphenyl by means of a kohonen neural network

Cited by 36 publications

References 11 publications

Comparative Molecular Surface Analysis (CoMSA) for Virtual Combinatorial Library Screening of Styrylquinoline HIV-1 Blocking Agents

Comparative Molecular Surface Analysis (CoMSA) for Virtual Combinatorial Library Screening of Styrylquinoline HIV-1 Blocking Agents

An Analysis of Thyroid Function Diagnosis Using Bayesian-Type and SOM-Type Neural Networks

Search for new sulfonyl-containing glucophores

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