Effective consideration of ring structures in CAST/CNMR for highly accurate C NMR chemical shift prediction

A new function that effectively takes into account ring structural environments achieves extensive highly accurate prediction of ¹³C NMR chemical shift in the CAST/CNMR system. The approach adapts a fast and flexible ring perception algorithm and a new CAST coding method for the ring information. ¹³C NMR chemical shift prediction is performed for complicated polycyclic natural products and their synthetic intermediates as the demonstration, which shows the reliability of the function in extending the scope of the practically accurate ¹³C NMR prediction for wide range of organic compounds.     

不同常压流化床煤气化方案的模型预测 Ⅱ. 模型预测及分析

利用已建立的流化床煤气化模型系统地研究了不同气化方案下的流化床煤气化性能，包括空气气化、空气/蒸气气化、空气/二氧化碳气化、氧气/水蒸气气化、氧气/二氧化碳气化5个气化方案，结果表明：空气/水蒸气和氧气/水蒸气方案具有较优的气化效率和较高的煤气品质，氧气/蒸气气化方案在煤气组分、气化效率和热效率等方面比空气/蒸气气化方案更具优势。     

Ab initio prediction of helical segments in polypeptides

An ab initio method has been developed to predict helix formation for polypeptides. The approach relies on the systematic analysis of overlapping oligopeptides to determine the helical propensity for individual residues. Detailed atomistic level modeling, including entropic contributions, and solvation/ionization energies calculated through the solution of the Poisson-Boltzmann equation, is utilized. The calculation of probabilities for helix formation is based on the generation of ensembles of low energy conformers. The approach, which is easily amenable to parallelization, is shown to perform very well for several benchmark polypeptide systems, including the bovine pancreatic trypsin inhibitor, the immunoglobulin binding domain of protein G, the chymotrypsin inhibitor 2, the R69 N-terminal domain of phage 434 repressor, and the wheat germ agglutinin.     

PASS-based prediction of metabolites detection in biological systems

ABSTRACT

Metabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield (‘major’, ‘minor’, ”trace” and ”negligible”) depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways (http://www.way2drug.com/mg).     

A note on the median of a distribution

Summary LetF be a distribution function over the real line. DefineR _p(y)=∫|x−y|^pdF(x) forp≧1. Forp>1 there is a unique minimizer ofR _p(·), sayγ _p. Under mild conditions onF it is shown that exists and that the limit is a median. Thus, one could use this limit as a definition of a unique median. Also it is shown that whereR is the right extremity ofF andL is the left extremity ofF provided that −∞<L≦R<∞. A similar result is available ifL=−∞,R=∞, yetF has symmetric tails.     

Asymmetric risk measures and tracking models for portfolio optimization under uncertainty

Traditional asset allocation of the Markowitz type defines risk to be the variance of the return, contradicting the common-sense intuition that higher returns should be preferred to lower. An argument of Levy and Markowitz justifies the mean/variance selection criteria by deriving it from a local quadratic approximation to utility functions. We extend the Levy-Markowitz argument to account for asymmetric risk by basing the local approximation onpiecewise linear-quadratic risk measures, which can be tuned to express a wide range of preferences and adjusted to reject outliers in the data. The implications of this argument lead us to reject the commonly proposed asymmetric alternatives, the mean/lower partial moment efficient frontiers, in favor of the risk tolerance frontier. An alternative model that allows for asymmetry is the tracking model, where a portfolio is sought to reproduce a (possibly) asymmetric distribution at lowest cost.     

Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model

This paper tests the performance of a simple empirical scoring function on a set of candidate designs produced by a de novo design package. The scoring function calculates approximate ligand-receptor binding affinities given a putative binding geometry. To our knowledge this is the first substantial test of an empirical scoring function of this type on a set of molecular designs which were then subsequently synthesised and assayed. The performance illustrates that the methods used to construct the scoring function and the reliance on plausible, yet potentially false, binding modes can lead to significant over-prediction of binding affinity in bad cases. This is anticipated on theoretical grounds and provides caveats on the reliance which can be placed when using the scoring function as a screen in the choice of molecular designs. To improve the predictability of the scoring function and to understand experimental results, it is important to perform subsequent Quantitative Structure-Activity Relationship (QSAR) studies. In this paper, Bayesian regression is performed to improve the predictability of the scoring function in the light of the assay results. Bayesian regression provides a rigorous mathematical framework for the incorporation of prior information, in this case information from the original training set, into a regression on the assay results of the candidate molecular designs. The results indicate that Bayesian regression is a useful and practical technique when relevant prior knowledge is available and that the constraints embodied in the prior information can be used to improve the robustness and accuracy of regression models. We believe this to be the first application of Bayesian regression to QSAR analysis in chemistry.     

Statistical forecasting for stochastic processes

The main purpose of this paper is to review the efficiency properties of least-squares predictors when the parameters are estimated. It is shown that the criterion of asymptotic best unbiased predictors for general stochastic models is a natural analogue of the minimum mean-square error criterion used traditionally in linear prediction for linear models. The results are applied to log-linear models and autoregressive processes. Both stationary and non-stationary processes are considered.This paper is based on a key note lecture given at the meeting of The Institute of Management Sciences and the Operations Research Society of America, held in Williamsburg, Virginia, January 7–9, 1985.     

Measurements of cloud contribution to rain millimeter wave attenuation by using a radar and radiometers

The measurement technique of cloud contribution to rain attenuation and the equipment consisted of the coherent pulse Doppler radar at wavelength =3.2 cm, the radiometers at=0.4; 0.8 and 1.35 cm and apparatus for signal recording and processing is described. The results of such measurements are given. The Doppler spectrum of the rain backscattered radar signal was used for determination of rain drop size distribution height profile then rain attenuation was calculated and cloud attenuation was determined as the difference between the total attenuation measured by using the radiometers and rain attenuation. The results of this work gave possibility to improve the known rain model of P.Misme for prediction of rain attenuation statistics for Earth-satellite links at millimeter wave.