Profiling and analysis of chemical compounds using pointwise mutual information

0554542 - ÚMG 2022 RIV GB eng J - Journal Article
Čmelo, I. - Voršilák, Milan - Svozil, Daniel
Profiling and analysis of chemical compounds using pointwise mutual information.
Journal of Cheminformatics. Roč. 13, č. 1 (2021), č. článku 3. ISSN 1758-2946. E-ISSN 1758-2946
R&D Projects: GA MŠMT(CZ) LM2018130
Research Infrastructure: CZ-OPENSCREEN III - 90130
Institutional support: RVO:68378050
Keywords : Hashed fingerprint * Structural key * Information theory * Pointwise mutual information * Synthetic accessibility
OECD category: Biochemistry and molecular biology
Impact factor: 8.489, year: 2021
Method of publishing: Open access
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00483-y

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 (Acc(ZRFT) = 94.5%, Acc(SYBA) = 98.8%, Acc(SAScore) = 99.0%, Acc(RF) = 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.
Permanent Link: http://hdl.handle.net/11104/0329252