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Explainable AI for Molecules - AiChemist

Project description

Advancing AI for safer chemical development

In the chemical industry, developing compounds requires balancing biological activity, physico-chemical properties, and minimal toxicity. Advanced machine learning is crucial in identifying harmful environmental and human impacts. However, traditional machine learning methods are limited to predicting compounds similar to their training sets, narrowing their applicability domain (AD). Emerging representation learning approaches offer the potential for broader AD by efficiently modelling molecular interactions, matching the accuracy of physics-based methods in a fraction of the time. Supported by the Marie Skłodowska-Curie Actions programme, the AiChemist project will use both public and in-house data to enhance predictions across chemical reactions and toxicity endpoints. It also provides structured training to foster European innovation in AI methods for chemistry.

Objective

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.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-DN-ID - HORIZON TMA MSCA Doctoral Networks - Industrial Doctorates

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Call for proposal

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(opens in new window) HORIZON-MSCA-2022-DN-01

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Coordinator

HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 217 116,00
Address
INGOLSTADTER LANDSTRASSE 1
85764 Neuherberg
Germany

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Region
Bayern Oberbayern München, Landkreis
Activity type
Research Organisations
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Total cost

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Participants (14)

Partners (12)

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