Profiling and analysis of chemical compounds using pointwise mutual information

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378050%3A_____%2F21%3A00554542" target="_blank" >RIV/68378050:_____/21:00554542 - isvavai.cz</a>

Alternative codes found

RIV/60461373:22310/21:43923443

Result on the web

<a href="https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00483-y" target="_blank" >https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00483-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13321-020-00483-y" target="_blank" >10.1186/s13321-020-00483-y</a>

Result language

angličtina

Original language name

Profiling and analysis of chemical compounds using pointwise mutual information

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10608 - Biochemistry and molecular biology

Project

<a href="/en/project/LM2018130" target="_blank" >LM2018130: National Infrastructure for Chemical Biology</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Cheminformatics

ISSN

1758-2946

e-ISSN

1758-2946

Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

3

UT code for WoS article

000609194500002

EID of the result in the Scopus database

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