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Guided Iterative Substructure Search (GI-SSS) - A New Trick for an Old Dog
1Boehringer Ingelheim Pharma GmbH & Co. KG, Discovery Research, Lead Identification and Optimization Support, Computational Chemistry, Birkendorfer Straße 65, 88397, Biberach an der Riss, Germany. nils.weskamp@boehringer-ingelheim.com.
Molecular Informatics
|August 6, 2016
Summary
This study introduces two heuristic extensions for substructure search (SSS) to simplify iterative query refinement. These methods provide intuitive feedback, reducing trial-and-error cycles for optimal search results in chemical information systems.
Area of Science:
- Chemical Information Science
- Computational Chemistry
- Cheminformatics
Background:
- Substructure search (SSS) is a core functionality in chemical information systems.
- Users often perform iterative SSS, modifying queries to refine retrieved chemical compound sets.
- This iterative process can be time-consuming and involve significant trial-and-error.
Purpose of the Study:
- To develop and evaluate heuristic extensions for SSS.
- To simplify the iterative modification of SSS queries.
- To provide users with intuitive feedback on query changes and their impact on search results.
Main Methods:
- Proposal of two heuristic extensions for SSS.
- Integration of additional information collection during query processing.
- Development of intuitive data visualization techniques for user feedback.
Main Results:
- The proposed heuristics effectively simplify iterative query modifications.
- Intuitive visualization aids users in understanding the impact of query changes.
- A significant reduction in trial-and-error cycles for achieving optimal search results was observed.
Conclusions:
- The heuristic extensions offer a simple yet effective enhancement to existing SSS implementations.
- These methods improve user efficiency in retrieving desired chemical structures.
- The approach is easily adaptable to current SSS systems.
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