基于矢量表示的分子图像特征化技术在药物分类中的应用
Augmenting Molecular Images with Vector Representations as a Featurization Technique for Drug Classification
摘要：
构建用于药物分类和生成的深度学习系统的关键步骤之一是选择分子的特征化。以前的特征化方法包括分子图像，二进制字符串，图形和SMILES字符串。本文提出了创建分子图像标题与二进制向量编码的信息不包含在或容易理解的分子图像单独。具体来说，我们使用Morgan指纹，它编码更高级别的结构信息，和MACCS密钥，它编码关于分子性质和结构的是或否问题。我们在Pande实验室发布的HIV数据集上测试了我们的方法，该数据集由41，127个分子组成，如果它们抑制HIV病毒，则标记为。我们的最终模型在HIV数据集上实现了最先进的AUC ROC，优于所有其他方法。此外，该模型的收敛速度明显快于大多数其他方法，所需的计算能力大大低于未增强的图像。
英文摘要：
One of the key steps in building deep learning systems for drug classification and generation is the choice of featurization for the molecules. Previous featurization methods have included molecular images, binary strings, graphs, and SMILES strings. This paper proposes the creation of molecular images captioned with binary vectors that encode information not contained in or easily understood from a molecular image alone. Specifically, we use Morgan fingerprints, which encode higher level structural information, and MACCS keys, which encode yes or no questions about a molecules properties and structure. We tested our method on the HIV dataset published by the Pande lab, which consists of 41,127 molecules labeled by if they inhibit the HIV virus. Our final model achieved a state of the art AUC ROC on the HIV dataset, outperforming all other methods. Moreover, the model converged significantly faster than most other methods, requiring dramatically less computational power than unaugmented images.
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。
