Friday, April 29, 2022 - Virtual

Machine Learning for Drug Discovery Workshop

Overview

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).

Speakers

Columbia University

University of Montreal, Mila

University of Cambridge

Genentech

Technical University of Denmark, University of Copenhagen

GeneDisco Challenge

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.

Organizers

Anna Bauer-Mehren

Melanie F. Pradier

Clare Lyle

Pascal Notin

Patrick Schwab

Stefan Bauer

Yarin Gal

Debora Marks

Ece Ozkan Elsen

Max Shen

Julia Domingo

Andrew Jesson

Arash Mehrjou

Sonali Parbhoo

Ashkan Soleymani

Program Committee

Yashas Annadani

Freddie Bickford Smith

Andrea Dittadi

Tobias Höppe

Aryan Mikaeili

Muhammed Razzak

Mario Wieser

Neil Band

Jan M. Brauner

Lea Goetz

Andreas Kirsch

Monika Nagy-Huber

Maxim Samarin

Anastasiya Belyaeva

Mathieu Chevalley

Lars Holdijk

Jannik Kossen

Cuong Quoc Nguyen

Frederik Träuble



Sponsor

GlaxoSmithKline (GSK) is a science-led global healthcare company with a special purpose to improve the quality of human life by helping people do more, feel better, live longer. Every day, we help improve the health of millions of people around the world by discovering, developing and manufacturing innovative medicines, vaccines and consumer healthcare products. We are building a stronger purpose and performance culture underpinned by our values and expectations - so that together we can deliver extraordinary impact for patients and consumers.

GSK uses AI to discover transformational medicines. AI is the key to interpret genetic datasets so we can understand the 'language' of the cell and develop medicines with a higher probability of success.

MLDD Workshop - ICLR 2022