NovaMechanics Ltd, Nicosia, Cyprus.
Curr Med Chem. 2020;27(38):6523-6535. doi: 10.2174/0929867327666200727114410.
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.
药物发现和（纳米）材料设计项目需要对具有相应性质/活性的化合物的大型数据集进行计算机分析，以及检索和虚拟筛选更多的结构，以识别新的有效化合物。这是一个要求很高的过程，需要将各种工具与不同的输入和输出格式相结合。为了自动化所需的数据分析，我们已经开发了必要的工具，以促进各种重要任务的构建工作流程，这些工作流程将简化化学信息学数据的处理、处理和建模，并提供时间和成本有效的解决方案，可重现且更容易维护。因此，我们开发并展示了一个超过 25 个处理模块的工具包，Enalos+节点，该工具包为 KNIME 平台上对化学和生物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非常有用的操作。Enalos+节点具有用户友好的界面，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，以及用于稳健和预测模型开发和验证的工具。Enalos+节点可通过 KNIME 作为附加组件获得，并提供了在化学或纳米信息学框架中提取有用信息和分析实验和虚拟筛选结果的有价值工具。除此之外，为了：（i）通过 Enalos+KNIME 节点进行大数据分析，（ii）加速 Enalos+KNIME 节点内执行的时间要求高的计算，以及（iii）提出集成在 Enalos+工具箱中的新的时间和成本有效的节点，我们研究并验证了在 Enalos+节点内进行 GPU 计算的优势。演示数据集、教程和教育视频使用户能够轻松理解可用于数据计算机分析的节点的功能。
