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
Integrating structure-based approaches in generative molecular design.
– Current Opinion in Structural Biology
(2023)
79,
102559
(doi: 10.1016/j.sbi.2023.102559)
In silico Prediction and Biological Assessment of Novel Angiogenesis Modulators from Traditional Chinese Medicine
– Front Pharmacol
(2023)
14,
1116081
(doi: 10.3389/fphar.2023.1116081)
Using chemical and biological data to predict drug toxicity
– SLAS Discovery
(2023)
28,
53
(doi: 10.1016/j.slasd.2022.12.003)
Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning.
– Toxicology and Applied Pharmacology
(2022)
459,
116342
(doi: 10.1016/j.taap.2022.116342)
Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database
– Regulatory Toxicology and Pharmacology
(2022)
138,
105309
(doi: 10.1016/j.yrtph.2022.105309)
Statistical analysis of preclinical inter-species concordance of histopathological findings in the eTOX database
– Regulatory Toxicology and Pharmacology
(2022)
138,
105308
(doi: 10.1016/j.yrtph.2022.105308)
Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation
– J Cheminform
(2022)
14,
68
(doi: 10.1186/s13321-022-00646-z)
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.
– Briefings in bioinformatics
(2022)
23,
bbac404
(doi: 10.1093/bib/bbac404)
scRNA-Seq-based drug repurposing targeting idiopathic pulmonary fibrosis (IPF)
(2022)
(doi: 10.1101/2022.09.17.508360)
PL05-01 Using chemical and biological data and AI for predictive safety – fundamental concepts, new readouts, applications, limitations
– Toxicology Letters
(2022)
368,
S11
(doi: 10.1016/j.toxlet.2022.07.045)
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