
Currently also: Chief Informatics and Technology Officer (CITO) at Pangea Botanica, London/UK and Berlin/Germany
Previous positions:
Director Digital Chemistry at NUVISAN Berlin
Associate Director Computational ADME and Safety (Clinical Pharmacology & Safety Sciences/Data Science and Artificial Intelligence - CPSS/DSAI) at AstraZeneca Cambridge
Co-founder of Healx Ltd.
Co-founder of PharmEnable Ltd.
- Committed to developing new life science data analysis methods (AI/ML/data science) and their application, primarily related to chemical biology, drug discovery and in silico toxicology
- Expertise comprises data ranging from chemical structure and gene expression data to phenotypic readouts and preclinical information, applied to both efficacy- and safety/tox-related questions
- Collaborating with academic research groups, as well as pharmaceutical, chemical, and consumer goods companies (Eli Lilly, AstraZeneca, GSK, BASF, Johnson&Johnson/Janssen, Unilever, ...)
- Co-founder/founding CTO and current SAB member of Healx Ltd. (data-driven drug repurposing for rare diseases, and beyond); co-founder of PharmEnable Ltd.; SAB member of Lhasa Ltd. (toxicology and metabolism prediction) and Cresset Ltd.
- Coordinator of the Computational & In Silico Toxicology Specialty Section of the British Toxicology Society (BTS)
- Steering Committee Member of the Cambridge Alliance on Medicines Safety (CAMS)
- Currently leading a group of ca. 15 PhD students, postdocs, project students and visitors at the Centre for Molecular Informatics at the University of Cambridge, https://www-cmi.ch.cam.ac.uk/centre-molecular-informatics
Publications
Computational analyses of mechanism of action (MoA): data, methods and integration.
RSC Chemical Biology
(2021)
3
170
(doi: 10.1039/d1cb00069a)
Machine Learning Models for Human in Vivo Pharmacokinetic Parameters with In-House Validation
Mol Pharm
(2021)
18
4520
Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
Methods in Molecular Biology
(2021)
2390
1
(doi: 10.1007/978-1-0716-1787-8_1)
Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)
(2021)
2021.09.23.461089
(doi: 10.1101/2021.09.23.461089)
In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.
Comput Toxicol
(2021)
20
100188
(doi: 10.1016/j.comtox.2021.100188)
In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity.
Computational toxicology (Amsterdam, Netherlands)
(2021)
20
100187
(doi: 10.1016/j.comtox.2021.100187)
Prediction and identification of synergistic compound combinations against pancreatic cancer cells.
iScience
(2021)
24
103080
(doi: 10.1016/j.isci.2021.103080)
Cell morphology descriptors and gene ontology profiles improve prediction for mitochondrial toxicity
Toxicology Letters
(2021)
350
S81
The impact of pooling animal histopathology control data on the statistical detection of treatment-related findings
Toxicology Letters
(2021)
350
s63
Relating early cellular events to Drug-Induced Liver Injury (DILI) using time-resolved transcriptomic and histopathology data
Toxicology Letters
(2021)
350
s124
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