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. 2023 Jul 14:2023:baad046.
doi: 10.1093/database/baad046.

APDB: a database on air pollutant characterization and similarity prediction

Affiliations

APDB: a database on air pollutant characterization and similarity prediction

Eva Viesi et al. Database (Oxford). .

Abstract

The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood-brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
An APDB overview. (a) The online resources, the collected molecular annotations, targets and bioassays, the computed molecular descriptors and similarities, the data statistics and the download functionalities. (b) The IDF weights distribution of fingerprints bits and counts and the first five principal components of molecular descriptors and quantum properties.
Figure 2.
Figure 2.
Data preprocessing and similarity analysis. (a) From data collection and storage to data preprocessing and similarity calculation. (b) k-means clustering of similarity spaces. (c) The panel of similar molecules to a query molecule (default threshold is 0.95; if no similar molecules are found, those with the closest threshold are returned).
Figure 3.
Figure 3.
The first two similar molecules for each case study molecule with a similarity threshold of >0.96 in the corresponding space.

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