APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions
Abstract More sophisticated representations of compounds attempt to incorporate not only information on the structure and physicochemical properties of molecules, but also knowledge about their biological traits, leading to the so-called bioactivity profile. The bioactive profiling of air pollutants...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00961-1 |
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| author | Eva Viesi Ugo Perricone Patrick Aloy Rosalba Giugno |
| author_facet | Eva Viesi Ugo Perricone Patrick Aloy Rosalba Giugno |
| author_sort | Eva Viesi |
| collection | DOAJ |
| description | Abstract More sophisticated representations of compounds attempt to incorporate not only information on the structure and physicochemical properties of molecules, but also knowledge about their biological traits, leading to the so-called bioactivity profile. The bioactive profiling of air pollutants is challenging and crucial, as their biological activity and toxicological effects have not been deeply investigated yet, and further exploration could shed light on the impact of air pollution on complex disorders. Therefore, a biological signature that simultaneously captures the chemistry and the biology of small molecules may be beneficial in predicting the behaviour of such ligands towards a protein target. Moreover, the interactivity between biological entities can be represented through combined feature vectors that can be given as input to a machine learning (ML) model to capture the underlying interaction. To this end, we propose a chemogenomic approach, called Air Pollutant Bioactivity (APBIO), which integrates compound bioactivity signatures and target sequence descriptors to train ML classifiers subsequently used to predict potential compound-target interactions (CTIs). We report the performances of the proposed methodology and, via external validation sets, demonstrate its outperformance compared to existing molecular representations in terms of model generalizability. We have also developed a publicly available Streamlit application for APBIO at ap-bio.streamlit.app, allowing users to predict associations between investigated compounds and protein targets. Scientific contribution We derived ex novo bioactivity signatures for air pollutant molecules to capture their biological behaviour and associations with protein targets. The proposed chemogenomic methodology enables the prediction of novel CTIs for known or similar compounds and targets through well-established and efficient ML models, deepening our insight into the molecular interactions and mechanisms that may have a deleterious impact on human biological systems. |
| format | Article |
| id | doaj-art-15c45b6bf8014a4dbd2cbbdb1d3d2c49 |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-15c45b6bf8014a4dbd2cbbdb1d3d2c492025-02-02T12:40:15ZengBMCJournal of Cheminformatics1758-29462025-01-0117111610.1186/s13321-025-00961-1APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactionsEva Viesi0Ugo Perricone1Patrick Aloy2Rosalba Giugno3Department of Computer Science, University of VeronaMolecular Informatics Unit, Ri.MED FoundationInstitute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and TechnologyDepartment of Computer Science, University of VeronaAbstract More sophisticated representations of compounds attempt to incorporate not only information on the structure and physicochemical properties of molecules, but also knowledge about their biological traits, leading to the so-called bioactivity profile. The bioactive profiling of air pollutants is challenging and crucial, as their biological activity and toxicological effects have not been deeply investigated yet, and further exploration could shed light on the impact of air pollution on complex disorders. Therefore, a biological signature that simultaneously captures the chemistry and the biology of small molecules may be beneficial in predicting the behaviour of such ligands towards a protein target. Moreover, the interactivity between biological entities can be represented through combined feature vectors that can be given as input to a machine learning (ML) model to capture the underlying interaction. To this end, we propose a chemogenomic approach, called Air Pollutant Bioactivity (APBIO), which integrates compound bioactivity signatures and target sequence descriptors to train ML classifiers subsequently used to predict potential compound-target interactions (CTIs). We report the performances of the proposed methodology and, via external validation sets, demonstrate its outperformance compared to existing molecular representations in terms of model generalizability. We have also developed a publicly available Streamlit application for APBIO at ap-bio.streamlit.app, allowing users to predict associations between investigated compounds and protein targets. Scientific contribution We derived ex novo bioactivity signatures for air pollutant molecules to capture their biological behaviour and associations with protein targets. The proposed chemogenomic methodology enables the prediction of novel CTIs for known or similar compounds and targets through well-established and efficient ML models, deepening our insight into the molecular interactions and mechanisms that may have a deleterious impact on human biological systems.https://doi.org/10.1186/s13321-025-00961-1Chemogenomic approachAir pollutants’ bioactivityCompound-target interaction prediction |
| spellingShingle | Eva Viesi Ugo Perricone Patrick Aloy Rosalba Giugno APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions Journal of Cheminformatics Chemogenomic approach Air pollutants’ bioactivity Compound-target interaction prediction |
| title | APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions |
| title_full | APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions |
| title_fullStr | APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions |
| title_full_unstemmed | APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions |
| title_short | APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions |
| title_sort | apbio bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions |
| topic | Chemogenomic approach Air pollutants’ bioactivity Compound-target interaction prediction |
| url | https://doi.org/10.1186/s13321-025-00961-1 |
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