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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Afnan attended the first occurrence of the NLP in Biology and Chemistry Symposium on March, 18th which was held at Bern University, Switzerland. She presented a 10 minutes talk about her latest work on reviewing the transformer models for molecular property prediction. The work was in collaboration with BASF, and a pre-print can be found here.
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Yanyuan holds a Bachelor’s degree in Bioinformatics from Université Claude Bernard Lyon 1 and a Master’s degree in Bioinformatics from Université Paris Cité. Joining us as a PhD student in March 2024, Yanyuan will contribute on the Ratar project, focusing on developing innovative methods for binding site comparison.
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Starting the new year by publishing a paper Hit Optimization by Dynamic Combinatorial Chemistry on Streptococcus pneumoniae Energy-Coupling Factor Transporter (ECF)-PanT.
We had a great collaboration with the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), identifying potential hits for energy-coupling factor (ECF) transporters by computationally analyzing the binding pocket using molecular dynamics simulations.
Thanks to the great collaboration on this project — we’re looking forward to discovering more hits!
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Afnan holds a bachelor’s degree in “biomedical sciences” with specialization in “computational biology” from the University of Science and Technology at Zewail City. She finished her master’s degree in “bioinformatics” at Saarland University. She joined our lab in October 2023 as a PhD candidate in collaboration project with BASF. She will work on the development of deep learning models, especially, transformer models, to predict molecular properties of small molecules and generate molecules with specific non-toxic properties.
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