Talk abstract: BenevolentAI is a London-based technology company that explores the use of deep learning and other cutting-edge computational techniques to enhance and accelerate drug discovery. The company has created an enriched data landscape by processing and integrating large volumes of data generated at all stages of drug discovery: from in vitro experiments involving novel chemical compounds, through to side effect information about approved drugs. Data derived from clinical trial registries – which are of special importance due to their focus on patients, clinical safety, and efficacy – are integrated with other resources through robust semantic indexing and linkage to drug structures and classes. BenevolentAI uses this information to identify interesting disease areas and repurposing opportunities and to train and validate computational models.
Bio: Following her undergraduate studies in Biotechnology, Magdalena was awarded a DAAD (German Academic Exchange Service) scholarship and studied Life Science Informatics (MSc) at the University of Bonn, Germany.During her studies, she worked at Fraunhofer Institute for Algorithms and Scientific Computing where her projects included the application of language models for classification of biomedical abstracts, analysis of expression data, and developing network models of chemical datasets. She later pursued an internship project at the European Bioinformatics Institute (UK), where she developed a graph database of pharmacological assays. Currently, Magdalena is working as a Biomedical Data Scientist at BenevolentAI, where she is involved in data integration, modelling, and analysis projects in the fields of bio- and chemoinformatics. In addition, she is registered as a PhD student at the University College London. Her PhD research focuses on applications of network science and machine learning models to bioassay and clinical data from drug discovery.