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 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. Last year, the first ‘Machine Learning for Drug Discovery’ (MLDD) workshop at ICLR 2022 brought together hundreds of attendees and world-class experts in ML for drug discovery. The second edition of MLDD workshop aims at bringing together the community to discuss cutting edge research in this area on the following three themes, covering the end-to-end drug discovery process:
Genetic & molecular representation learning: Methods aiming at learning compact lower dimensional representations of high dimensional structured biological objects (e.g., DNA, proteins, small molecules). The objective is to then leverage these representations in disease prediction models (e.g., variant effect predictions) or quantify the affinity between two biological entities (e.g., binding between antibody and viral proteins) to support drug and vaccine design.
Molecule optimization & target identification: Approaches to enhance the identification or the generation of new molecules that optimize specific properties of interest (e.g., drug-likeness, solubility). This is crucial for efficient large scale screening of drug precursors and protein biotherapeutics design.
Biological experiment design: Methods to guide the design and execution of complex biological experiments (e.g., active learning), in particular the efficient exploration of experiment spaces that span hundreds of billions of potential configurations. The overarching goal is to uncover causal relationships between genes and pathologies and subsequently identify more promising drug targets.
The workshop will feature talks from leading researchers and pioneers in ML applied to drug discovery, a community challenge, as well as spotlight presentations and poster sessions for accepted papers.
Causal Bench Challenge
In parallel to the workshop, we are organizing a machine-learning challenge focusing on gene graph inference on perturbational single cell data.
Teams with the best submissions will be invited to present their solution during the workshop and are eligible for prizes from our sponsor.
All details are available on the challenge website, here.
For more information about the challenge, check out the video below!
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MLDD Workshop - ICLR 2023