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QUANTUM-TOX - Revolutionizing Computational Toxicology with Electronic Structure Descriptors and Artificial Intelligence

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QUANTUM-TOX - Revolutionizing Computational Toxicology with Electronic Structure Descriptors and Artificial Intelligence Toxicology is at a crossroads. With ever more drugs going to market and more chemicals having an environmental impact, the need for fast, cheap and accurate technologies to assess toxic effects is pressing. Computational toxicology provides an array of tools and methods for toxicity prediction only using computer approaches. Conceptually, computational toxicology has significant advantages since testing is fast and cheaper than in vitro. However, currently computational toxicology has severe limitations. Predictions typically use Quantitative Structure-Activity Relationship (QSAR) models that rely on large sets of molecular descriptors. This causes severe problems since the methodologies cannot assess chemicals different than the ones used to develop the QSAR models, and when that is possible, the very large number of descriptors limits understandability. Therefor

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recordType

award

status

SIGNED

region

EU

value

1994770

unit

EUR