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AIDD

Financiado

Cerrado

Advanced machine learning for Innovative Drug Discovery

"The dramatic increase in using of Artificial Intelligence (AI) and machine learning methods in different fields of science becomes an essential asset in the development of the chemical industry, including pharmaceutical, agro bio... ver más

31/03/2025

HMGU

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 18 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2025-03-31

MSCA-ITN-2020: Innovative Training Networks Objective:The Innovative Training Networks (ITN) aim to train a new generation of creative, entrepre...

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

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

18 Participantes

HMGU

390.21K€ | Lider

APTUS BIOTECH

179.76K€ | Participante

JANSSEN PHARMACEUTICA NV

640.80K€ | Participante

uni.lu

202.92K€ | Participante

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Duración del proyecto: 55 meses Fecha Inicio: 2020-08-24
Fecha Fin: 2025-03-31

Líder del proyecto

HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "The dramatic increase in using of Artificial Intelligence (AI) and machine learning methods in different fields of science becomes an essential asset in the development of the chemical industry, including pharmaceutical, agro biotech, and other chemical companies. However, the application of AI in these fields is not straightforward and requires excellent knowledge of chemistry. Thus, there is a strong need to train and prepare a new generation of scientists who have skills both in machine learning and in chemistry and can advance medicinal chemistry, which is the prime goal of the AIDD proposal. Research WPs include sixteen topics selected to cover the key innovative directions in machine learning in chemistry. Fellows employed will be supervised by academics who have excellent complementary expertise and contributed some of the fundamental AI algorithms which are used billions of times per day in the world, and leading EU Pharma companies who are in charge of new medicine and public health. All developed methods can be used individually but will also contribute to an integrated ""One Chemistry"" model that can predict outcomes ranging from different properties to molecule generation and synthesis. Training on various modalities allows the model to understand how to intertwine chemistry and biology to develop a new drug making its design robust and explainable. All partners agreed to make their software open source. It will boost the field and will provide the broadest possible dissemination of the results both to the academy and industry, including SMEs. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six Schools. The IP policy and commercial exploitation of the project results have the highest priority supported by intellectual property asset management organizations. Comprehensive public engagement activities will complement the dissemination of results to the scientific community."

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