
Professor of Molecular Informatics
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
Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
– Methods Mol Biol
(2021)
2390,
1
(DOI: 10.1007/978-1-0716-1787-8_1)
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.
– Comput Toxicol
(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)
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
Cell morphology descriptors and gene ontology profiles improve prediction for mitochondrial toxicity
– TOXICOLOGY LETTERS
(2021)
350,
S81
Relating early cellular events to Drug-Induced Liver Injury (DILI) using time-resolved transcriptomic and histopathology data
– TOXICOLOGY LETTERS
(2021)
350,
S124
Artificial Intelligence in Drug Discovery and Computational Safety: What is Realistic, What are Illusions?
– TOXICOLOGY LETTERS
(2021)
350,
S49
A transcriptomics-based new approach methodology (NAM) identifies points of departure (PoDs) of adaptive stress in HepG2 cells
– TOXICOLOGY LETTERS
(2021)
350,
S216
The impact of pooling animal histopathology control data on the statistical detection of treatment-related findings
– TOXICOLOGY LETTERS
(2021)
350,
S63
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