Dr.Emb Appyter : A web platform for drug discovery using embedding vectors
© 2024 The Author(s). Journal of Computational Chemistry published by Wiley Periodicals LLC.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 45(2024), 31 vom: 05. Dez., Seite 2659-2665 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article compound search embedding vectors in silico drug discovery |
| Zusammenfassung: | © 2024 The Author(s). Journal of Computational Chemistry published by Wiley Periodicals LLC. Using embedding methods, compounds with similar properties will be closely located in latent space, and these embedding vectors can be used to find other compounds with similar properties based on the distance between compounds. However, they often require computational resources and programming skills. Here we develop Dr.Emb Appyter, a user-friendly web-based chemical compound search platform for drug discovery without any technical barriers. It uses embedding vectors to identify compounds similar to a given query in the embedding space. Dr.Emb Appyter provides various types of embedding methods, such as fingerprinting, SMILES, and transcriptional response-based methods, and embeds numerous compounds using them. The Faiss-based search system efficiently finds the closest compounds of query in the library. Additionally, Dr.Emb Appyter offers information on the top compounds; visualizes the results with 3D scatter plots, heatmaps, and UpSet plots; and analyses the results using a drug-set enrichment analysis. Dr.Emb Appyter is freely available at https://dremb.korea.ac.kr |
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| Beschreibung: | Date Completed 10.10.2024 Date Revised 10.10.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.27469 |