New Book Chapter: Morphological Data Analysis for Predictive Modelling We are happy to share that a new protocol has been published in Methods in Molecular Biology (MIMB, Volume 2989) titled
“Morphological Data Analysis: From Descriptor Development to Predictive Modelling.” The chapter is based on work carried out during the first year of my PhD project and focuses on the computational analysis of morphological fingerprints derived from Cell Painting assays. These fingerprints capture quantitative information about cellular morphology, texture, and organelle organization, and can be used for downstream applications such as compound similarity search and activity prediction using machine learning methods.
Read more
This year’s ELLIS workshop ML4Molecules was held in conjunction with EurIPS in Copenhagen. Michael Backenköhler and Joschka Gross presented two posters on recent work in machine learning using ChEMBL data.
The first poster presents the ideas laid out in “Assay-Based Machine Learning: Rethinking Evaluation in Drug Discovery”. Public datasets are typically aggregates of experimental data originating from multiple laboratories. As a result, inconsistencies in the data are unavoidable. In this work, we investigate how conclusions drawn from standard machine learning workflows change when the assumption of measurement consistency is relaxed.
Read more
We are happy to announce that the preprint of our paper “The Trustworthiness Landscape in Machine Learning: A Conceptual Guide with Applications in Medicine” is finally live on Zenodo: https://zenodo.org/records/17591544. It explains more than 17 concepts related to the trustworthiness of machine learning (ML), including, e.g., reliability, robustness, fairness, interpretability/explainability, security and privacy, and explores how these concepts connect and conflict. It is targeted at anyone who wants to use ML responsibly, and especially researchers who encounter ML-based science in their daily work (not necessarily as developers).
Read more
Last week, the CytoData Hackathon in Berlin-Buch brought together researchers from different backgrounds to work on challenges in bio-image–based profiling, offering a great space for collaboration and exchange.
This was followed by the #CytoData2025 Symposium, where I presented ongoing work titled “Uncovering Compound Mode of Action via Hierarchical Clustering of Structural and Morphological Profiles” as part of the Morphology-based Endocrine Disruptors Screening project. The project aims to predict endocrine disruption by combining Cell Painting profiles with cheminformatics methods.
Read more
For this year’s group retreat, we kicked things off with a fun bowling outing that brought out some friendly competition. The evening continued with pizza and drinks, giving everyone a chance to catch up outside of work. It was a great opportunity to unwind together and strengthen connections as a group.
Read more
Lisa and Kerstin, who now is a junior professor at Universtiy Medicine Göttingen, attended together the BioTechX Europe 2025 conference in Basel. They presented together a poster “Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach”, which is also published on ChemRxiv. It was a great opportunity to share our work and interact with fellow researches in academia and industry.
Read more
A new preprint is out! We developed a new fragment-based pipeline to generate novel kinase ligands using subpocket-based docking. In collaboration with the Diederich lab at the Phillips University Marburg, selected compounds were synthesized and tested against the PKA kinase, showing promising activity. We were able to crystallize the most promising hit in complex with PKA.
Thanks for all the co-authors for a great collaboration on this project: Katharina Buchthal, Paula Linh Kramer, Andrea Volkamer and the Diederich lab from the Phillips University Marburg: Dominik Hubach, Johannes Krieger, Gina Bach, Naemi Wagner, Moritz Klopper, Michael Daude and Wibke E.
Read more
As part of the 8th Artificial Intelligence in Chemistry Symposium
(AiChem 8 conference website), Raquel, Afnan, and Lisa participated in both the pre-conference workshop and the main conference, contributing to discussions at the intersection of chemistry, data science, and machine learning.
Raquel, Afnan, Lisa, Andrea and Pat organized the pre-conference workshop, which focused on introducing practical machine learning workflows for chemistry. Lisa introduced the basics of Python and Jupyter notebooks, Raquel presented a hands-on notebook on data curation and cleaning, and Afnan explored molecular representations and machine learning models commonly used in cheminformatics.
Read more
This year’s EUROPIN Summer School on Drug Design was organized by the Pharmacoinformatics research group in Vienna from 14th-19th of September 2025. Floriane presented a poster on “Integrating structural and morphological fingerprints: Understanding information for pattern identification and better toxicity prediction”. Paula presented her poster on “Active learning for fragment-based kinase inhibitor design”, for which she won the poster prize. In addition, as part of the EUROPIN PhD program, they both gave their application talks presenting their current research projects.
Read more
This year’s Gordon’s Seminar and Conference on Computer-Aided Drug Design iteration was held in Portland, Maine. Paula contributed a spotlight presentation and a poster on fragment-based active learning for kinase ligand design. It was a great experience connecting with researchers from Europe and the U.S., and engaging in insightful discussions with both academic and industry experts.
Read more