The scientific community is currently witnessing rapid technological advancements in artificial intelligence (AI) applied to the natural sciences. Some notable breakthroughs have involved the domains of proteins and small molecules, where innovation in AI has allowed to accurately predict the protein structure from sequence [1, 2], design novel bioactive molecules in a ‘rule-free’ manner [3–6], protein design and optimization [7, 8], and organic synthesis prediction [9–12]. Such breakthroughs have been driven by unique synergies between deep learning developments and biological/chemical knowledge.

The domains of small molecules and proteins are highly intertwined, and synergies between these domains bear tremendous potential to tackle multiple scientific challenges, e.g., to accelerate drug discovery, design safer molecules and for receptor ‘de-orphaning’.

Despite this untapped potential, the communities hardly interact and are progressively diverging in the methods and applications. Moreover, experiments are often ne- glected in AI-focused research, but are crucial to validate the predictions and generate value in adjacent scientific domains. Future breakthroughs will be possible only thanks to interdisciplinary research, at the interface between domain knowledge, method development, and experimental application.

How will AI help us to design the small molecules and proteins of tomorrow? In this track, we will bring together researchers with multidisciplinary backgrounds (e.g., chem- and bioinformatics, deep and machine learning, medicinal chemistry and chemical biology, and protein engineering) to discuss grand challenges in biology and chemistry, which only become ac- cessible when experts from diverse fields meet and interact.