VEGA-QSAR: AI Inside a Platform for Predictive Toxicology

@inproceedings{Benfenati2013VEGAQSARAI,
  title={VEGA-QSAR: AI Inside a Platform for Predictive Toxicology},
  author={Emilio Benfenati and Alberto Manganaro and Giuseppina C. Gini},
  booktitle={PAI@AI*IA},
  year={2013}
}
Computer simulation and predictive models are widely used in engineering, much less considered in life sciences. We present an initiative aimed to establish a dialogue within the community of scientists, regulators, industry representatives, offering a platform which combines the predictive capability of computer models, with some explanation tools, which may be convincing and helpful for human users to derive a conclusion. The resulting system covers a large set of toxicological endpoints. 

References

Publications referenced by this paper.
Showing 1-10 of 11 references

The acceptance of in silico models for REACH: Requirements, barriers, and perspectives

  • E Benfenati
  • Chemistry Central Journal,
  • 2011
1 Excerpt

Validation of the models

  • E. Benfenati, R. CrètienJ., G. Gini, N. Piclin, M. Pintore, A. Roncaglioni
  • Quantitative Structure - Activity Relationships…
  • 2007

Validation of the models. In Quantitative Structure-Activity Relationships (QSAR) for Pesticide Regulatory Purposes

  • E. Benfenati, J. R. Crètien, G. Gini, N. Piclin, M. Pintore, A. Roncaglioni
  • 2007
1 Excerpt

Predictive Toxicology of Chemicals: Experiences and Impact of Artificial Intelligence Tools

  • G Gini, A. Katritzky
  • AAAI Spring Symposium on Predictive Toxicology,
  • 1999
1 Excerpt

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