Abstract
In this chapter, we present a brief description of compound datasets and programs developed to serve chemoinformatics as well as, more specifically, nanoinformatics purposes. Emphasis has been placed on publicly available tools and particularly on KNIME (Konstanz Information Miner), the most widely used freely available platform for data processing and analysis. Among a multitude of studies that have demonstrated the usefulness of chemoinformatics tools to chemical and medicinal applications, herein we present indicative cases of five successful KNIME-based approaches. The first two studies include the risk assessment of nanoparticles (NPs) through the Enalos InSilicoNano platform, namely, (1) the prediction of the toxicity of iron oxide NPs and (2) the cellular uptake prediction of computationally designed NPs with the aid of reliable quantitative nanostructure–activity relationships (QNAR) models. The third case study deals with the recognition of organic substances as corrosion inhibitors though the construction of predictive quantitative structure–property relationships (QSPR) models with Enalos KNIME nodes. Finally, two more cases are briefly described and involve the accurate prediction of yellow fever inhibitors from the ChEMBL database and the de novo design of compounds with the reaction vectors methodology. The aim of this work is to familiarize the interested reader with the freely available in silico tools in KNIME analytics platform and to demonstrate their value and effectiveness toward specific computational applications.