CPANNatNIC software for counter-propagation neural network to assist in read-across.

Background: CPANNatNIC is software for development of counter-propagation artificial neural network models. Besides the interface for training of a new neural network it also provides an interface for visualisation of the results which was developed to aid in interpretation of the results and to use the program as a tool for read-across.

Results: The work presents the details of the program's interface.

Prediction of antiprion activity of therapeutic agents with structure-activity models.

We have developed computational structure-activity models for the prediction of antiprion activity of compounds with known molecular structure. The aim is to apply the developed classification and predictive models in further drug design of antiprion therapeutics. The neural network models developed on the counter-propagation reinforcement learning strategy performed better than the linear regression models.

Structural elucidation of transmembrane transporter protein bilitranslocase: conformational analysis of the second transmembrane region TM2 by molecular dynamics and NMR spectroscopy.

Membrane proteins represent about a third of the gene products in most organisms, as revealed by the genome sequencing projects. They account for up to two thirds of known drugable targets, which emphasizes their critical pharmaceutical importance. Here we present a study on bilitranslocase (BTL) (TCDB 2.

Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum euclidean distance space analysis: a case study.

Alongside the validation, the concept of applicability domain (AD) is probably one of the most important aspects which determine the quality as well as reliability of the established quantitative structure-activity relationship (QSAR) models. To date, a variety of approaches for AD estimation have been devised which can be applied to particular type of QSAR models and their practical utilization is extensively elaborated in the literature. The present study introduces a novel, simple, and effective distance-based method for estimation of the AD in case of developed and validated predictive counter-propagation artificial neural network (CP ANN) models through a proficient exploitation of the euclidean distance (ED) metric in the structure-representation vector space.

Structural analysis of a peptide fragment of transmembrane transporter protein bilitranslocase.

Using a combination of genomic and post-genomic approaches is rapidly altering the number of identified human influx carriers. A transmembrane protein bilitranslocase (TCDB 2.A.

Experimental determination and prediction of bilitranslocase transport activity.

The transport activity of a membrane protein, bilitranslocase (T.C. # 2.

Linear vs. Non-linear Modelling. Case study: modelling of binding affinity of inhibitors to Trypsin.

On the set of 53 trypsin inhibitors the affinity to the covalent bound ligands is modeled using linear (MLR) and non-linear (ANN) methods. Each compound is represented by 343 chemical descriptors. The hypothesis was that linear models are not sufficiently flexible to yield the best model, because in MLR (multiple regression analysis) the number of variables (descriptors) is limited by the number of objects in the training set.

Structure Elucidation of Transmembrane Proteins using Public-Available Databases and Experimental Data on Competitive Inhibition.

We present an approach towards structure elucidation of bilitranslocase, the membrane protein which transports bilirubin from blood to liver cells. The sequence and secondary structure information of transmembrane segments of proteins with known 3D structure is exploited to predict the transmembrane domains of structurally unresolved target protein. With the help of known structures the transmembrane domains are encoded in such a way that it is possible to group and classify them with respect to their specific sub-structural characteristics and to build a model for prediction of transmembrane segments.