Machine learning in drug discovery at Bayer Pharmaceuticals: from models to molecules

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About Marc Osterland's Session on Machine Learning in Drug Discovery: From Models to Molecules

Join Marc Osterland, ML Engineer at Bayer, as he explores the transformative impact of machine learning on pharmaceutical research. This session focuses on how ML is revolutionizing the drug discovery process, from identifying potential drug targets to optimizing candidate molecules.

Key points covered in the session include:

An overview of the drug discovery process and where machine learning can make a significant impact.
Techniques for lead identification, including morphological profiling and in silico high-throughput screening.
The integration of machine learning models to predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties.
How self-supervised learning and data integration enhance the efficiency and accuracy of drug discovery.
The future of drug discovery with AI, including the potential for digital twins and the reduction of animal testing.

Discover how Bayer is leveraging cutting-edge machine learning techniques to streamline drug development and bring new therapies to market faster and more efficiently.