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
A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria
– Malar J
(2018)
17,
160
(doi: 10.1186/s12936-018-2294-5)
Special Issue: Cheminformatics in Drug Discovery.
– ChemMedChem
(2018)
13,
467
(doi: 10.1002/cmdc.201800123)
eMolTox: prediction of molecular toxicity with confidence
– Bioinformatics
(2018)
34,
2508
Common structural and pharmacophoric features of mPGES-1 and LTC4S.
– Future Medicinal Chemistry
(2018)
10,
259
(doi: 10.4155/fmc-2017-0123)
Understanding the effect of arsenic treatment on breast cancer cell lines using gene expression analysis
– ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)
256,
Developments in toxicogenomics: Understanding and predicting compound-induced toxicity from gene expression data
– Mol Omics
(2018)
14,
218
(doi: 10.1039/c8mo00042e)
MD-assisted approach for designing multi-target ligands at A2AR and PDE10A that elevate cyclic AMP
– ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)
256,
Computational selectivity modelling for bromodomains: Insights into selectivity and discovery of new small-molecule hits
– ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)
256,
Gearing transcriptomics towards high-throughput screening: Compound shortlisting from gene expression using in silico information
– ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)
256,
Binding mode ensembles determine ligand efficacy at a GPCR
– NAUNYN-SCHMIEDEBERGS ARCHIVES OF PHARMACOLOGY
(2018)
391,
S6
- <
- 15 of 42