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  • Early target (drug) discovery with AI: challenges, progress, and future directions

Early target (drug) discovery with AI: challenges, progress, and future directions

February 27, 2024
11:00 pm EDT | 8:00 am PDT | 4:00 pm GMT

In this presentation, we’ll delve into the fascinating world of using machine learning and AI for drug discovery. The potential of these technologies to uncover drugs that traditional methods might overlook and accelerate the typically slow drug discovery process is emphasized.

The speech highlights the importance of understanding cell biology and the role of DNA in disease development. Through machine learning, the goal is to identify differences between healthy and diseased cells, providing a blueprint for potential therapies.

The challenges in AI drug discovery are discussed, particularly the need for comprehensive and searchable data assets. Despite the high costs and complexities involved, the value of sharing data assets while recognizing the nuanced considerations involved in open-sourcing such resources is acknowledged.

This presentation offers a glimpse into the promising intersection of AI and pharmaceuticals, fueled by ongoing innovation and collaboration within the industry.

Shane Lewin, Vice President, AI/ML, GSK

Shane builds teams that leverage machine learning to solve impossible problems, now as VP in AI/ML at GSK working on early target discovery.

He has been building data-driven products for a little under 15 years, spanning early-stage start-ups, large organizations, and companies making the transition between the two.

He holds an MS in Computational and Mathematical Engineering from Stanford, and degrees in Mathematics and Molecular Biology from the University of Colorado at Boulder.

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