Drug Metabolism as an Object of Computational Analysis by the Monte Carlo Method.

Background: Metabolism of therapeutic agents in organism is extremely important from a point of view of drug discovery. Unfortunately, experimental estimation of phenomena related to metabolism is available for the limited number of substances. Under such circumstances, the development of computational method to predict endpoints related to metabolism of therapeutic agents becomes an attractive alternative for expensive and timeconsuming experiments.

CORAL Software: Analysis of Impacts of Pharmaceutical Agents Upon Metabolism via the Optimal Descriptors.

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).

The Utilization of the Monte Carlo Technique for Rational Drug Discovery.

Quantitative structure - activity relationships (QSARs) are built up for three endpoints (i) blood-brain barrier permeability; (ii) butyrylcholinesterase (BChE) inhibitory activity; and (iii) for biological effect of antibacterial drugs. The models are based on utilization of the Monte Carlo technique. The CORAL software available on the Internet has been utilized for the calculations.

Monte Carlo method for predicting of cardiac toxicity: hERG blocker compounds.

The estimation of the cardiotoxicity of compounds is an important task for the drug discovery as well as for the risk assessment in ecological aspect. The experimental estimation of the above endpoint is complex and expensive. Hence, the theoretical computational methods are very attractive alternative of the direct experiment.

QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids.

Antimicrobial peptides have emerged as new therapeutic agents for fighting multi-drug-resistant bacteria. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Therefore, computational techniques had to be applied for process optimization.

Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method.

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).