Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program

This article provides a systematic study of several important parameters of the Associative Neural Network (ASNN), such as the number of networks in the ensemble, distance measures, neighbor functions, selection of smoothing parameters, and strategies for the user-training feature of the algorithm. The performance of the different methods is assessed with several training/test sets used to predict lipophilicity of chemical compounds. The Spearman rank-order correlation coefficient and Parzen-window regression methods provide the best performance of the algorithm. If additional user data is available, an improved prediction of lipophilicity of chemicals up to 2-5 times can be calculated when the appropriate smoothing parameters for the neural network are selected. The detected best combinations of parameters and strategies are implemented in the ALOGPS 2.1 program that is publicly available at http://www.vcclab.org/lab/alogps.     

Tokamak DEMO-FNS: Concept of magnet system and vacuum chamber

The level of knowledge accumulated to date in the physics and technologies of controlled thermonuclear fusion (CTF) makes it possible to begin designing fusion—fission hybrid systems that would involve a fusion neutron source (FNS) and which would admit employment for the production of fissile materials and for the transmutation of spent nuclear fuel. Modern Russian strategies for CTF development plan the construction to 2023 of tokamak-based demonstration hybrid FNS for implementing steady-state plasma burning, testing hybrid blankets, and evolving nuclear technologies. Work on designing the DEMO-FNS facility is still in its infancy. The Efremov Institute began designing its magnet system and vacuum chamber, while the Kurchatov Institute developed plasma-physics design aspects and determined basic parameters of the facility. The major radius of the plasma in the DEMO-FNS facility is R = 2.75 m, while its minor radius is a = 1 m; the plasma elongation is k ₉₅ = 2. The fusion power is P _FUS = 40 MW. The toroidal magnetic field on the plasma-filament axis is B _t0 = 5 T. The plasma current is I _p = 5 MA. The application of superconductors in the magnet system permits drastically reducing the power consumed by its magnets but requires arranging a thick radiation shield between the plasma and magnet system. The central solenoid, toroidal-field coils, and poloidal-field coils are manufactured from, respectively, Nb₃Sn, NbTi and Nb₃Sn, and NbTi. The vacuum chamber is a double-wall vessel. The space between the walls manufactured from 316L austenitic steel is filled with an iron—water radiation shield (70% of stainless steel and 30% of water).     

Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices

A new method, ALOGPS v 2.0 (http://www.lnh.unil.ch/~itetko/logp/), for the assessment of n-octanol/water partition coefficient, log P, was developed on the basis of neural network ensemble analysis of 12 908 organic compounds available from PHYSPROP database of Syracuse Research Corporation. The atom and bond-type E-state indices as well as the number of hydrogen and non-hydrogen atoms were used to represent the molecular structures. A preliminary selection of indices was performed by multiple linear regression analysis, and 75 input parameters were chosen. Some of the parameters combined several atom-type or bond-type indices with similar physicochemical properties. The neural network ensemble training was performed by efficient partition algorithm developed by the authors. The ensemble contained 50 neural networks, and each neural network had 10 neurons in one hidden layer. The prediction ability of the developed approach was estimated using both leave-one-out (LOO) technique and training/test protocol. In case of interseries predictions, i.e., when molecules in the test and in the training subsets were selected by chance from the same set of compounds, both approaches provided similar results. ALOGPS performance was significantly better than the results obtained by other tested methods. For a subset of 12 777 molecules the LOO results, namely correlation coefficient r(2)= 0.95, root mean squared error, RMSE = 0.39, and an absolute mean error, MAE = 0.29, were calculated. For two cross-series predictions, i.e., when molecules in the training and in the test sets belong to different series of compounds, all analyzed methods performed less efficiently. The decrease in the performance could be explained by a different diversity of molecules in the training and in the test sets. However, even for such difficult cases the ALOGPS method provided better prediction ability than the other tested methods. We have shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data. A comparative performance of the methods as well as a dependence on the number of non-hydrogen atoms in a molecule is also presented.     

Parametric Studies of a Globus-3 Spherical Tokamak with Various Options of Electromagnetic Systems Based on Copper Alloys Using the GLOBSYS Code

Physics of Atomic Nuclei - The engineering part of the GLOBSYS code is presented, and the parameters of the Globus-3 facility, which is a development of the Globus program, are analyzed. The...     

Internet software for the calculation of the lipophilicity and aqueous solubility of chemical compounds

In this paper we describe an Internet Java-based technology that allows scientists to make their analytical software available worldwide. The implementation of this technology is exemplified by programs for the calculation of the lipophilicity and water solubility of chemical compounds available at http://www.lnh.unil.ch/~itetko/logp. Both these molecular properties are key parameters in quantitative structure-activity relationship studies and are used to provide invaluable information for the overall understanding of the uptake distribution, biotransformation, and elimination of a wide variety of chemicals. The compounds can be analyzed in batch or single-compound mode. The single-compound analysis offers the possibility to compare our results with several popular lipophilicity calculation methods, including CLOGP, KOWWIN, and XLOGP. The chemical compounds are analyzed according to SMILES line notation that can be prepared with the JME molecular editor of Peter Ertl. Conversion to SMILES from 56 formats is also available using the molecular structure information interchange hub developed by Pat Walters and Matt Stahl.