The familiar Chemical Spreadsheet paradigm is an extremely useful way of presenting structural information together with calculated or measured structural properties. Indeed, most software which handles or stores chemical data will make available a tabular view implementing at least some of the more common spreadsheet functionality such as sorting by columns. Many excellent chemical spreadsheet tools are commercially available and there are also notable freeware/open source examples [1]. Most such software is self-contained which, of course, gives the developers maximum freedom of implementation. This approach has certain potential disadvantages however, particularly considered in the context of a corporate environment:

1. Molecular Descriptors For Cheminformatics Pdf To Excel Online
2. Molecular Descriptors For Chemoinformatics Pdf To Excel

• An interested user needs to buy/download and install the software. This of course is trivial in the case of a 'home' or independent user but may pose almost insurmountable challenges in a 'locked-down' corporate environment

• The user must get to grips with an entirely new piece of software overcoming a potentially steep learning curve

• It is extremely difficult to provide spreadsheet features (powerful calculated columns, visualisation, macro language, etc) which begin to rival those of the industry standard, Microsoft Excel - a program already very familiar to target users.

‘plug and play’ cheminformatics toolkit With over 1000 API functions, Accord SDK lets. 100 molecular descriptors & properties can be calculated from 2D structure, giving you the power. Excel - adding all the functionality available in Accord into MS. Such as contained in molecular descriptors. A Java framework for cheminformatics. As a result, it is possible to read in a variety of molecular formats, calculate molecular descriptors and evaluate fingerprints. In addition, we describe. 4 Chemical Informatics Functionality in R Function Description.

The last point suggests a different approach in which the chemistry engine is build on top of Excel. This tactic appears extremely attractive partly because the potential developer can concentrate on implementing chemical functionality but also because of the ubiquity and power of Excel. Two well-known realisations of this approach are Isis for Excel [2] and Accord for Excel [3].

Solutions of this type are typically implemented as Excel AddIns, using Visual Basic for Applications (VBA) to interface with chemistry engines. Structures are usually stored on the spreadsheets as some kind of object (including structure-layout or image data) which may be interpreted by the chemistry engine for visualisation and calculation purposes. To ensure that structure objects display and sort properly, it is usually necessary to intercept several of Excel's fundamental calls (such as the main calculation routine). This necessity, together with the size of the stored objects, can lead to rapid degradation of performance for spreadsheets containing large numbers of structures.

Bearing the foregoing in mind, LICSS was designed to appeal particularly to corporate users of Excel for Windows. Because of one of the authors' experience of corporate locked-down environments and because LICSS was to be a 'hobby' project, initially with just one spare-time developer, some rather specific design criteria were developed:

• LICSS should require no installation beyond file copying. Users should be able to share spreadsheets with fully automatic installation (if necessary)

• LICSS would implement chemistry functionality by interfacing with the excellent CDK Java library [4, 5] (and the corresponding rendering package, JChemPaint [6])

• Structures should be stored purely as Smiles strings in cells; structure rendering would be on-the-fly

• LICSS spreadsheets would not intercept Excel's calculation calls

• An Excel add-in would not be used (they normally need user installation and can require admin privileges). Any necessary VBA would exist on each chemically-enabled spreadsheet.

User Implementation and Features

Timings for common cheminformatics tasks using LICSS.

Times refer to a 2.13 GHz laptop with 4 GB of memory running Vista/Microsoft Excel 2007.

Two datasets were used: [1]: a set containing ~1.6 k pesticidal compounds, [2]: a set containing ~27 k anti-malarial compounds.

Technical Implementation

The main enabling program is contained in an Excel for Windows workbook (Excel 97-2003 format), EnableChemicalSpreadsheetV2.1.xls. It is written in VBA using the VBA Extensibility library which allows the program to copy code to and create code in the workbook being enabled. Most code is simply copied from EnableChemicalspreadsheetV2.1.xls but some event handling routines are created specifically for the workbook being enabled; this makes possible features such as structure pop-up upon mouse hover over chart data points for example.

The CDK and OPSIN Java libraries are accessed in one of two ways. For batch processes (such as Substructure and Similarity searching) the relevant compounds are first written to file in Smiles (SMI) file format (after an in-sheet fingerprint search if necessary). Then an executable JAR file, CDKSSWin.jar is synchronously executed. This contains a number of routines corresponding to each of the available LICSS programs and taking appropriate input/output file and other control parameters. Each of these routines creates an output file and terminates, whereupon the calling VBA processes the output file appropriately. The synchronous Jar file execution is done without a command line window through Javaw.exe and CDKSSWin.jar starts by creating a pop-up Swing progress window. In this way, the routines appear to run as part of Excel.

CDK classes are widely used within CDKSSWin.jar to provide cheminformatics methods (fingerprint generation, substructure searching etc). Where available, existing open source code was adapted to use the CDK minimising the need to rewrite algorithms (eg for Jarvis Patrick clustering and Sammon projection; see acknowledgments). Algorithms for R-Group table generation, similarity searching and diverse compound picking were written in-house.

