Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing
<p>Computational drug repurposing is an efficient method to utilize existing knowledge for understanding and predicting their effect on neurological diseases. The ability of a molecule to cross the blood-brain barrier is a primary criteria for effective therapy. Thus, accurate predictions by e...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513562730299392 |
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| author | Mohammed Yusuf Ansari (16904523) |
| author2 | Vaisali Chandrasekar (16904526) Ajay Vikram Singh (204056) Sarada Prasad Dakua (14151789) |
| author2_role | author author author |
| author_facet | Mohammed Yusuf Ansari (16904523) Vaisali Chandrasekar (16904526) Ajay Vikram Singh (204056) Sarada Prasad Dakua (14151789) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammed Yusuf Ansari (16904523) Vaisali Chandrasekar (16904526) Ajay Vikram Singh (204056) Sarada Prasad Dakua (14151789) |
| dc.date.none.fl_str_mv | 2022-12-29T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3233110 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Re-Routing_Drugs_to_Blood_Brain_Barrier_A_Comprehensive_Analysis_of_Machine_Learning_Approaches_With_Fingerprint_Amalgamation_and_Data_Balancing/24056232 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Information and computing sciences Machine learning Drugs Fingerprint recognition Permeability Predictive models Compounds Machine learning Data models Blood brain barrier Drug permeability Drug repurposing Empirical study |
| dc.title.none.fl_str_mv | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Computational drug repurposing is an efficient method to utilize existing knowledge for understanding and predicting their effect on neurological diseases. The ability of a molecule to cross the blood-brain barrier is a primary criteria for effective therapy. Thus, accurate predictions by employing Machine learning models can effectively identify the drug candidates that could be repurposed for neurological conditions. This study comprehensively analyzes the performance of the well-known machine learning models on two different datasets to overcome dataset-related biases. We found that random forest and extratrees (i.e., tree-based ensembled models) have the highest accuracy with mol2vec fingerprint for BBB permeability prediction, attaining AUC_ROC of 0.9453 and 0.9601 on BBB and B3DB dataset, respectively. Additionally, we have analyzed the impact of the data balancing technique (i.e., SMOTE) to improve the specificity of the models. Finally, we have explored the impact of different fingerprint combinations on accuracy. By employing SMOTE and fingerprint combination, SVC attains the highest AUC_ROC of 0.9511 on BBB dataset. Finally, we used the best-performing models of the B3DB dataset to evaluate the BBB permeability for drugs intended to be used for repurposing. Model validation for repurposing predicted the non-passage for most antihypertensive drugs and passage for CYP17A1 cancer drugs.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3233110" target="_blank">https://dx.doi.org/10.1109/access.2022.3233110</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8f81934781ce64bc94b94ac52b50a2b1 |
| identifier_str_mv | 10.1109/access.2022.3233110 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056232 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data BalancingMohammed Yusuf Ansari (16904523)Vaisali Chandrasekar (16904526)Ajay Vikram Singh (204056)Sarada Prasad Dakua (14151789)Biomedical and clinical sciencesPharmacology and pharmaceutical sciencesInformation and computing sciencesMachine learningDrugsFingerprint recognitionPermeabilityPredictive modelsCompoundsMachine learningData modelsBlood brain barrierDrug permeabilityDrug repurposingEmpirical study<p>Computational drug repurposing is an efficient method to utilize existing knowledge for understanding and predicting their effect on neurological diseases. The ability of a molecule to cross the blood-brain barrier is a primary criteria for effective therapy. Thus, accurate predictions by employing Machine learning models can effectively identify the drug candidates that could be repurposed for neurological conditions. This study comprehensively analyzes the performance of the well-known machine learning models on two different datasets to overcome dataset-related biases. We found that random forest and extratrees (i.e., tree-based ensembled models) have the highest accuracy with mol2vec fingerprint for BBB permeability prediction, attaining AUC_ROC of 0.9453 and 0.9601 on BBB and B3DB dataset, respectively. Additionally, we have analyzed the impact of the data balancing technique (i.e., SMOTE) to improve the specificity of the models. Finally, we have explored the impact of different fingerprint combinations on accuracy. By employing SMOTE and fingerprint combination, SVC attains the highest AUC_ROC of 0.9511 on BBB dataset. Finally, we used the best-performing models of the B3DB dataset to evaluate the BBB permeability for drugs intended to be used for repurposing. Model validation for repurposing predicted the non-passage for most antihypertensive drugs and passage for CYP17A1 cancer drugs.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3233110" target="_blank">https://dx.doi.org/10.1109/access.2022.3233110</a></p>2022-12-29T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3233110https://figshare.com/articles/journal_contribution/Re-Routing_Drugs_to_Blood_Brain_Barrier_A_Comprehensive_Analysis_of_Machine_Learning_Approaches_With_Fingerprint_Amalgamation_and_Data_Balancing/24056232CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240562322022-12-29T00:00:00Z |
| spellingShingle | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing Mohammed Yusuf Ansari (16904523) Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Information and computing sciences Machine learning Drugs Fingerprint recognition Permeability Predictive models Compounds Machine learning Data models Blood brain barrier Drug permeability Drug repurposing Empirical study |
| status_str | publishedVersion |
| title | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing |
| title_full | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing |
| title_fullStr | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing |
| title_full_unstemmed | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing |
| title_short | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing |
| title_sort | Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing |
| topic | Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Information and computing sciences Machine learning Drugs Fingerprint recognition Permeability Predictive models Compounds Machine learning Data models Blood brain barrier Drug permeability Drug repurposing Empirical study |