𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Quantitative prediction of substituted products based on quantum-chemical parameters and neural network method

✍ Scribed by Wang Xue-Ye; Song Huang

Publisher
John Wiley and Sons
Year
2010
Tongue
English
Weight
327 KB
Volume
18
Category
Article
ISSN
0256-7660

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

The criterion of orientating group of electrophilic aromatic nitration was discussed by means of pattern recognition method with quantum‐chemical parameters as features, and the product ratios of the reactions were quantitatively calculated using artificial neural network (ANN) method with the same parameters as inputs, based on the ab initio calculation of quantum chemistry, The quantum‐chemical parameters involved orbital energy, orbital electron population, atomic total electron density and atomic net charge. The predicted values are in agreement with experimental results and the predicted error of the ANN with quantum‐chemical parameters for the reaction is the smallest among the all methods.

📜 SIMILAR VOLUMES

Prediction of Chemical Oxygen Demand (CO
Prediction of Chemical Oxygen Demand (COD) Based on Wavelet Decomposition and Neural Networks
✍ Davut Hanbay; Ibrahim Turkoglu; Yakup Demir 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 733 KB

## Abstract The chemical oxygen demand (COD) parameter of a wastewater treatment plant is predicted based on wavelet decomposition, entropy, and neural networks (NN) for rapid COD analysis. This paper also describes the usage of wavelet and NNs for parameter prediction. Data from a wastewater treat

ChemInform Abstract: Prediction of Enzym
ChemInform Abstract: Prediction of Enzyme Binding: Human Thrombin Inhibition Study by Quantum Chemical and Artificial Intelligence Methods Based on X-Ray Structures.
✍ G. Mlinsek; M. Novic; M. Hodoscek; T. Solmajer 📂 Article 📅 2010 🏛 John Wiley and Sons ⚖ 26 KB 👁 1 views

## Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable v