DTA Atlas: A massive-scale drug repurposing database
The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions. While modern in silico methods enhance scalability, an open database for projected dru...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Artificial Intelligence in the Life Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667318524000229 |
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| author | Madina Sultanova Elizaveta Vinogradova Alisher Amantay Ferdinand Molnár Siamac Fazli |
| author_facet | Madina Sultanova Elizaveta Vinogradova Alisher Amantay Ferdinand Molnár Siamac Fazli |
| author_sort | Madina Sultanova |
| collection | DOAJ |
| description | The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions. While modern in silico methods enhance scalability, an open database for projected drug-target interactions across the entire human proteome is still lacking. In this work, we introduce an open database of predicted drug-target interactions, termed DTA Atlas, covering the entire human proteome as well as a wide range of marketed drugs, resulting in over 220 million drug-target pairs. The database integrates 4 billion affinity predictions from advanced deep neural networks and offers a user-friendly web interface, enabling users to explore drug-target affinity predictions for the human proteome. To the best of our knowledge, DTA Atlas represents the first comprehensive collection of drug-target binding strength predictions. It is open-source and can serve as an important resource for drug development, drug repurposing, toxicity studies and more. |
| format | Article |
| id | doaj-art-08cc79105f91450aa2fc2b137585548c |
| institution | OA Journals |
| issn | 2667-3185 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Artificial Intelligence in the Life Sciences |
| spelling | doaj-art-08cc79105f91450aa2fc2b137585548c2025-08-20T01:56:34ZengElsevierArtificial Intelligence in the Life Sciences2667-31852024-12-01610011510.1016/j.ailsci.2024.100115DTA Atlas: A massive-scale drug repurposing databaseMadina Sultanova0Elizaveta Vinogradova1Alisher Amantay2Ferdinand Molnár3Siamac Fazli4Department of Computer Sciences, School of Digital Sciences and Engineering, Nazarbayev University, Kabanbay batyr 53, Z05H0P9, Astana, KazakhstanDepartment of Biology, School of Sciences and Humanities, Nazarbayev University, Kabanbay batyr 53, Z05H0P9, Astana, KazakhstanDepartment of Computer Sciences, School of Digital Sciences and Engineering, Nazarbayev University, Kabanbay batyr 53, Z05H0P9, Astana, KazakhstanDepartment of Biology, School of Sciences and Humanities, Nazarbayev University, Kabanbay batyr 53, Z05H0P9, Astana, KazakhstanDepartment of Computer Sciences, School of Digital Sciences and Engineering, Nazarbayev University, Kabanbay batyr 53, Z05H0P9, Astana, Kazakhstan; Corresponding author.The drug development process is costly and time-consuming. Repurposing existing approved drugs, an efficient and cost-effective strategy, involves assessing numerous drug-protein pairs to uncover new interactions. While modern in silico methods enhance scalability, an open database for projected drug-target interactions across the entire human proteome is still lacking. In this work, we introduce an open database of predicted drug-target interactions, termed DTA Atlas, covering the entire human proteome as well as a wide range of marketed drugs, resulting in over 220 million drug-target pairs. The database integrates 4 billion affinity predictions from advanced deep neural networks and offers a user-friendly web interface, enabling users to explore drug-target affinity predictions for the human proteome. To the best of our knowledge, DTA Atlas represents the first comprehensive collection of drug-target binding strength predictions. It is open-source and can serve as an important resource for drug development, drug repurposing, toxicity studies and more.http://www.sciencedirect.com/science/article/pii/S2667318524000229Drug discoveryDrug repurposing databaseMachine learningDrug target affinity predictionDrugbankUniprot |
| spellingShingle | Madina Sultanova Elizaveta Vinogradova Alisher Amantay Ferdinand Molnár Siamac Fazli DTA Atlas: A massive-scale drug repurposing database Artificial Intelligence in the Life Sciences Drug discovery Drug repurposing database Machine learning Drug target affinity prediction Drugbank Uniprot |
| title | DTA Atlas: A massive-scale drug repurposing database |
| title_full | DTA Atlas: A massive-scale drug repurposing database |
| title_fullStr | DTA Atlas: A massive-scale drug repurposing database |
| title_full_unstemmed | DTA Atlas: A massive-scale drug repurposing database |
| title_short | DTA Atlas: A massive-scale drug repurposing database |
| title_sort | dta atlas a massive scale drug repurposing database |
| topic | Drug discovery Drug repurposing database Machine learning Drug target affinity prediction Drugbank Uniprot |
| url | http://www.sciencedirect.com/science/article/pii/S2667318524000229 |
| work_keys_str_mv | AT madinasultanova dtaatlasamassivescaledrugrepurposingdatabase AT elizavetavinogradova dtaatlasamassivescaledrugrepurposingdatabase AT alisheramantay dtaatlasamassivescaledrugrepurposingdatabase AT ferdinandmolnar dtaatlasamassivescaledrugrepurposingdatabase AT siamacfazli dtaatlasamassivescaledrugrepurposingdatabase |