Implementation of an AI-assisted fragment-generator in an open-source platform
We recently reported a deep learning model to facilitate fragment library design, which is critical for efficient hit identification. However, our model was implemented in Python. We have now created an implementation in the KNIME graphical pipelining environment which we hope will allow experimenta...
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| Veröffentlicht in: | MedChemComm 2022-10, Vol.13 (1), p.125-1211 |
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| creator | Bilsland, Alan E Pugliese, Angelo Bower, Justin |
| description | We recently reported a deep learning model to facilitate fragment library design, which is critical for efficient hit identification. However, our model was implemented in Python. We have now created an implementation in the KNIME graphical pipelining environment which we hope will allow experimentation by users with limited programming knowledge.
We report a deep learning model to facilitate fragment library design, which is critical for efficient hit identification, and an implementation in the KNIME graphical workflow environment which should facilitate a more codeless use. |
| doi_str_mv | 10.1039/d2md00152g |
| format | Article |
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| identifier | ISSN: 2632-8682 |
| ispartof | MedChemComm, 2022-10, Vol.13 (1), p.125-1211 |
| issn | 2632-8682 2040-2503 2632-8682 2040-2511 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2731427898 |
| source | Royal Society Of Chemistry Journals 2008-; PubMed Central |
| subjects | Chemistry Deep learning Experimentation |
| title | Implementation of an AI-assisted fragment-generator in an open-source platform |
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