Dr.Emb Appyter: A web platform for drug discovery using embedding vectors,Journal of Computational Chemistry

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Dr.Emb Appyter: A web platform for drug discovery using embedding vectors
Journal of Computational Chemistry ( IF 3.4 ) Pub Date : 2024-07-29 , DOI: 10.1002/jcc.27469
Songhyeon Kim ₁ , Hyunsu Bong ₁ , Minji Jeon _{1,

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Affiliation

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.

中文翻译：

Dr.Emb Appyter：使用包埋载体进行药物发现的 Web 平台

使用嵌入方法，具有相似性质的化合物将紧密位于潜在空间中，这些嵌入向量可用于根据化合物之间的距离找到具有相似性质的其他化合物。但是，它们通常需要计算资源和编程技能。在这里，我们开发了 Dr.Emb Appyter，这是一个用户友好的基于 Web 的化合物搜索平台，用于药物发现，没有任何技术障碍。它使用嵌入向量来识别与嵌入空间中的给定查询相似的化合物。Dr.Emb Appyter 提供各种类型的包埋方法，例如指纹图谱、SMILES 和基于转录响应的方法，并使用它们包埋了许多化合物。基于 Faiss 的搜索系统可以有效地在库中查找最接近的查询复合词。此外，Dr.Emb Appyter 还提供了有关主要化合物的信息;使用 3D 散点图、热图和 UpSet 图可视化结果;并使用药物组富集分析分析结果。Dr.Emb Appyter 可在 https://dremb.korea.ac.kr 免费获得。

更新日期：2024-07-29

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