Informatica

Ligand Based Virtual Screening methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. Since the databases processed are enormously large, this pre-selection process requires the use of fast and precise methodologies. In this work, the similarity between compounds is measured in terms of electrostatic potential. To do so, we propose a new and alternative methodology, called LBVS-Electrostatic. Accordingly to the obtained results, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one.

A multitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which type of instances, our focus is here on the benchmarking of the behavior of algorithms by applying experiments on test instances. We argue that a good minimum performance benchmark is due to pure random search; i.e. algorithms should do better. We introduce the concept of the cumulative distribution function of the record value as a measure with the benchmark of pure random search and the idea of algorithms being dominated by others. The concepts are illustrated using frequently used algorithms.

The analysis of the method for multiple criteria optimization problems applying a computer network has been presented in the paper. The essence of the proposed method is the distribution of the concrete optimization problem into the network rather than the parallelization of some optimization method. The aim of the authors is to design and investigate the interactive strategies to solve complex multiple criteria problems by applying a computer network. The optimized objective function is the weight sum of the criteria. The multiple criteria problem is iterated by selecting interactively different weight coefficients of the criteria. Therefore, the process is organized by designating the computers as the master (that coordinates the process of other computers) and the slaves (that execute different tasks). In the beginning of the process the researcher allocates a certain number of optimization problems to the network. The objective function optimization problems differ only in weight coefficients of the criteria. As soon as the task of a slave has been executed, the result is sent to the master. Every computer of the network behaves in analogous way. Whenever the researcher receives an immediate result from one of the computers, he gives a decision taking into consideration the latter and all the previous results, i.e., he selects new weight coefficients for the criteria and assigns a new task to the network. Likewise the multiple criteria problem is solved until the result is acceptable for the researcher. The application of the proposed method is illustrated on the basis of the problem for the selection of the optimal nutritive value. Message Passing Interface (MPI) software has been used. The trials have been carried out with the network of computers under the operation system Windows NT.

We consider a possibility of automating the analysis of a computer program realizing the objective function of an extremal problem, and of distributing the calculation of the function value into parallel processes on the basis of results of the analysis. The first problem is to recognize the constituent parts of the function. The next one is to determine their computing times. The third problem is to distribute the calculation of these parts among independent processes. A special language similar to PASCAL has been used to describe the objective function. A new scheduling algorithm, seeking to minimize the maximal finishing time of processing units, was proposed and investigated. Experiments are performed using a computer network.