VenomPred 2.0

VenomPred 2.0 is the upgraded version of the VenomPred platform. The WebTool allows you, through an innovative machine learning consensus strategy, to evaluate both the toxicological profile of one or multiple small molecules and the features that strongly contribute to toxicological predictions in order to derive a structural toxicophore .


TOXICITY PREDICTIONS: you must fill in the form below by providingĀ  the SMILES strings of the molecules to be analyzed. In case multiple compounds are submitted, please separate their SMILES strings with a comma and make sure there are no empty spaces!* Alternatively, you can sketch the 2D structure of a molecule using the JMSE widget provided below and obtain the corresponding SMILES directly in the field below the widget by clicking on the “Add SMILES” button. After submitting your request, you will receive via email a pdf report and a csv file including the toxicological prediction results. You can find an example of the pdf report here.

TOXICOPHORE PREDICTIONS: you must fill in the form below by providing the SMILES string of the molecule predicted to be toxic and select the endpoint under consideration.** After submitting your request, you will receive via email a pdf report including the features importance analysis. You can find an example of the pdf report here.

*Only 100 molecules can be submitted in a single request for toxicity prediction.
** Only one molecule and one endpoint can be considered at a time for the potential toxicophore prediction.
If you need toxicity and toxicophore predictions for a larger number of molecules, please contact us at venompred@mmvsl.it.

Citation
If you use VenomPred in your research, please use the following citations:

  1. Di Stefano, M; Galati, S; Piazza, L; Granchi, C; Mancini, S; Fratini, F; Macchia, M; Poli, G; Tuccinardi, T.  VenomPred 2.0: a novel in silico platform for an extended and human interpretable toxicological profiling of small molecules.  J. Chem Inf. Model. 2023.
    DOI: https://doi.org/10.1021/acs.jcim.3c00692
  2. Galati, S.; Di Stefano, M.; Martinelli, E.; Macchia, M.; Martinelli, A.; Poli, G.; Tuccinardi, T. VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions. Int. J. Mol. Sci. 2022, 23, 2105. DOI:10.3390/ijms23042105

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