AI in 3D compound design
The success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do not account for the 3D structure of the target at...
| Main Authors: | , |
|---|---|
| Format: | Journal article |
| Language: | English |
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Elsevier
2022
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| _version_ | 1797108652649218048 |
|---|---|
| author | Hadfield, TE Deane, CM |
| author_facet | Hadfield, TE Deane, CM |
| author_sort | Hadfield, TE |
| collection | OXFORD |
| description | The success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do not account for the 3D structure of the target at all or struggle to capture meaningful spatial information from the target. In this Opinion, we highlight a range of recent structure-aware approaches which utilise deep learning for compound design and virtual screening. We discuss how such methods can be better integrated into existing drug discovery pipelines by facilitating the design of compounds which conform to a specified design hypothesis and by uncovering key protein-ligand interactions which can be used to aid molecule design. |
| first_indexed | 2024-03-07T07:31:42Z |
| format | Journal article |
| id | oxford-uuid:50703be8-3cc3-4727-af7c-b5f20fd8b777 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:31:42Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:50703be8-3cc3-4727-af7c-b5f20fd8b7772023-01-31T11:08:35ZAI in 3D compound designJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:50703be8-3cc3-4727-af7c-b5f20fd8b777EnglishSymplectic ElementsElsevier2022Hadfield, TEDeane, CMThe success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do not account for the 3D structure of the target at all or struggle to capture meaningful spatial information from the target. In this Opinion, we highlight a range of recent structure-aware approaches which utilise deep learning for compound design and virtual screening. We discuss how such methods can be better integrated into existing drug discovery pipelines by facilitating the design of compounds which conform to a specified design hypothesis and by uncovering key protein-ligand interactions which can be used to aid molecule design. |
| spellingShingle | Hadfield, TE Deane, CM AI in 3D compound design |
| title | AI in 3D compound design |
| title_full | AI in 3D compound design |
| title_fullStr | AI in 3D compound design |
| title_full_unstemmed | AI in 3D compound design |
| title_short | AI in 3D compound design |
| title_sort | ai in 3d compound design |
| work_keys_str_mv | AT hadfieldte aiin3dcompounddesign AT deanecm aiin3dcompounddesign |