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What is PharmaExpert about?
PharmaExpert analyzes the relationships between biological activities, drug-drug interactions and multiple targeting of chemical compounds and selects compounds that have a pre-defined biological activity. It helps answer a question like "How to select the most promising compounds among those known to interact with the selected protein?"
The input of the program is a (set of) SDfile(s) with PASS results.
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PharmaExpert then allows to:
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Select compounds with the required therapeutic, but without adverse effects;
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Comparatively analyze compounds with similar structures;
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Do a multitargeted selection of compounds with multiple mechanisms of action;
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Assess drug-drug interactions with regard to their pharmacokinetic, pharmacodynamic, and adverse effects.
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PharmaExpert performs these tasks through an elaborate system to assign:
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Superclass–subclass relations between biological activities (see box below)
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Mechanistic cause–effect relations in either direction:
- Effects (e.g., anxiolytic effect) are traced back to a molecular mechanism (e.g., acting as 5-Hydroxytryptamine 1A agonist)
- Mechanisms are predicted to exhibit certain effects
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The core of PharmaExpert is MER Base, the Mechanism – Effect Relationships Knowledge Base. It contains numerous classification and cause–effect relationships, all of them manually curated by a staff of experts.
Illustration of PharmaExpert relationships
 Further information:
PharmaExpert Flyer ( download; pdf, 0.4 MB) PharmaExpert Info ( download; pdf, 0.87 MB) PharmaExpert User Guide ( download; pdf, 1.1 MB) |
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Get a picture of PharmaExpert
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Click for an enlarged view.
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More to gain an insight into PharmaExpert's look-and-feel can be found on facebook or in our slide show!
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Or go straight here to request a free demo version!
(Available for Windows® XP/Vista/7)
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New features of release 2012
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The number of activity types has increased by over 3000 terms.
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The underlying MER Base has been improved by adding 450 activity-activity relationships.
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Enhanced functionality for drug-drug interaction analysis: automatic pair-wise analysis for two sdf files with pass prediction results added.
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Recent PharmaExpert publications:
Kumar A., Lohan P., Aneja D.K., Gupta G.K., Kaushik D., Prakash O. (2012) Design, synthesis, computational and biological evaluation of some new hydrazino derivatives of DHA and pyranopyrazoles. Eur J Med Chem. 50:81-89. PubMed. Lagunin A., Filimonov D., Poroikov V. (2010) Multi-targeted natural products evaluation based on biological activity prediction with PASS. Curr. Pharm. Des. 16:1703-1717. PubMed. Geronikaki A.A., Lagunin A.A., Hadjipavlou-Litina D.I., Eleftheriou P.T., Filimonov D.A., Poroikov V.V., Alam I., Saxena A.K. (2008) Computer-aided discovery of anti-inflammatory thiazolidinones with dual cyclooxygenase/lipoxygenase inhibition. J. Med. Chem. 51:1601-1609. PubMed. Filz O., Lagunin A., Filimonov D., Poroikov V. (2008) Computer-aided prediction of QT-prolongation. SAR QSAR Environ. Res. 19:81-90. PubMed. Benaamane N., Nedjar-Kolli B., Bentarzi Y., Hammal L., Geronikaki A., Eleftheriou P., Lagunin A. (2008) Synthesis and in silico biological activity evaluation of new N-substituted pyrazolo-oxazin-2-one systems. Bioorg. Med. Chem. 16:3059-3066. PubMed. |
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