Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Agar-Based Gel Electrolyte for Electrochromic Device Application

Gel Polymer Electrolytes (GPE) based on agar and containing LiClO4 have been prepared, characterized and applied to electrochromic devices. The ionic conductivity revealed the best result of 6.5 x 10(-5) S/cm for the sample with 17 wt.% of LiClO4, which increased to 5.4 x 10(-4) S/cm at 72 degrees C. TheGPE have been used in electrochromic devices (ECD) with K-glass/WO3/GPE/CeO2-TiO2/K-glass configuration. The ECD changed transmittance values up to 30% between the colored and transparent states. The charge density measurements revealed an increase of 5.5 to 7.5 mC/cm(2) from the first to 500th cycles and then a decrease to 4.4 mC/cm(2) during the next 4500 cycles. Coloration efficiency (eta) of 25 cm(2)/C was obtained.

Vanadium and Titanium Mixed Oxide Films: Synthesis, Characterization and Application as Ion Sensor

Vanadium/titanium mixed oxide films were produced using the sol-gel route. The structural investigation revealed that increased TiO2 molar ratio in the mixed oxide disturbs the V2O5 crystalline structure and makes it amorphous. This blocks the TiO2 phase transformation, so TiO2 stabilizes in the anatase phase. In addition the surface of the sample always presents larger amounts of TiO2 than expected, revealing a concentration gradient along the growth direction. For increased TiO2 molar ratios the roughness of the surface is reduced. Ion sensors were fabricated using the extended gate field effect transistor configuration. The obtained sensitivities varied in the range of 58 mV/pH down to 15 mV/pH according to the composition and morphology of the surface of the samples. Low TiO2 amounts presented better sensing properties that might be related to the cracked and inhomogeneous surfaces. Rising the TiO2 quantity in the films produces homogeneous surfaces but diminishes their sensitivities. Thus, the present paper reveals that the compositional and structural aspects change the surface morphology and electrical properties accounting for the final ion sensing properties of the V2O5/TiO2 films. (C) 2012 The Electrochemical Society. [DOI: 10.1149/2.053206jes] All rights reserved.

Computation of (3)J(HH) coupling constants with a combination of density functional theory and semiempirical calculations. Application to complex molecules

This work evaluates the efficiency of economic levels of theory for the prediction of (3)J(HH) spin-spin coupling constants, to be used when robust electronic structure methods are prohibitive. To that purpose, DFT methods like mPW1PW91. B3LYP and PBEPBE were used to obtain coupling constants for a test set whose coupling constants are well known. Satisfactory results were obtained in most of cases, with the mPW1PW91/6-31G(d,p)//B3LYP/6-31G(d,p) leading the set. In a second step. B3LYP was replaced by the semiempirical methods PM6 and RM1 in the geometry optimizations. Coupling constants calculated with these latter structures were at least as good as the ones obtained by pure DFT methods. This is a promising result, because some of the main objectives of computational chemistry - low computational cost and time, allied to high performance and precision - were attained together. (C) 2012 Elsevier B.V. All rights reserved.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.