Webinar - The Value of AI-driven Computational Modeling for BioTechs

June 22, 2021




Time: 13.00 CEST 
Speakers: Dan Cannon and David Siebert

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, we will focus on useful computational applications for BioTech companies in the development of new large and small molecule therapies. Showcasing a comparison of powerful physics-based methods vs. wet lab techniques as well as on advantageous employments of machine/deep learning for molecular designs. The presentation will also highlight the significance of digitization and how it can transform the recent drug discovery working environment. 

Furthermore, we will give an outlook on computational modeling becoming a main methodology in the drug discovery process and where we imagine it to be in the next decade.

In this webinar you will:  

  • Learn about physics-based methods for large as well as 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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For any additional questions or information, please email eu_webinar@schrodinger.com.

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