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
Applying synergy metrics to combination screening data: agreements, disagreements and pitfalls.
Drug discovery today
(2019)
24
2286
(doi: 10.1016/j.drudis.2019.09.002)
Triazole-Pyridine Dicarbonitrile Targets Phosphodiesterase 4 to Induce Cytotoxicity in Lung Carcinoma Cells.
Chemistry & biodiversity
(2019)
16
e1900234
(doi: 10.1002/cbdv.201900234)
Understanding Conditional Associations between ToxCast in Vitro Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods
Chemical research in toxicology
(2019)
33
137
Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account.
Current protocols in chemical biology
(2019)
11
e73
(doi: 10.1002/cpch.73)
Concepts and Applications of Conformal Prediction in Computational Drug
Discovery
(2019)
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
Journal of cheminformatics
(2019)
11
41
(doi: 10.1186/s13321-019-0364-5)
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images.
Journal of cheminformatics
(2019)
11
41
(doi: 10.1186/s13321-019-0364-5)
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.
Nature communications
(2019)
10
2674
(doi: 10.1038/s41467-019-09799-2)
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout.
Journal of Chemical Information and Modeling
(2019)
59
3330
(doi: 10.1021/acs.jcim.9b00297)
Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity.
Journal of Cheminformatics
(2019)
11
36
(doi: 10.1186/s13321-019-0356-5)
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