Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

药物发现以及（纳米）材料设计项目要求对具有相应性质/活性的化合物的大型数据集进行计算机分析，并需要对更多结构进行检索和虚拟筛选，以识别出新的有效药物。这是一个苛刻的过程，必须将各种工具与不同的输入和输出格式结合使用。为了自动化所需的数据分析，我们开发了必要的工具来促进各种重要任务来构建工作流，这些工作流将简化化学信息学数据的处理，处理和建模，并提供可重复且易于维护的省时，低成本的解决方案。因此，我们开发并展示了一个工具箱，其中包含25个以上的处理模块，Enalos +节点，为对化学和生物学数据的纳米信息学和化学信息学分析感兴趣的用户提供了KNIME平台内非常有用的操作。通过用户友好的界面，Enalos +节点提供了广泛的重要功能，包括从大型可用数据库中进行数据挖掘和检索，以及用于健壮和预测性模型开发和验证的工具。Enalos +节点可通过KNIME作为附件使用，并提供了宝贵的工具，可用于提取有用的信息以及在化学或纳米信息学框架中分析实验和虚拟筛选结果。最重要的是，为了：（i）通过Enalos + KNIME节点进行大数据分析，（ii）加快Enalos + KNIME节点中执行的时间要求计算，并且（iii）提出集成在Enalos +工具箱中的新的时间和成本有效节点，我们已经研究并验证了Enalos +节点中GPU计算的优势。演示数据集，教程和教学视频使用户可以轻松理解可用于计算机数据分析的节点功能。