Project description
Advanced deep learning to accelerate drug discovery and toxicology
Traditional drug testing methods rely on animal models, which are time-consuming, costly, and ethically challenging. The ERC-funded EmbryoNet-AI project aims to improve drug discovery and toxicology testing by automating the phenotypic analysis of embryos and organoids using advanced deep learning. This platform will enable faster, more accurate assessments of compound effects, reducing dependence on animal models. With a focus on development and commercialisation, the project will refine AI models, create a user-friendly web interface, and collaborate with partners. By enhancing traditional testing methods, EmbryoNet-AI intends to accelerate drug discovery, lower costs, and support sustainable research in developmental biology and pharmacology.
Objective
The EmbryoNet-AI project seeks to transform drug discovery and toxicology testing by integrating advanced deep learning technologies to automate phenotypic analysis of embryos and organoids. Traditional drug testing methods rely heavily on animal models, which are time-consuming, costly, and often ethically problematic. EmbryoNet-AI offers a faster, more accurate, and comprehensive solution for evaluating the effects of compounds on biological development, thus significantly enhancing the efficiency of early-stage drug screening. The core innovation is EmbryoNet-AI's ability to analyze complex biological data with precision, providing insights into drug mechanisms that are not only quicker but also more reliable than current methods. This will ultimately reduce the need for animal models in drug testing, addressing both ethical and logistical concerns. The EmbryoNet-AI platform is poised to fill a critical gap in the pharmaceutical and biotech industries, offering a scalable, non-invasive approach that can be easily integrated into existing workflows. With the support of the ERC Proof of Concept grant, we will focus on the further development, validation, and commercialization of the EmbryoNet-AI platform. The project will involve refining our AI models through rigorous testing, building a prototype web interface for user interaction, and collaborating with industrial and academic partners to ensure the platforms practical utility. Additionally, we will explore intellectual property strategies and assess market readiness, paving the way for wide-spread adoption. By addressing the limitations of traditional phenotyping methods, EmbryoNet-AI aims to accelerate drug discovery, reduce costs, and promote more sustainable research practices in developmental biology and pharmacology. Through ERC funding, we aim to establish EmbryoNet-AI as a ground-breaking tool that will impact both the scientific community and the pharmaceutical industry.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- medical and health sciences clinical medicine embryology
- medical and health sciences basic medicine toxicology
- medical and health sciences basic medicine pharmacology and pharmacy
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-POC
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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.
78464 Konstanz
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.