ICLR 2023
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Machine Learning for Drug Discovery (MLDD)

Pascal Notin · Sonali Parbhoo · Patrick Schwab · Ece Özkan Elsen · Stefan Bauer · Debora Marks · Yarin Gal · Ashkan Soleymani · Clare Lyle · Max Shen · Ehsan Hajiramezanali


We are at a pivotal time in healthcare characterized by unprecedented scientific and technological progress in recent years together with the promise borne by personalized medicine to radically transform the way we provide care to patients. However, drug discovery has become an increasingly challenging endeavor: not only has the success rate of developing new therapeutics been historically low, but this rate has been steadily declining. The average cost to bring a new drug to market (factoring in failures) is now estimated at 2.6 billion – 140% higher than a decade earlier. Machine learning-based approaches present a unique opportunity to address this challenge. While there has been growing interest and pioneering work in the machine learning (ML) community over the past decade, the specific challenges posed by drug discovery are largely unknown by the broader community. Last year, the first MLDD workshop at ICLR 2022 brought together hundreds of attendees, world-class experts in ML for drug discovery, received about 60 paper submissions from the community, and featured a two-month community challenge in parallel to the workshop. Building on the success from last year, we would like to organize a second instance of the MLDD workshop at ICLR 2023, with the ambition to federate the community interested in this application domain where i) ML can have a significant positive impact for the benefit of all and ii) the application domain can drive ML method development through novel problem settings, benchmarks and testing grounds at the intersection of many subfields ranging representation, active and reinforcement learning to causality and treatment effects.

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