结合分子图和原子图信息的多模态药物相互作用预测数据

数据集围绕网络协同制造工厂在智能制造方面的知识认知表示需求，主要面向制药知识提取、表示与融合研究，基于多模态学习和图神经网络的知识提取表示算法，主要记录了该算法在zhangDDI数据集上结合分子图和原子图信息的多模态药物相互作用预测的实验结果，数据量为18727条。

This dataset is developed to address the knowledge cognition and representation requirements of networked collaborative manufacturing plants in smart manufacturing. It primarily targets the research on pharmaceutical knowledge extraction, representation and fusion, and employs knowledge extraction and representation algorithms based on multimodal learning and graph neural networks. The dataset mainly records the experimental results of multimodal drug-drug interaction prediction that integrates molecular graph and atomic graph information, when the above-mentioned algorithm is validated on the ZhangDDI dataset. It contains a total of 18727 data entries.

搜集汇总

背景与挑战

背景概述

该数据集主要记录了基于多模态学习和图神经网络的药物相互作用预测实验结果，数据量为18727条，格式为csv和docx。数据集面向制药知识提取、表示与融合研究，结合了分子图和原子图信息，适用于人工智能和计算机神经网络领域的研究。