We are at a pivotal moment in healthcare: unprecedented scientific and technological progress in biology over the past two decades bear the promise of radically transforming the way we develop treatments and provide care to patients. Yet, 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 is now twice higher than just a decade earlier.

Machine learning-based approaches present a unique opportunity to address this challenge. The MLDD workshop aims at bringing together the community to discuss cutting edge research in this area, with a focus on the following three themes:

• 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 (see full schedule).

In parallel to the workshop, we are organizing a machine-learning challenge focusing on active learning for drug target identification.

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 GeneDisco Challenge website.

Contact: mldd.workshop@gmail.com

Discord: https://discord.gg/7MVhbMkSpe

Registration: https://iclr.cc/Register/view-registration

MLDD Workshop - ICLR 2022