Calls to JChemPaint, to display structure editing or structure display windows, are handled quite differently. Originally (version 1 x), the JChemPaint applet was used inside a WebBrowser control within VBA. However, this approach was not suitable for the rapid display of several structures (eg for displaying all worksheet structures). From version 2.0 onwards, a JVM is run within the Excel process space so calls to Java can be made directly, without per action initialisation or context switching overheads. Calls to Java of this type are made possible by creating C++ proxies for each Java method (contained within a single CDecl dll file, CDKInterfaceDll.dll) using JNI via the open-source Jace project technology [10]. The C++ proxy functions may then be declared and called directly from VBA.

In practice, after one-off Java initialisation, this approach enables extremely rapid access to Java routines directly from VBA in Excel. Thus, for example, a user can render a screen's worth of structures from Smiles in < 1 second. The same method has been used for all the new Excel formulas - for example, on a 2.13 MHz laptop with 4 GB of memory running Vista, a formula entry such as: ' = GetCDKDescriptor(C2,'XLogP',1)' will calculate the XLogP descriptor for > 100 compounds per second when copied down for a column of Smiles strings (see also Table 1).

Molecular descriptors play a fundamental role in chemistry, pharmaceutical sciences, environmental protection policy, and health researches, as well as in quality control, being the way molecules, thought of as real bodies, are transformed into numbers, allowing some mathematical treatment of the chemical information contained in the molecule. This was defined by Todeschini and Consonni as:

'The molecular descriptor is the final result of a logic and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment.'^[1]

By this definition, the molecular descriptors are divided into two main categories: experimental measurements, such as log P, molar refractivity, dipole moment, polarizability, and, in general, additive physico-chemical properties, and theoretical molecular descriptors, which are derived from a symbolic representation of the molecule and can be further classified according to the different types of molecular representation.

The main classes of theoretical molecular descriptors are: 1) 0D-descriptors (i.e. constitutional descriptors, count descriptors), 2) 1D-descriptors (i.e. list of structural fragments, fingerprints),3) 2D-descriptors (i.e. graph invariants),4) 3D-descriptors (such as, for example, 3D-MoRSE descriptors, WHIM descriptors, GETAWAY descriptors, quantum-chemical descriptors, size, steric, surface and volume descriptors),5) 4D-descriptors (such as those derived from GRID or CoMFA methods, Volsurf).

Invariance properties of molecular descriptors[edit]

The invariance properties of molecular descriptors can be defined as the ability of the algorithm for their calculation to give a descriptor value that is independent of the particular characteristics of the molecular representation, such as atom numbering or labeling, spatial reference frame, molecular conformations, etc. Invariance to molecular numbering or labeling is assumed as a minimal basic requirement for any descriptor.

Two other important invariance properties, translational invariance and rotational invariance, are the invariance of a descriptor value to any translation or rotation of the molecules in the chosen reference frame. These last invariance properties are required for the 3D-descriptors.

Degeneracy of molecular descriptors[edit]

This property refers to the ability of a descriptor to avoid equal values for different molecules. In this sense, descriptors can show no degeneracy at all, low, intermediate, or high degeneracy. For example, the number of molecule atoms and the molecular weights are high degeneracy descriptors, while, usually, 3D-descriptors show low or no degeneracy at all.

Basic requirements for optimal descriptors[edit]

1. Should have structural interpretation
2. Should have good correlation with at least one property
3. Should preferably discriminate among isomers
4. Should be possible to apply to local structure
5. Should possible to generalize to 'higher' descriptors
6. Should be simple
7. Should not be based on experimental properties
8. Should not be trivially related to other descriptors
9. Should be possible to construct efficiently
10. Should use familiar structural concepts
11. Should change gradually with gradual change in structures
12. Should have the correct size dependence, if related to the molecule size

See also[edit]

References[edit]

Molecular Descriptors For Cheminformatics Pdf To Excel Online

1. ^Roberto Todeschini and Viviana Consonni, Handbook of Molecular Descriptors, Wiley-VCH, 2000.http://www.moleculardescriptors.eu/books/handbook.htm

Bibliography[edit]

Roberto Todeschini and Viviana Consonni, Molecular Descriptors for Chemoinformatics (2 volumes), Wiley-VCH, 2009.

Mati Karelson, Molecular Descriptors in QSAR/QSPR, John Wiley & Sons, 2000.

James Devillers and Alexandru T. Balaban (Eds.), Topological indices and related descriptors in QSAR and QSPR. Taylor & Francis, 2000.

Lemont Kier and Lowell Hall, Molecular structure description. Academic Press, 1999.

Alexandru T. Balaban (Ed.), From chemical topology to three-dimensional geometry. Plenum Press, 1997.

External links[edit]

Molecular Descriptors For Chemoinformatics Pdf To Excel