Profiling and analysis of chemical compounds using pointwise mutual information
| Autor: | Čmelo, I., Voršilák, M., Svozil, D. |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Information theory Synthetic accessibility Pointwise mutual information Library and Information Sciences 01 natural sciences lcsh:Chemistry Set (abstract data type) 03 medical and health sciences Feature (machine learning) Physical and Theoretical Chemistry Structural key Mathematics lcsh:T58.5-58.64 lcsh:Information technology business.industry Methodology Pattern recognition chEMBL Computer Graphics and Computer-Aided Design 0104 chemical sciences Computer Science Applications Random forest Hashed fingerprint 010404 medicinal & biomolecular chemistry 030104 developmental biology lcsh:QD1-999 Artificial intelligence business DrugBank PubChem |
| Zdroj: | Journal of Cheminformatics Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-18 (2021) |
| ISSN: | 1758-2946 |
| DOI: | 10.1186/s13321-020-00483-y |
| Popis: | Pointwise mutual information (PMI) is a measure of association used in information theory. In this paper, PMI is used to characterize several publicly available databases (DrugBank, ChEMBL, PubChem and ZINC) in terms of association strength between compound structural features resulting in database PMI interrelation profiles. As structural features, substructure fragments obtained by coding individual compounds as MACCS, PubChemKey and ECFP fingerprints are used. The analysis of publicly available databases reveals, in accord with other studies, unusual properties of DrugBank compounds which further confirms the validity of PMI profiling approach. Z-standardized relative feature tightness (ZRFT), a PMI-derived measure that quantifies how well the given compound’s feature combinations fit these in a particular compound set, is applied for the analysis of compound synthetic accessibility (SA), as well as for the classification of compounds as easy (ES) and hard (HS) to synthesize. ZRFT value distributions are compared with these of SYBA and SAScore. The analysis of ZRFT values of structurally complex compounds in the SAVI database reveals oligopeptide structures that are mispredicted by SAScore as HS, while correctly predicted by ZRFT and SYBA as ES. Compared to SAScore, SYBA and random forest, ZRFT predictions are less accurate, though by a narrow margin (AccZRFT = 94.5%, AccSYBA = 98.8%, AccSAScore = 99.0%, AccRF = 97.3%). However, ZRFT ability to distinguish between ES and HS compounds is surprisingly high considering that while SYBA, SAScore and random forest are dedicated SA models, ZRFT is a generic measurement that merely quantifies the strength of interrelations between structural feature pairs. The results presented in the current work indicate that structural feature co-occurrence, quantified by PMI or ZRFT, contains a significant amount of information relevant to physico-chemical properties of organic compounds. |
| Databáze: | OpenAIRE |
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