Engineered nanoparticles (ENPs) are being extensively used in a great variety of applications with a pace that is increasingly growing. The evaluation of the biological effects of ENPs is of utmost importance and for that experimental and most recently computational methods have been suggested. In an effort to computationally explore available datasets that will lead to ready-to-use applications we have developed and validated a QNAR model for the prediction of the cellular uptake of nanoparticles in pancreatic cancer cells. Our insilico workflow was made available online through the Enalos InSilicoNano platform (http://enalos.insilicotox.com/QNAR_PaCa2/), a web service based solely on open source and freely available software that was developed with the purpose of making our model available to the interested user wishing to generate evidence on potential biological effects in the decision making framework. This web service will facilitate the computer aided nanoparticle design as it can serve as a source of activity prediction for novel nano-structures. To demonstrate the usefulness of the web service we have exploited the whole PubChem database within a virtual screening framework and then used the Enalos InSilicoNano platform to identify novel potent nanoparticles from a prioritized list of compounds.
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