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The Value of AI-driven Computational Modeling for BioTechs
The development of clinical drug candidates is very challenging and resource-intensive, especially in the early stages of a company. In this talk, we will showcase how powerful physics-based methods as well as the use of machine/deep learning algorithms can outperform wet lab techniques to save resources in the preclinical development of small and large molecules.

In this webinar you will:
- Learn about physics-based methods for large and small molecule workflows (wet lab vs dry lab)
- Understand how computational methods are becoming more powerful and the role of AI in Drug Discovery
- See the impact of digitalization on an international and interdisciplinary workplace

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Dr. Dan Cannon
Senior Scientist @Schrödinger
Dr. David Siebert
Account Manager @Schrödinger