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.
Diagnosing surface versus bulk reactivity for molecular catalysis within metal–organic frameworks using a quantitative kinetic model
