The objective of this Statement of Need (SON) was to develop advanced computational methods and validated models capable of predicting toxicological and physical properties related to novel energetic materials (EM) including binders, plasticizers, oxidizers, and fuels. Proposals that offered a systems approach to investigate compounds and synergistic effects of their formulations were preferred. Work proposed against this SON should have creatively leveraged EM datasets, public datasets, and insights from past property prediction work to create new representations and models for properties of interest to SERDP. Models meeting the requirements of this solicitation should have been capable of predicting [one or more of] the following toxicology parameters on energetic material datasets:

• Mutagenicity
• Acute and chronic toxicity using representative exposure routes
  • Oral, dermal, and inhalation
  • Aquatic, terrestrial and avian
• Irritation/sensitization
• Developmental/reproductive toxicity
• Carcinogenicity

Additionally, models were needed for prediction of physical properties relevant for assessing environmental fate and transport, including the following parameters:

• Aqueous solubility
• pH sensitivity
• Octanol-water partition coefficients
• Vapor pressure/Henry’s Law Constant
• Biodegradability in soil or fresh water
• Photolytic stability

Models with clearly defined domains of applicability and that were interpretable were preferred. Proposers could have also included experimental validation of models with energetic materials or structural analogs, and the creation or curation of experimental datasets for use with their proposed method. Proposals should have discussed validation metrics and methods for splitting of datasets that would be used for model development. It was also essential that new modeling techniques were user-friendly to the risk assessment community. Ideally, models should have been available for use by energetics researchers for incorporation into existing synthetic toolkits. Proposals based on updated line arregression based models (as commonly seen in quantitative structure-property relationship or multi-linear regression modeling efforts) were not considered.

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