Search results for "ALGORITHMS"

Artificial multiple criticality and phase equilibria: an investigation of the PC-SAFT approach

2005

The perturbed-chain statistical associating fluid theory (PC-SAFT) is studied for a wide range of temperature, T, pressure, p, and (effective) chain length, m, to establish the generic phase diagram of polymers according to this theory. In addition to the expected gas-liquid coexistence, two additional phase separations are found, termed "gas-gas" equilibrium (at very low densities) and "liquid-liquid" equilibrium (at densities where the system is expected to be solid already). These phase separations imply that in one-component polymer systems three critical points occur, as well as equilibria of three fluid phases at triple points. However, Monte Carlo simulations of the corresponding sys…

Density functional theory fragment descriptors to quantify the reactivity of a molecular family: Application to amino acids

2007

By using the exact density functional theory, one demonstrates that the value of the local electronic softness of a molecular fragment is directly related to the polarization charge (Coulomb hole) induced by a test electron removed (or added) from (at) the fragment. Our finding generalizes to a chemical group a formal relation between these molecular descriptors recently obtained for an atom in a molecule using an approximate atomistic model [P. Senet and M. Yang, J. Chem. Sci. 117, 411 (2005)]. In addition, a practical ab initio computational scheme of the Coulomb hole and related local descriptors of reactivity of a molecular family having in common a similar fragment is presented. As a b…

A Probabilistic Analysis About the Concepts of Difficulty and Usefulness of a Molecular Ranking Classification

2013

Discerning between the concepts of difficulty and usefulness of a molecular ranking classification is of significant importance in virtual design chemistry. Here, both concepts are viewed from the statistical and practical point of view according to the standard definitions of enrichment and statistical significance p-values. These parameters are useful not only to compare distinct rankings obtained for the same molecular database, but also in order to compare the ones established in distinct molecular sets from an objective point of view.

NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction.

2012

Background NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model. A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore. Results In this work, we present the framework NightShift (NMR Shift …

Self-organized modularization in evolutionary algorithms.

2005

The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenber…

Information entropy-based classification of triterpenoids and steroids from Ganoderma

2015

Abstract A set of 71 triterpenoid and steroid compounds from Ganoderma were periodically classified using a procedure based on information entropy with artificial intelligence. Six features were used in hierarchical order to classify the triterpenoids and steroids structurally. The phytochemicals belonging to the same group in the periodic table present similar antioxidant activity, and those compounds belonging to the same period exhibit maximum resemblance. The periodic classification is related to the experimental bioactivity and antioxidant potency data that are available in the literature: a steroid with a three-ketone group conjugated with two carbon–carbon double bonds in the right s…

Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies

2019

The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experim…

On the convergence of unconstrained adaptive Markov chain Monte Carlo algorithms

2010

Selective Change Driven Imaging: A Biomimetic Visual Sensing Strategy

2011

Selective Change Driven (SCD) Vision is a biologically inspired strategy for acquiring, transmitting and processing images that significantly speeds up image sensing. SCD vision is based on a new CMOS image sensor which delivers, ordered by the absolute magnitude of its change, the pixels that have changed after the last time they were read out. Moreover, the traditional full frame processing hardware and programming methodology has to be changed, as a part of this biomimetic approach, to a new processing paradigm based on pixel processing in a data flow manner, instead of full frame image processing.

Using deep neural networks for kinematic analysis: Challenges and opportunities

2020

Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers.\ud With the advent of artificial intelligence techniques such as deep neural networks, it is now possible\ud to perform such analyses without markers, making outdoor applications feasible. In this paper I summarise\ud 2D markerless approaches for estimating joint angles, highlighting their strengths and limitations.\ud In computer science, so-called ‘‘pose estimation” algorithms have existed for many years. These methods\ud involve training a neural network to detect features (e.g. anatomical landmarks) using a process called\ud supervised learning, which requires ‘‘training” images to be …