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This is the last webinar in the three-part series titled “Dear data: what compound to make next? - From SAR databases to AI/ML models”

In this session you will learn about synergies between academic and industry research when applying AI/ML for drug discovery. Both are key players for the progress of molecular machine learning, but despite common open research questions and long-term goals, the nature and scope of investigations typically differ between academia and industry.

We will highlight the opportunities that machine learning models offer to accelerate and improve compound selection, reviewing the model life cycle: data preparation, model building, validation, and deployment. Furthermore, application aspects in the design-make-test-analyze cycle are discussed. We close with strategies that could improve collaboration between academic and industrial institutions and will advance the field even further.

"There has been great advances in the field of molecular ML, and models have permeated almost every step in the DMTA cycle”.
  • Section One [40 Min]: AI advancing drug discovery research in the pharmaceutical industry and academia - Dr. Raquel Rodríguez-Pérez & Dr. Jessica Lanini
  • Section Two [15 Min]: Q&A with Audience Questions
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