Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta
<p dir="ltr">Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively...
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2023
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| _version_ | 1864513527549526016 |
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| author | Vaisali Chandrasekar (16904526) |
| author2 | Mohammed Yusuf Ansari (16904523) Ajay Vikram Singh (204056) Shahab Uddin (154400) Kirthi S. Prabhu (17947853) Sagnika Dash (17947856) Souhaila Al Khodor (6807155) Annalisa Terranegra (3486953) Matteo Avella (17947859) Sarada Prasad Dakua (14151789) |
| author2_role | author author author author author author author author author |
| author_facet | Vaisali Chandrasekar (16904526) Mohammed Yusuf Ansari (16904523) Ajay Vikram Singh (204056) Shahab Uddin (154400) Kirthi S. Prabhu (17947853) Sagnika Dash (17947856) Souhaila Al Khodor (6807155) Annalisa Terranegra (3486953) Matteo Avella (17947859) Sarada Prasad Dakua (14151789) |
| author_role | author |
| dc.creator.none.fl_str_mv | Vaisali Chandrasekar (16904526) Mohammed Yusuf Ansari (16904523) Ajay Vikram Singh (204056) Shahab Uddin (154400) Kirthi S. Prabhu (17947853) Sagnika Dash (17947856) Souhaila Al Khodor (6807155) Annalisa Terranegra (3486953) Matteo Avella (17947859) Sarada Prasad Dakua (14151789) |
| dc.date.none.fl_str_mv | 2023-05-04T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3272987 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Investigating_the_Use_of_Machine_Learning_Models_to_Understand_the_Drugs_Permeability_Across_Placenta/25204247 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Drugs Permeability Predictive models Pregnancy Fingerprint recognition Machine learning Computational modeling Placenta barrier developmental toxicity |
| dc.title.none.fl_str_mv | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively cross the placental barrier and reach the fetus determines the drug’s toxicological effects on the fetus. In this regard, our study aims to predict the permeability of molecules across the placental barrier. Based on publicly available datasets, several machine learning models are comprehensively analysed across different fingerprints and toolkits to find the best suitable models. Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. K-nearest neighbour (KNN), standard vector classifier (SVC) and Multi-layer perceptron (MLP) are found to be the best-performing models with a prediction percentage of 82%, 86.4% and 90.8%, respectively. Different models are compared to predict the chosen set of drugs, drugs like Aliskiren, some insulin secretagogues and glucocorticoids are found to be negative while predicting the permeability.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3272987" target="_blank">https://dx.doi.org/10.1109/access.2023.3272987</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_42f7ec8d812528ad345d93d84a650dde |
| identifier_str_mv | 10.1109/access.2023.3272987 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25204247 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across PlacentaVaisali Chandrasekar (16904526)Mohammed Yusuf Ansari (16904523)Ajay Vikram Singh (204056)Shahab Uddin (154400)Kirthi S. Prabhu (17947853)Sagnika Dash (17947856)Souhaila Al Khodor (6807155)Annalisa Terranegra (3486953)Matteo Avella (17947859)Sarada Prasad Dakua (14151789)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringDrugsPermeabilityPredictive modelsPregnancyFingerprint recognitionMachine learningComputational modelingPlacenta barrierdevelopmental toxicity<p dir="ltr">Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively cross the placental barrier and reach the fetus determines the drug’s toxicological effects on the fetus. In this regard, our study aims to predict the permeability of molecules across the placental barrier. Based on publicly available datasets, several machine learning models are comprehensively analysed across different fingerprints and toolkits to find the best suitable models. Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. K-nearest neighbour (KNN), standard vector classifier (SVC) and Multi-layer perceptron (MLP) are found to be the best-performing models with a prediction percentage of 82%, 86.4% and 90.8%, respectively. Different models are compared to predict the chosen set of drugs, drugs like Aliskiren, some insulin secretagogues and glucocorticoids are found to be negative while predicting the permeability.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3272987" target="_blank">https://dx.doi.org/10.1109/access.2023.3272987</a></p>2023-05-04T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3272987https://figshare.com/articles/journal_contribution/Investigating_the_Use_of_Machine_Learning_Models_to_Understand_the_Drugs_Permeability_Across_Placenta/25204247CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252042472023-05-04T03:00:00Z |
| spellingShingle | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta Vaisali Chandrasekar (16904526) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Drugs Permeability Predictive models Pregnancy Fingerprint recognition Machine learning Computational modeling Placenta barrier developmental toxicity |
| status_str | publishedVersion |
| title | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta |
| title_full | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta |
| title_fullStr | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta |
| title_full_unstemmed | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta |
| title_short | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta |
| title_sort | Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Drugs Permeability Predictive models Pregnancy Fingerprint recognition Machine learning Computational modeling Placenta barrier developmental toxicity |