Project description
Merging chemical structure and words to accelerate vital drug discoveries
In the battle against emerging pandemics, the sluggish pace of new drug development has become a glaring impediment. With an average cost of USD 2.6 billion and a timeline of up to 14 years, innovation is urgently needed. With this in mind, the EU-funded AIIPDD project is poised to redefine drug discovery. As AI technologies identify potential medicines among thousands of new molecules, the project stands out by swiftly sifting through an overwhelming sea of information. Over 135 million patents, 70 million journal articles and 4 billion web pages form a complex, unstructured corpus. Project coordinator Semantic Intelligence’s state-of-the-art AI engine, powered by natural language processing, machine learning and more, ushers in a new era.
Objective
Today, new drug development on average costs $2.6 billion and takes 10-14 years. But, for example, the global health emergency
over the spread of the deadly new coronavirus, is proving to be a crucial real-life test case for industry to aim dramatically reduce the
time it takes to create new vaccines and drugs to combat emerging pandemics. While AI technologies help to identify thousands of
new molecules that could be turned into potential medicines, it`s crucial to quickly establish the novelty of the most promising
candidates. Therefore, more than 135 million patents, 70 million journal articles, and 4 billion web pages are published creating a vast
corpus, Big Data of information that potentially needs to be searched to establish, for example, the novelty of a product, the state of
the art in the field of interest or the variety of components within complex compositions. Besides, 88% of the Big Data is unstructured
(in form of texts) and its volume increases by a staggering 62% annually, according to IDC. It leads to the main challenge: there is
simply way too much data for any human to read, analyze and understand.
Semantic Intelligence develops an AI-driven IP intelligence Engine enabling users to search, analyze and extract complex knowledge
on chemical-biological interactions in patents. Our proprietary State-of-the-art technology combines Natural Language Processing +
Machine Learning + Full linguistic data analysis + Chemical structure processing and Image Recognition, delivering novel ways by
integrating two languages: chemical structure and words, into a single semantic meaning. The target audience for this solution is
small and medium-sized life science companies, which currently have limitations in accessing expensive biochemistry databases. We
are aiming to disrupt the life science industry by delivering a low-cost solution to enable small & medium market players to create value by protecting their IP.
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 basic medicine pharmacology and pharmacy drug discovery
- natural sciences computer and information sciences data science big data
- natural sciences computer and information sciences data science natural language processing
- natural sciences computer and information sciences artificial intelligence computer vision image recognition
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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.3.2 - European innovation ecosystems
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-CSA - HORIZON Coordination and Support Actions
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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) HORIZON-EIE-2022-SCALEUP-02
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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.
LV-3135 Tukuma
Latvia
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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.