Development of a novel pH sensor based upon Janus Green B immobilized on triacetyl cellulose membrane: Experimental design and optimization.

A novel pH optical sensor was prepared by immobilizing an azo dye called Janus Green B on the triacetylcellulose membrane. Condition of the dye solution used in the immobilization step, including concentration of the dye, pH, and duration were considered and optimized using the Box-Behnken design. The proposed sensor showed good behavior and precision (RSD<5%) in the pH range of 2.0-10.0. Advantages of this optical sensor include on-line applicability, no leakage, long-term stability (more than 6 months), fast response time (less than 1 min), high selectivity and sensitivity as well as good reversibility and reproducibility.

Building optimal regression tree by ant colony system-genetic algorithm: application to modeling of melting points.

The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.

Application of ant colony optimization in development of models for prediction of anti-HIV-1 activity of HEPT derivatives.

Quantitative structure-activity relationship models were derived for 107 analogs of 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio)thymine, a potent inhibitor of the HIV-1 reverse transcriptase. The activities of these compounds were investigated by means of multiple linear regression (MLR) technique. An ant colony optimization algorithm, called Memorized_ACS, was applied for selecting relevant descriptors and detecting outliers. This algorithm uses an external memory based upon knowledge incorporation from previous iterations. At first, the memory is empty, and then it is filled by running several ACS algorithms. In this respect, after each ACS run, the elite ant is stored in the memory and the process is continued to fill the memory. Here, pheromone updating is performed by all elite ants collected in the memory; this results in improvements in both exploration and exploitation behaviors of the ACS algorithm. The memory is then made empty and is filled again by performing several ACS algorithms using updated pheromone trails. This process is repeated for several iterations. At the end, the memory contains several top solutions for the problem. Number of appearance of each descriptor in the external memory is a good criterion for its importance. Finally, prediction is performed by the elitist ant, and interpretation is carried out by considering the importance of each descriptor. The best MLR model has a training error of 0.47 log (1/EC(50)) units (R(2) = 0.90) and a prediction error of 0.76 log (1/EC(50)) units (R(2) = 0.88).

An efficient variable selection method based on the use of external memory in ant colony optimization. Application to QSAR/QSPR studies.

A novel approach for the use of external memory in ant colony optimization strategy for solving descriptor selection problem in quantitative structure-activity/property relationship studies is described. In this approach, several ant colony system algorithms are run to build an external memory containing a number of elite ants. In the next step, all of the elite ants in the external memory are allowed to update the pheromones. Then the external memory is emptied and the updated pheromones are used again, by several ant colony system algorithms to build a new external memory. These steps are iteratively run for certain number of iterations. At the end, the memory will be containing several top solutions to the problem. The proposed approach was applied to solving variable selection problem in quantitative structure-activity/property relationship studies of rate constants of o-methylation of 36 phenol derivatives and activities of 31 antifilarial antimycin compounds, for which the obtained results revealed that both the speed and the solution quality are improved compared to conventional ant colony system algorithms.