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QUANTUM-TOX

Financiado

QUANTUM-TOX - Revolutionizing Computational Toxicology with Electronic Structure...

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 toxicolog... ver más

31/01/2028

IRFMN

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

ISTITUTO DI RICERCHE FARMACOLOGICHE MARIO NEG... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 7 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-11-06

HORIZON-EIC-2023-PATHFINDEROPEN-01-01: EIC Pathfinder Open Scope:

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

7 Participantes

IRFMN

394.93K€ | Lider

POSITIVAZIMUTELDA

283.13K€ | Participante

UNIVERSITY OF MANCHESTER

N.D. | Participante

FASTCOMPCHEM LDA

972.47K€ | Participante

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Duración del proyecto: 50 meses Fecha Inicio: 2023-11-06
Fecha Fin: 2028-01-31

Líder del proyecto

ISTITUTO DI RICERCHE FARMACOLOGICHE MARIO NEG... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto 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. Therefore, new methodologies are needed to address those shortcomings. This project will develop a new type of descriptor, totally based on quantum mechanics that can cover the whole chemical space and relies on a small number of parameters that are easily interpretable. Starting with meaningful chemical perturbations, that extract the behaviour of the chemicals in assumed mechanisms of toxic action, the approach will develop specific Electronic SIGNatures (ESigns). ESigns are mathematical invariants that map the results of the quantum chemical calculations. Using Artificial Intelligence, the ESigns will relate to toxicity. The new approach introduces a momentous change in computational toxicology. It can cover the whole chemical space, since we are abandoning predictions based on molecular structures, uses fewer parameters, and can be related to the new trends in toxicology regarding use of pathways information. In fact, it is a powerful tool to allow accurate toxicology predictions solely on the basis of biochemical and chemical insight.

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