ML for Life and Material Science: From Theory to Industry Applications (ICML 2024 Workshop)

Abstract

Biology and chemistry play a central role in understanding life, and are a fundamental pillar of human well-being through their roles as medicines, materials, or agro-chemicals. With increasing challenges associated with climate change, growth of the global population, diseases associated with aging, and the global supply of food and energy, it is becoming increasingly urgent to accelerate the pace at which technical discoveries can be made, and translated into practical solutions to these societal issues. However, compared to other modalities such as images or language, the study of biology and chemistry with machine learning is not as industrially established. Multiple factors contribute to this delay. Different research questions require many levels and scales of representation, from electronic structure to graph and point cloud representations of (bio) molecules, to protein and nucleic acid sequences, crystals, omics data, cell and tissue-level representations. This workshop aims to highlight translational ML research in biology and chemistry ML for real-world applications in life-and materials science. The goal is to bridge theoretical advances with practical applications and connect academic and industry researchers. We envision a balanced scientific industrial and academic attendance, and propose committees and a lineup that reflect a mix of top industry scientists, academic leaders and double-affiliated scientists, as well as emerging scientists and new voices in ML for healthcare, molecular-, life- and material sciences. We welcome a broad range of submissions, from dataset curation, analysis and benchmarking work highlighting opportunities and pitfalls of current ML applications in health and materials, to novel models and algorithms unlocking capabilities previously thought available only through non-ML approaches. We welcome all types of ML algorithms and models relevant for this problem space. Lastly, we aim to integrate two areas - life and material sciences – as ML approaches in these areas can usually be adapted to one or the other discipline, and we want to encourage discussion between practitioners in the respective fields. Lastly, we are committed to create an inclusive workshop with broad representation across research areas, regions and beliefs.