Latest updates

Lisa completed her bachelor and master studies in Bioinformatics at Saarland University. After being a research assistant at our lab for almost a year, she now joined our team as PhD candidate. Her resarch focuses on the development of trustworthy ML models. Particularly, as part of the EU-funded RADAR project, she develops interpretable and reliable ML methods that are used to create sustainable plastic alternatives.

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We attended this year’s German Conference on Cheminformatics in November held in Bad Soden am Taunus. We were happy to present our work on multiple posters: Floriane Odje: Morphological data analysis: From descriptor development to predictive modelling Afnan Sultan: Domain adaptation as a computationally efficient approach for improving transformer models for molecular property prediction Yanyuan Zhang: Read-Across the Targetome – An integrated structure- and ligand-based workbench for computational design of novel tool compounds Hamza Ibrahim: MolDockLab: Data-driven workflow for best balanced consensus docking pipeline for hit identification Paula Kramer: Active learning for fragment-based kinase inhibitor design using docking Michael Backenköhler: Structural activity prediction models recover known kinase binding modes Katharina Buchthal: Novel kinase ligand generation using subpocket-based docking Erika Primavera: Why is Miransertib effective against the AKT1-E17K mutation?

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Our group is growing so fast, we had our own group retreat to spend some time together. We started with group games at the office and ended the day with pizza and drinks in the city. We all had a lot of fun and got to know each other much better. To be repeated soon!

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This year’s RDKit user group meeting was organized by Sereiner Riniker’s lab in Zurich, Switzerland on September 11th-13th. We used this opportunity to present our current projects during the poster sessions: Floriane Odje: Morphological data analysis: From descriptor development to predictive modeling Paula Kramer: Active learning for fragment-based kinase inhibitor design using docking Hamza Ibrahim: MolDockLab: Data-driven workflow to find best balanced consensus docking pipeline for hit identification Additionally, Afnan Sultan gave a talk about the current state of transformers for property prediction and Antoine Lacour introduced us to his consensus docking tool called DockM8 in his lightning talk.

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As part of the ICML conference in Vienna, we attended the Machine Learning for Life and Material Science workshop in Vienna, Austria on July 26th. Michael and Joschka presented their poster on the current updates on their kinodata-3d project by investigating the explainability of their structural model for binding affinity prediction. Paula presented an approach for fragment-based kinase inhibitor design using active learning on her poster. We especially enjoyed meeting fellow researchers working in the intersection of machine learning and drug discovery.

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We recently published a literature review in Frontiers in Toxicology titled “Unleashing the Potential of Cell Painting Assays for Compound Activities and Hazards Prediction.” This paper is part of the research topic “Leveraging Artificial Intelligence and Open Science for Toxicological Risk Assessment.” In this review, we highlight how single-cell-level data from cell painting assays can be combined with structural information to predict compound activities for various human-relevant disease endpoints and to identify underlying modes of action using machine learning and deep learning.

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Hamza has successfully defended his master’s thesis titled “MolDockLab: A Data-Driven Approach to Optimizing Docking Tools, Scoring Functions, and Ranking Methods for Targeted Applications." In his research, he developed MolDockLab, a data-driven approach for optimizing the combination of docking tools, scoring functions, and ranking methods for specific targets. Using energy-coupling factor (ECF) transporters as a case study, his approach identified 18 compounds from the in-house HIPS library for further in vitro assays.

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We are happy to announce that our paper “Reliable anti-cancer drug senstivity prediction and prioritization” was finally published in Scientific Reports. Tackling the challenge of reliable ML predictions in medical applications, we developed a novel approach for predicting and prioritizing anti-cancer drug responses with guaranteed certainty levels, a unique contribution to the field. This research was conducted in collaboration with Kerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, and Hans-Peter Lenhof from the Lenhof chair.

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Hamza holds a bachelor’s degree in Pharmaceutical Sciences and a master’s degree in Bioinformatics from Saarland University, and he is joining as a PhD candidate. He completed his master’s thesis in the Volkamer Lab, developing MolDockLab. In his doctoral research, Hamza will focus on developing a physics-based scoring function for protein-ligand interactions. His work aims to advance structure-based virtual screening techniques for hit identification, which is crucial in the early stages of drug development.

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Antoine joins our team coming from a medicinal chemistry background, bringing extensive experience in computational approaches to drug discovery. After completing his MSc in Medicinal Chemistry at the University of York, he pursued doctoral research under Prof. Anna Hirsch’s supervision, specializing in computational chemistry. His research portfolio spans multiple therapeutic areas, including work on sepsis and neurodegenerative diseases in the Netherlands, and antibiotic development in Germany. In his current role, he will focus on developing consensus virtual screening methods while providing computational chemistry expertise across our medicinal chemistry collaborations.

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