Property database

The JME Editor is a Java program which allows one to draw, edit, and display molecules and reactions directly within a web page and may also be used as an application in a stand-alone mode. The editor was originally developed for use in an in-house web-based chemoinformatics system but because of many requests it was released to the public. The JME currently is probably the most popular molecule entry system written in Java. Internet sites that use the JME applet include several structure databases, property prediction services, various chemoinformatics tools (such as for generation of 3D structures or molecular orbital visualization), and interactive sites focused on chemistry education [209]. 

Figure 13.5 Decision tree and splitting diagram of partition analysis oftheTRI dataset and an equal randomly selected sample from the Zl NC database. Properties represented (all calculated by QikProp) QPlogPoct = logarithm of octanol-gas partition coefficient QPpolrz = polarizability SASA = solvent accessible surface area). Reproduced with permission [18].

Hussey PJ, Allwood EG, Smertcnko AP. Actin-binding proteins in the Arabidopsis genome database properties of functionally distinct plant actin-depolymerizing factors/cofilins. Phil Trans R Soc Lond... 

Today, fragment coding is still quite important in patent databases (sec Chapter 5, Section 5.11, e.g., Dei went) where Markush structures are also stored. There, the fragments can be applied to substructure or othei types of searches where the fragments arc defined, c.g., on the basis of chemical properties. 

To get to know various databases covering the topics of bibliographic data, physicochemical properties, and spectroscopic, crystallographic, biological, structural, reaction, and patent data... 

Numeric databases primarily contain numeric data on chemical compounds, such as physicochemical values and the results of series of measurements. Therefore, the files correspond to printed tables of numeric property data. Since the attributes of numeric data are different from those of text data, the search has to be managed... 

Gmelin contains over 800 different chemical and physical property fields, and a detailed index of the original literature. Broad categories of data found in the database include ... 

This database provides thermophysical property data (phase equilibrium data, critical data, transport properties, surface tensions, electrolyte data) for about 21 000 pure compounds and 101 000 mixtures. DETHERM, with its 4.2 million data sets, is produced by Dechema, FIZ Chcmic (Berlin, Germany) and DDBST GmhH (Oldenburg. Germany). Definitions of the more than SOO properties available in the database can be found in NUMERIGUIDE (sec Section 5.18). 

The protein sequence database is also a text-numeric database with bibliographic links. It is the largest public domain protein sequence database. The current PIR-PSD release 75.04 (March, 2003) contains more than 280 000 entries of partial or complete protein sequences with information on functionalities of the protein, taxonomy (description of the biological source of the protein), sequence properties, experimental analyses, and bibliographic references. Queries can be started as a text-based search or a sequence similarity search. PIR-PSD contains annotated protein sequences with a superfamily/family classification. 

INPADOC (International Patent Documentation Center) is the most comprehensive tttbliographic database of scientific and technological patent documents in the world. The stock encompasses more than 26 miUion patent documents, more than 59 miUion legal status data, and about 10 million patent famihes (January, 2003). The database contains more than 35 milhon patent citations from 71 patent-issuing organizations (European Patent Office, World Intellectual Property Organization (WlPO)) and is updated weekly with about 40 000 new citations. 

NUMERI- GUIDE American Chemical Society, USA property data direc- tory 875 STN onhne irregular wuAv.stn-inter- national.de/ stndatabases/ databases/nu- merigu.html... 

Direct property prediction is a standard technique in drug discovery. "Reverse property prediction can be exemplified with chromatography application databases that contain separations, including method details and assigned chemical structures for each chromatogram. Retrieving compounds present in the database that are similar to the query allows the retrieval of suitable separation conditions for use with the query (method selection). 

Following the similar structure - similar property principle", high-ranked structures in a similarity search are likely to have similar physicochemical and biological properties to those of the target structure. Accordingly, similarity searches play a pivotal role in database searches related to drug design. Some frequently used distance and similarity measures are illustrated in Section 8.2.1. 

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

Downs G M, P Willett and W Fisanick 1994. Similarity Searching and Qustering of Chemical Structure Databases using Molecular Property Data, journal of Chemical Information and Computer Sciences 34 1094-1102. 

There are now extensive databases of molecular structures and properties. There are some research efforts, such as drug design, in which it is desirable to hnd all molecules that are very similai to a molecule which has the desired property. Thus, there are now techniques for searching large databases of structures to hnd compounds with the highest molecular similarity. This results in hnding a collection of known structures that are most similar to a specihc compound. 

Molecular similarity is also useful in predicting molecular properties. Programs that predict properties from a database usually hrst search for compounds in the database that are similar to the unknown compound. The property of the unknown is probably close in value to the property for the known... 

CHEOPS is based on the method of atomic constants, which uses atom contributions and an anharmonic oscillator model. Unlike other similar programs, this allows the prediction of polymer network and copolymer properties. A list of 39 properties could be computed. These include permeability, solubility, thermodynamic, microscopic, physical and optical properties. It also predicts the temperature dependence of some of the properties. The program supports common organic functionality as well as halides. As, B, P, Pb, S, Si, and Sn. Files can be saved with individual structures or a database of structures. 

The functionality available in MedChem Explorer is broken down into a list of available computational experiments, including activity prediction, align/ pharmacophore, overlay molecules, conformer generation, property calculation, and database access. Within each experiment, the Web system walks the user through a series of questions that must be answered sequentially. The task is then submitted to a remote server, where it is performed. The user can view the progress of the work in their Web browser at any time. Once complete, the results of the calculation are stored on the server. The user can then run subsequent experiments starting with those results. The Web interface includes links to help pages at every step of the process. 

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