The drug discovery process is complex, requiring substantial financial investment and many years of work to bring a new drug to market. One way to shorten the time and reduce the costs of this process is through artificial intelligence. This approach can reduce the number of failures in clinical trials and facilitate the exploration of the chemical compound space.
In this project, we propose utilizing tools based on knowledge retrieval and interactive, interpretable machine learning to increase the efficiency of the drug discovery process. The developed tool will be able to analyze images of cellular events, such as those obtained in the CellPainting (CP) protocol, and identify potential small-molecule drugs that can induce specific cellular events. It will make drug discovery, such as hit identification, virtual screening, and drug repurposing, faster and more cost-effective.
We will collaborate with Matthias Zeppelzaur, an expert in knowledge retrieval, and Ardigen SA, a global leader in AI-based services for pharmaceutical companies. Ardigen also develops phenAID software, which enables advanced analysis of images obtained using the CP protocol.
The execution of the project occurred under the FIRST TEAM FENG programme of the Foundation for Polish Science, with co-financing from the European Union through the European Regional Development Fund. The project received funding amounting to PLN 3,900,600.
PhD Student in Information Retrieval (December 16, 2024)
PhD Student in Cheminformatics (December 16, 2024)
MSc Student in Explainable Artificial Intelligence (December 16, 2024)
MSc Student in Cognitive Science (December 16, 2024)
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Postdoc in Information Retrieval (February 2, 2025)
Postdoc in Cheminformatics - 2 positions (February 2, 2025)