For the first time, Biocentral makes complex AI models for biomedical research centrally accessible and easy to use – without the need for programming skills, but with maximum impact for science and teaching.
Two trends are converging in biomedical research: the volume of available data is growing rapidly. At the same time, the existing technologies and tools for analysing this data are often too fragmented, technically complex or of limited use. As a result, researchers spend a great deal of time searching for models, resolving technical dependencies or manually adapting software to their needs.
Particularly in the field of AI-supported analysis of biomedical data, whilst there are many powerful models, these are scattered across various repositories, written in different programming languages and often cannot be maintained. The result: enormous potential that remains untapped, for example, in the rapid development of medicinal compounds.
Together with the Technical University of Munich, iteratec has specifically addressed this problem and further developed the existing open platform Biocentral. The aim was to create a scalable, intuitive and future-proof solution that makes biomedical data analysis more accessible and powerful.
The joint team of researchers from the Technical University of Munich and AI experts from iteratec developed Biocentral as an open platform on which powerful AI models for biomedical research are made easily accessible. One example is the SETH model, which can quickly identify disordered regions in protein structures and is relevant, amongst other things, to Alzheimer’s research. At the heart of the platform are state-of-the-art deep learning methods, in particular so-called protein-language models. These models – inspired by developments such as AlphaFold, which was awarded the Nobel Prize in 2024 – enable a new level of quality in the prediction and analysis of biological structures.
Biocentral brings these highly specialised models together in a user-friendly environment that can be used in both research and teaching. The aim was not merely technical integration, but a genuine simplification of access to AI, without the need for programming skills and without hidden dependencies.
A key element of the implementation was the standardisation of the AI models: originally developed using PyTorch – a machine learning library that is costly to maintain in production environments – they were systematically migrated by the iteratec team to the open ONNX format. This not only ensures independence from proprietary frameworks, but also long-term maintainability and interoperability.
This step required extensive technical work – in some cases, PyTorch functionalities had to be rewritten to achieve ONNX compatibility. Together with the Technical University of Munich, our colleagues developed bespoke solutions. In addition to model standardisation, we took on the relevant parts of the platform’s backend development. All models were embedded in accordance with the highest standards of scientific software, ensuring reproducibility, validation and transparency throughout.
The release as an open-source platform on GitHub ensures that the global scientific community can benefit from the solution and continue to develop it. Biocentral is therefore not just a research project, but an invitation to collaboration and a catalyst for open science.
![[Translate to en:] Biocentral Datenvisualisierung](/fileadmin/_processed_/3/d/csm_Biocentral__1__310d0e8355.png)
With Biocentral, researchers now have, for the first time, a central hub where they can work with various AI models without having to familiarise themselves with their technical inner workings or operate them themselves. The platform abstracts technical complexity, thereby making the potential of modern AI truly accessible, even for smaller research units or university research groups.
Scientists can apply existing models directly, analyse data, validate hypotheses and generate new insights – more quickly, more reliably and in a standardised manner. This is particularly beneficial for biochemical, pharmaceutical and medical research teams that have previously been unable to use machine learning due to a lack of resources or expertise.
![[Translate to en:] Benutzeroberfläche der Biocentral Datenbank](/fileadmin/_processed_/b/c/csm_biocentral_protein_database_a04939981d.png)
The development of Biocentral is only just beginning: In future, the platform is set to be expanded to include further biological data sources and analysis functions. The long-term vision is a central, interdisciplinary working platform that not only accelerates scientific exchange but also supports the development of new active substances and the planning of preclinical studies.
Working with iteratec GmbH was a very pleasant experience – solutions to tricky problems were developed reliably and with determination, which saved us a great deal of time at the department. We are delighted by the interest shown in our research area of bioinformatics and are firmly convinced that innovative technology, combined with cutting-edge research, unlocks optimal synergies for both sides. We’d be happy to work with them again!
TU Munich
Python, PyTorch, ONNX, Flask, Jupyter Notebook
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Dr. Felix Böhmer, Director Al & Data Analytics
Biocentral provides centralised access to powerful AI models for biomedical research and makes them easy to use. Researchers can analyse biomedical data, validate hypotheses and gain new insights without any programming knowledge. The platform is used in both research and teaching, thereby accelerating scientific work.
iteratec has further developed the open-source Biocentral platform in collaboration with the Technical University of Munich. Our colleagues have standardised AI models, taken charge of parts of the back-end development and embedded the models in accordance with high standards for scientific software. This has transformed scattered, hard-to-maintain models into a scalable, intuitive and future-proof platform.
Biocentral relies on modern deep learning methods, in particular protein language models. One example is the SETH model, which can quickly identify disordered regions in protein structures and is relevant to Alzheimer’s research, amongst other things. The platform is inspired by developments such as AlphaFold and enables a new level of quality in the prediction and analysis of biological structures.
Many of the original models were developed in PyTorch and were difficult to maintain in production environments. By consistently migrating them to the open ONNX format, dependencies on proprietary frameworks are reduced. This enhances the platform’s maintainability, interoperability and future-proofing, and facilitates the deployment of the models in various environments.
Biocentral is aimed at biochemical, pharmaceutical and medical research teams, as well as university research groups. They can use AI models without having to familiarise themselves with how they work technically or operate their own infrastructure. This lowers the barriers to entry, saves time in the laboratory and makes modern AI methods accessible even to smaller research units.
