Profiling and analysis of chemical compounds using pointwise mutual information
I Čmelo1, M Voršilák1,2, D Svozil3,4
1CZ-OPENSCREEN National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.
Journal of Cheminformatics
|January 11, 2021
Summary
Pointwise mutual information (PMI) profiling characterizes compound databases by analyzing structural feature associations. A derived measure, ZRFT, effectively classifies compounds as easy or hard to synthesize, offering insights into chemical properties.
Area of Science:
- Computational chemistry and cheminformatics
- Information theory applications in drug discovery
Background:
- Databases like DrugBank, ChEMBL, PubChem, and ZINC contain vast chemical information.
- Characterizing compound databases and predicting synthetic accessibility (SA) are crucial for drug development.
- Existing methods for SA prediction have limitations, particularly with complex structures.
Purpose of the Study:
- To characterize publicly available compound databases using Pointwise Mutual Information (PMI).
- To introduce and apply a PMI-derived measure, Z-standardized relative feature tightness (ZRFT), for assessing compound synthetic accessibility.
- To compare ZRFT's performance against established SA prediction tools.
Main Methods:
- Compounds were represented using MACCS, PubChemKey, and ECFP fingerprints to define structural features.
- Pointwise Mutual Information (PMI) was computed to establish association strengths between structural features within databases.
- Z-standardized relative feature tightness (ZRFT) was calculated to quantify feature combination fit and predict synthetic accessibility (easy/hard to synthesize).
Main Results:
- PMI profiling revealed distinct interrelation profiles for different compound databases, highlighting unusual properties of DrugBank compounds.
- ZRFT successfully classified compounds as easy (ES) or hard (HS) to synthesize, showing comparable performance to dedicated SA models.
- ZRFT correctly identified complex oligopeptide structures as ES, a task where SAScore misclassified them as HS.
Conclusions:
- Structural feature co-occurrence, quantified by PMI and ZRFT, contains significant information relevant to the physico-chemical properties of organic compounds.
- ZRFT offers a valuable, albeit less accurate than some dedicated models, generic approach for synthetic accessibility prediction.
- PMI-based profiling provides a robust method for characterizing chemical databases and understanding compound properties.
Keywords:
Hashed fingerprintInformation theoryPointwise mutual informationStructural keySynthetic accessibilityMore Related Videos
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