面向化合物增强聚类与涌现性质的无监督分子嵌入学习
Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds
AI总结：
本文提出基于SMILES数据的无监督分子嵌入学习方法，结合图结构向量搜索与GPT3.5自然语言嵌入聚类，提升化合物检索与聚类效率，为分子性质预测中涌现性质的发展提供基础。
摘要：
通过机器学习对分子结构和性质进行详细分析，为药物发现与开发带来了巨大潜力。在模型中发展出用于理解分子的涌现性质，将借助一种新的计算工具拓宽开发视野。我们引入了多种基于SMILES数据检测和聚类化合物的方法。我们的第一种方法利用嵌入数据分析化合物的图结构，并采用向量搜索来满足阈值要求。结果产生了明显且集中的簇，该方法在查询和理解化合物方面取得了良好效果。我们还使用了存储在向量数据库中的自然语言描述嵌入，并结合GPT3.5，其表现优于基础模型。因此，我们提出了一种相似性搜索与聚类算法，以辅助搜索和交互分子，提高化学探索的效率，并为分子性质预测模型中涌现性质的未来发展奠定基础。
英文摘要：
The detailed analysis of molecular structures and properties holds great potential for drug development discovery through machine learning. Developing an emergent property in the model to understand molecules would broaden the horizons for development with a new computational tool. We introduce various methods to detect and cluster chemical compounds based on their SMILES data. Our first method, analyzing the graphical structures of chemical compounds using embedding data, employs vector search to meet our threshold value. The results yielded pronounced, concentrated clusters, and the method produced favorable results in querying and understanding the compounds. We also used natural language description embeddings stored in a vector database with GPT3.5, which outperforms the base model. Thus, we introduce a similarity search and clustering algorithm to aid in searching for and interacting with molecules, enhancing efficiency in chemical exploration and enabling future development of emergent properties in molecular property prediction models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
