Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives
Abstract This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrat...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06991-6 |
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| author | Nouman Ali Nimra Hanif Hassan Abbas Khan Muhammad Abdullah Waseem Afshan Saeed Sadia Zakir Abeeha Khan Mejerrah Aamir Adeeba Ali Aamir Ali Amna Saleem |
| author_facet | Nouman Ali Nimra Hanif Hassan Abbas Khan Muhammad Abdullah Waseem Afshan Saeed Sadia Zakir Abeeha Khan Mejerrah Aamir Adeeba Ali Aamir Ali Amna Saleem |
| author_sort | Nouman Ali |
| collection | DOAJ |
| description | Abstract This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.' |
| format | Article |
| id | doaj-art-a48dae6e6e3c45af9d6b2fa24aa5c34c |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-a48dae6e6e3c45af9d6b2fa24aa5c34c2025-08-20T02:33:26ZengSpringerDiscover Applied Sciences3004-92612025-05-017611610.1007/s42452-025-06991-6Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectivesNouman Ali0Nimra Hanif1Hassan Abbas Khan2Muhammad Abdullah Waseem3Afshan Saeed4Sadia Zakir5Abeeha Khan6Mejerrah Aamir7Adeeba Ali8Aamir Ali9Amna Saleem10Department of Biotechnology, Faculty of Science and Technology, University of Central PunjabDepartment of Biotechnology, Faculty of Science and Technology, University of Central PunjabDepartment of Biotechnology, Faculty of Science and Technology, University of Central PunjabDepartment of Biotechnology, Faculty of Science and Technology, University of Central PunjabDepartment of Applied and Basic Chemistry, Faculty of Science and Technology, University of Central PunjabDepartment of Biotechnology, Faculty of Science and Technology, University of Central PunjabDepartment of Food Sciences, Faculty of Science and Technology, University of Central PunjabDepartment of Microbiology, Faculty of Science and Technology, University of Central PunjabDepartment of Biotechnology, Faculty of Science and Technology, University of Central PunjabDepartment of Botany, Faculty of Science and Technology, University of Education Township LahoreDepartment of Biotechnology, Faculty of Science and Technology, University of Central PunjabAbstract This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'https://doi.org/10.1007/s42452-025-06991-6Deep learning technologyArtificial intelligenceMedicineTarget identificationLead optimizationGenerative models |
| spellingShingle | Nouman Ali Nimra Hanif Hassan Abbas Khan Muhammad Abdullah Waseem Afshan Saeed Sadia Zakir Abeeha Khan Mejerrah Aamir Adeeba Ali Aamir Ali Amna Saleem Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives Discover Applied Sciences Deep learning technology Artificial intelligence Medicine Target identification Lead optimization Generative models |
| title | Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives |
| title_full | Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives |
| title_fullStr | Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives |
| title_full_unstemmed | Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives |
| title_short | Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives |
| title_sort | deep learning and artificial intelligence for drug discovery application challenge and future perspectives |
| topic | Deep learning technology Artificial intelligence Medicine Target identification Lead optimization Generative models |
| url | https://doi.org/10.1007/s42452-025-06991-6 |
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