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
Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes using novel waveform parameters and a modified random forest algorithm
– TOXICOLOGY LETTERS
(2021)
350,
S61
Inferring longitudinal cascades of mechanistic events in drug-induced liver injury from transcriptomic and histopathology data using sequential pattern and rule mining
– NAUNYN-SCHMIEDEBERGS ARCHIVES OF PHARMACOLOGY
(2021)
394,
S18
Artificial Intelligence in Drug Discovery and Computational Safety: What is Realistic, What are Illusions?
– TOXICOLOGY LETTERS
(2021)
350,
S49
Systematic Analysis of Protein Targets Associated with Adverse Events of Drugs from Clinical Trials and Postmarketing Reports.
– Chemical research in toxicology
(2020)
34,
365
New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets
– Chemical research in toxicology
(2020)
34,
438
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet
– Drug Discovery Today
(2020)
26,
511
(doi: 10.1016/j.drudis.2020.12.009)
Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein–Ligand Predictions
– Journal of chemical information and modeling
(2020)
60,
4546
(doi: 10.1021/acs.jcim.0c00476)
Transcriptomics predicts compound synergy in drug and natural product treated glioblastoma cells
– PLoS One
(2020)
15,
e0239551
(doi: 10.1371/journal.pone.0239551)
Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens
– Patterns (New York, N.Y.)
(2020)
1,
100065
(doi: 10.1016/j.patter.2020.100065)
Systematic analysis of protein targets associated with adverse events of drugs from clinical trials and post-marketing reports
(2020)
2020.06.12.135939
(doi: 10.1101/2020.06.12.135939)
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