Professor of Molecular Informatics
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 in Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure
– Mol Pharm
(2022)
19,
1488
Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data
– Chem Res Toxicol
(2022)
35,
670
Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning
– Stem Cell Reports
(2022)
17,
556
(doi: 10.1016/j.stemcr.2022.01.009)
Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection
– biorxiv
(2022)
2022.01.07.475326
(doi: 10.1101/2022.01.07.475326)
DDREL: From drug-drug relationships to drug repurposing
– Intelligent Data Analysis
(2022)
26,
221
(doi: 10.3233/ida-215745)
Chapter 8: Using Artificial Intelligence for Drug Repurposing
(2022)
2022-January,
147
(doi: 10.1039/9781839163401-00147)
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 Mol Biol
(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)
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