Machine Learning for Drug Discovery (MLDD)

Pascal Notin · Stefan Bauer · Andrew Jesson · Yarin Gal · Patrick Schwab · Debora Marks · Sonali Parbhoo · Ece Ozkan · Clare Lyle · Ashkan Soleymani · Júlia Domingo · Arash Mehrjou · Melanie Fernandez Pradier · Anna Bauer-Mehren · Max Shen

Abstract Workshop Website
Fri 29 Apr, 6 a.m. PDT


We are at a pivotal moment in healthcare characterized by unprecedented scientific and technological progress in recent years together with the promise of personalized medicine to radically transform the way we provide care to patients. However, drug discovery has become an increasingly challenging endeavour: 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 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. We would like to organize a workshop on ‘Machine Learning for Drug Discovery’ (MLDD) at ICLR 2022 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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Timezone: America/Los_Angeles »