Researchers Use Molecular Dynamics and Machine Learning to Create ‘Hyper-predictive’ Computer Models for Drug Discovery May 15, 2017 Tracey Peake Molecular dynamics (MD) simulations of ERK2 inhibitors to extract MD descriptors for next-generation cheminformatics analysis and machine learning. However, one aspect common to these tools is that they do not have direct access to the information that is available from chemical structures, such as contained in molecular descriptors. We describe the rcdk package that provides the R user with access to the CDK, a Java framework for cheminformatics. Download reference work entry PDF. The recently released curated small-molecule databases are. The OpenBabel chemoinformatics package (is primarily a file converter toolbox (KNIME chemistry nodes). (4) calculation of important molecular descriptors with the Mold2 node.

• Molecular Descriptors For Cheminformatics Pdf Creator Online
• Molecular Descriptors For Cheminformatics Pdf Creator Pdf

QSPR methods represent a useful approach in the drug discovery process, since they allow predicting in advance biological or physicochemical properties of a candidate drug. For this goal, it is necessary that the QSPR method be as accurate as possible to provide reliable predictions. Moreover, the selection of the molecular descriptors is an important task to create QSPR prediction models of low complexity which, at the same time, provide accurate predictions.

In this work, a matrix-based method [1] is used to transform the original data space of chemical compounds into an alternative space where compounds with different target properties can be better separated. For using this approach, QSPR is considered as a classification problem. The advantage of using adaptive matrix metrics is twofold: it can be used to identify important molecular descriptors and at the same time it allows improving the classification accuracy.

A recently proposed method making use of this concept [2] is extended to multi-class data. The new method is related to linear discriminant analysis and shows better results at yet higher computational costs. An application for relating chemical descriptors to hydrophobicity property [3] shows promising results.

References

• Strickert M, Keilwagen J, Schleif F-M, Villmann T, Biehl M. Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. Lecture Notes in Computer Science. 2009;5517/2009:933–940. full_text. [Google Scholar]
• Strickert M, Soto AJ, Keilwagen J, Vazquez GE. Towards matrix-based selection of feature pairs for efficient ADMET prediction. Argentine Symposium on Artificial Intelligence, ASAI. 2009. pp. 83–94.
• Soto AJ, Cecchini RL, Vazquez GE, Ponzoni I. A Wrapper-based Feature Selection Method for ADMET Prediction using Evolutionary Computing. Lecture Notes in Computer Science. 2008;4973/2008:188–199. full_text. [Google Scholar]

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.

Transition to Democracy and Citizenship Education in Portugal: Changes and Continuities in the Curricula and in. This period, Portugal experienced dictatorship, the last socialist revolution in Europe. (1974) (Barreto, 2002), the end of colonial. Portugal hoje: O medo de existir. [Portugal today: The afraid of existence].

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]

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.

Molecular Descriptors For Cheminformatics Pdf Creator Online

External links[edit]

Molecular Descriptors For Cheminformatics Pdf Creator Pdf