AiChemist
Financiado
Explainable AI for Molecules - AiChemist
Optimising biological activity and physico-chemical properties, while minimising their toxicity, are objectives when developing new compounds in chemical industries. Advanced machine learning (AI) methods are indispensable to this...
Optimising biological activity and physico-chemical properties, while minimising their toxicity, are objectives when developing new compounds in chemical industries. Advanced machine learning (AI) methods are indispensable to this process. They are also increasingly used in environmental chemistry to identify compounds damaging to the environment and humans. Traditional machine learning (ML) methods provide reliable predictions though only for compounds similar to the training set, thus defining their applicability domain (AD). Emerging representation learning approaches can efficiently approximate the physical interactions of molecules with an accuracy comparable to physics-based methods in only fractions of time. Models based on these representations should have much larger AD due to pre-training on large chemical sets of theoretical values. Here we will develop and benchmark representation learning approaches, addressing their accuracy and ADs, using public and in-house data for endpoints ranging from chemical reactions to toxicity. While explainable AI (XAI) methods are actively developing in the ML community, there is a gap with their use in chemistry, i.e. there is a need to translate their results to the end users, chemists and regulatory bodies. Since the research program is tightly coupled with the target users - large companies, regulatory agencies and SMEs - it provides a clear path for technology transfer from academia to industry. AiChemist will provide structured training to its fellows through a combination of online courses and schools, strengthening European innovation capacity in the education of specialists in AI methods. The fellows will receive comprehensive training in transferable skills. The complementary expertise and strong commitment of the partners make this ambitious innovative research program realistic via the proper allocation of individual tasks and resources, as described below.
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Duración del proyecto: 47 meses
Fecha Inicio: 2023-09-01
Fecha Fin: 2027-08-31
Fecha Fin: 2027-08-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-09-01
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2022-DN-01-01:
MSCA Doctoral Networks 2022
Cerrada
hace 1 año
Líder del proyecto
HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUN...
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Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5