CORAL software
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Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R2v= 0.923, and RMSE = 0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.
The algorithm of building up a model for the biological activity of peptides as a mathematical function of a sequence of amino acids is suggested. The general scheme is the following: The total set of available data is distributed into the active training set, passive training set, calibration set, and validation set. The training (both active and passive) and calibration sets are a system of generation of a model of biological activity where each amino acid obtains special correlation weight. The numerical data on the correlation weights calculated by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral). The target function aimed to give the best result for the calibration set (not for the training set). The final checkup of the model is carried out with data on the validation set (peptides, which are not visible during the creation of the model). Described computational experiments confirm the ability of the approach to be a tool for the design of predictive models for the biological activity of peptides (expressed by pIC50).
- Publikační typ
- časopisecké články MeSH
BACKGROUNDS: The CORAL software has been developed as a tool to build up quantitative structure- activity relationships (QSAR) for various endpoints. OBJECTIVE: The task of the present work was to estimate and to compare QSAR models for biochemical activity of various therapeutic agents, which are built up by the CORAL software. METHOD: The Monte Carlo technique gives possibility to build up predictive model of an endpoint by means of selection of so-called correlation weights of various molecular features extracted from simplified molecular input-line entry system (SMILES). Descriptors calculated with these weights are basis for building up correlations "structure - endpoint". RESULTS: Optimal descriptors, which are aimed to predict values of endpoints with apparent influence upon metabolism are crytically compared in aspect of their robustness and heuristic potential. Arguments which are confirming the necessity of reformulation of basics of QSARs are listed: (i) each QSAR model is stochastic experiment. The result of this experiment is defined by distribution into the training set and validation set; (ii) predictive potential of a model should be checked up with a group of different splits; and (iii) only model stochastically stable for a group of splits can be estimated as a reliable tool for the prediction. Examples of the improvement of the models previously suggested are demonstrated. CONCLUSION: The current version of the CORAL software remains a convenient tool to build up predictive models. The Monte Carlo technique involved for the software confirms the principle "QSAR is a random event" is important paradigm for the QSPR/QSAR analyses.
- MeSH
- kvantitativní vztahy mezi strukturou a aktivitou * MeSH
- léčivé přípravky metabolismus MeSH
- lidé MeSH
- metoda Monte Carlo MeSH
- molekulární modely * MeSH
- software * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Quantitative structure - activity relationships (QSARs) for the pIC50 (binding affinity) of gamma-secretase inhibitors can be constructed with the Monte Carlo method using CORAL software (http://www.insilico.eu/coral). The considerable influence of the presence of rings of various types with respect to the above endpoint has been detected. The mechanistic interpretation and the domain of applicability of the QSARs are discussed. Methods to select new potential gamma-secretase inhibitors are suggested.
- MeSH
- Alzheimerova nemoc farmakoterapie MeSH
- kvantitativní vztahy mezi strukturou a aktivitou MeSH
- lidé MeSH
- metoda Monte Carlo MeSH
- objevování léků metody MeSH
- sekretasy * antagonisté a inhibitory chemie metabolismus MeSH
- software MeSH
- výpočetní biologie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
