Discovering new molecules with desired functions or
activities is crucial for human well-being by providing
new medicines, securing the world’s food supply via
agrochemicals, or enabling a sustainable energy
conversion and storage to counter or mitigate climate
change. However, the discovery of new molecules or
molecular materials that are optimized for a particular
purpose can often take up to a decade and is highly
cost-intensive. Machine-learning (ML) methods can
accelerate molecular discovery, which is of considerable importance generally, but especially in light of the COVID-19 crisis and future pandemics. To reach this goal of speeding up the discovery of new
functional molecules, the new ELLIS program ‘Machine Learning for Molecule Discovery’ aims to
establish a dialogue between domain experts and ML researchers to ensure that ML positively impacts
real world scenarios.

The program’s objectives are to advance computational
molecular science by improving molecular
representations, molecular modeling, property
prediction, generative modeling for molecules and
molecular optimization, and chemical synthesis through
ML methods. The program intends to connect
researchers from ELLIS units such as Cambridge, Linz,
and Berlin, as well as from academia, pharmaceutical and technology companies. The direct exchange
among experts and open discussions about research results are a crucial aspect for advancing science.

"Molecules are in the center of almost all natural sciences, from chemistry and material science over
physics to molecular biology. Similarly, almost all sub-fields of machine learning yielded applications for molecules, for example: Geometric Deep Learning finds molecules a rich field for equi- and invariances, Deep Learning architectures predict molecular properties or generate molecules, and deep reinforcement learning helps with planning chemical synthesis routes. By advancing machine learning, we will speed up molecule discovery, and ELLIS has the key role in this to connect machine learning researchers with molecular sciences and industry. First steps in this direction have been made through
the ELLIS ML4Molecules workshops in 2021 and 2022 with together over 1300 registered participants",
says Günter Klambauer, ELLIS Scholar, one of the Coordinators of this program and Associate Professor
for Artificial Intelligence in Life Sciences at Johannes Kepler University Linz. The program proposal was
also coordinated by Jose Miguel Hernandez Lobato (University Cambridge, ELLIS unit Cambridge) and
Nadine Schneider (Novartis).

ELLIS Program ‘Learning for Graphics and Vision’
The fields of computer graphics and 3D computer vision have been revolutionized by machine
learning. Computer graphics sub-areas such as rendering, geometric modeling, computer animation or
computational photography and videography have witnessed tremendous algorithmic advancements
by new deep learning concepts which enable new ways for visual content creation in general. The
same holds true for 3D computer vision sub-areas such as 3D reconstruction, 3D human analysis,
intrinsic image decomposition and inverse rendering where new deep learning based methods enabled
significant improvements over the state of the art. However, deep learning has not replaced the
fundamental principles of classical computer graphics and 3D vision. Rather, the field has seen a rise of
research which systematically integrates explicit 3D world representations, image formation models, and
classical rendering techniques with deep learning frameworks.

The members of the new ELLIS program ‘Learning for Graphics and Vision’ see a big opportunity to
integrate classical computer graphics, geometry processing and 3D vision principles more tightly within
deep learning frameworks. The mission of this program is to connect researchers in the fields of
machine learning, computer graphics, 3D computer vision, and geometry processing to discuss and
investigate how machine learning can benefit from computer graphics, geometry processing and 3D
vision, as well as to investigate the explosion of new possibilities that machine learning enables for
graphics and vision.

“We are super excited to bring leading European researchers in the fields of vision, graphics and learning
together. We will establish a collaborative network and organize joint workshops to investigate how
deep learning can benefit from classical principles of 3D vision and graphics. Likewise, we will investigate
how deep learning can boost the creative process of graphics pipelines ”, highlights Gerard Pons-Moll,
ELLIS Scholar, one of the Program Coordinators and Professor of Computer Science at the University of
Tübingen.

“The new program will enable us to bring together world leading experts who explore how deep
learning and graphics and vision come together to enable new possibilities and new methods at the
intersection of the fields”, says Christian Theobalt, ELLIS Fellow, one of the Program Coordinators and
Director at the Max-Planck-Institute for Informatics in Saarbrücken.

Members of the new research programs
The new ELLIS research programs advance science by connecting outstanding scientists across Europe.
Following an ongoing evaluation process, a complete and official list of all program members and their
institutions will be announced soon.