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PASS biological activity spectrum predictions in the enhanced open NCI database browser
Vladimir V Poroikov1, Dmitrii A Filimonov, Wolf-Dietrich Ihlenfeldt
1Laboratory of Structure-Function Based Drug Design, V.N. Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 10 Pogodinskaya Street, Moscow 119121, Russia.
Journal of Chemical Information and Computer Sciences
|January 28, 2003
Summary
The Prediction of Activity Spectra for Substances (PASS) program was applied to predict compound activities in a large database. PASS predictions showed significant enrichment for antineoplastic activity, aiding drug discovery.
Area of Science:
- Computational chemistry
- Pharmacology
- Cheminformatics
Background:
- The NCI Open Database contains a vast collection of chemical compounds.
- Predicting the biological activities of these compounds is crucial for drug discovery.
- Existing methods for activity prediction can be computationally intensive or limited in scope.
Purpose of the Study:
- To describe the application of the Prediction of Activity Spectra for Substances (PASS) program to a large chemical database.
- To incorporate millions of PASS predictions into an enhanced database browser for user accessibility.
- To demonstrate the utility of PASS predictions for identifying compounds with specific biological activities, such as antineoplastic effects.
Main Methods:
- Applied the PASS program to approximately 250,000 compounds from the NCI Open Database.
- Integrated over 64 million PASS predictions into the Enhanced NCI Database Browser.
- Evaluated PASS predictions for various activity types, including angiogenesis inhibition, HIV antiviral activity, and antineoplastic action.
Main Results:
- A total of 565 different activity types were included in the PASS predictions.
- PASS predictions demonstrated a substantial enrichment over random selection for antineoplastic activity.
- The Enhanced NCI Database Browser allows complex searches combining PASS predictions with physicochemical and substructural data.
Conclusions:
- The PASS program is a valuable tool for predicting a wide range of compound activities.
- The Enhanced NCI Database Browser facilitates efficient exploration of large chemical datasets with integrated activity predictions.
- PASS predictions can significantly aid in the identification of potential drug candidates, particularly for antineoplastic applications.