Optibrium partners with TalTech to advance sustainable drug discovery

The research will focus on developing faster and more accurate methods for predicting drug metabolism

4 Jul 2025

Industry news

Optibrium has announced a partnership with TalTech (Tallinn University of Technology) to co-supervise an EU-funded PhD position as part of the INNOCHEMBIO programme. This research project will focus on developing more accurate metabolism models that will reduce the reliance on costly laboratory experiments while improving speed, efficiency and sustainability in drug discovery.

The PhD research will address a critical need in modern drug development. To overcome the limited amount of available experimental data, existing predictive models use hybrid approaches that combine machine learning with physics-based methods.

The most accurate physics-based methods are too computationally expensive to apply routinely, so it is necessary to use faster but less-accurate approximations. This project aims to replace those methods with more efficient and accurate machine learning interatomic potentials (MLIPs).

These improved models will enable pharmaceutical teams to better identify risks associated with drug metabolism and guide the design of optimal compounds for synthesis, saving time and reducing costs while aligning their efforts with green chemistry principles.

This research will specifically target MLIPs for drug-like molecules and metabolism mediated by Cytochrome P450 enzymes, a family responsible for the metabolism of 70–80% of all small molecule drugs.

The developed models will be integrated directly into Optibrium's StarDrop™ platform, enhancing the company's predictive capabilities for real-world drug discovery applications. This integration ensures immediate practical relevance and positions Optibrium to deliver even greater value to pharmaceutical companies seeking efficient and sustainable drug development solutions.

The collaboration forms part of the Marie Sklodowska-Curie Actions (MSCA) COFUND programme INNOCHEMBIO, funded by the European Commission in 2025 (Grant agreement 101217295). This initiative supports the EU's Green Deal objectives by training future experts in sustainable chemistry and biotechnology. INNOCHEMBIO will co-finance 15 PhD positions in total, with the first call for applications now open.

Mario Öeren, Principal Scientist, Optibrium, and Senior Researcher, TalTech, said, “Industry-academia collaborations like this provide PhD candidates with unique insights into real-world challenges whilst ensuring their research has immediate practical impact. The models we develop will directly improve our ability to support pharmaceutical researchers in making more informed decisions earlier in the drug discovery process.”

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Artificial Intelligence / Machine LearningArtificial intelligence (AI) and machine learning (ML) are transformative technologies used to analyze complex data, identify patterns, and make data-driven predictions across diverse scientific fields. Automate the analysis of large or complex data sets using AI algorithms and leverage machine learning models to improve diagnostics, accelerate drug discovery, and refine experimental design. Discover the best AI/ML software, platforms, and analytical tools in our peer-reviewed product directory: compare features, read customer reviews, and request pricing directly from manufacturers.Small MoleculesDrug MetabolismDrug metabolism is the enzymatic conversion within the body of pharmaceutical substances into other chemical compounds. The primary site for drug metabolism is the liver.SustainabilitySustainability in research and industry focuses on reducing environmental impact, conserving resources, and promoting long-term ecological balance. Innovations in sustainable practices include renewable energy, waste reduction, and eco-friendly materials. Explore more sustainabile solutions in our peer-reviewed product directory; compare products, check reviews, and get pricing directly from manufacturers.
Optibrium partners with TalTech to advance sustainable drug discovery