Towards multimodal foundation models in molecular cell biology

Haotian Cui, Alejandro Tejada-Lapuerta, Maria Brbić, Julio Saez-Rodriguez, Simona Cristea, Hani Goodarzi, Mohammad Lotfollahi, Fabian J. Theis () and Bo Wang ()
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Haotian Cui: University of Toronto
Alejandro Tejada-Lapuerta: Helmholtz Center Munich
Maria Brbić: Swiss Federal Institute of Technology (EPFL)
Julio Saez-Rodriguez: Heidelberg University, Faculty of Medicine, Heidelberg University Hospital
Simona Cristea: Dana-Farber Cancer Institute
Hani Goodarzi: Arc Institute
Mohammad Lotfollahi: Wellcome Sanger Institute
Fabian J. Theis: Helmholtz Center Munich
Bo Wang: University of Toronto

Nature, 2025, vol. 640, issue 8059, 623-633

Abstract: Abstract The rapid advent of high-throughput omics technologies has created an exponential growth in biological data, often outpacing our ability to derive molecular insights. Large-language models have shown a way out of this data deluge in natural language processing by integrating massive datasets into a joint model with manifold downstream use cases. Here we envision developing multimodal foundation models, pretrained on diverse omics datasets, including genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial profiling. These models are expected to exhibit unprecedented potential for characterizing the molecular states of cells across a broad continuum, thereby facilitating the creation of holistic maps of cells, genes and tissues. Context-specific transfer learning of the foundation models can empower diverse applications from novel cell-type recognition, biomarker discovery and gene regulation inference, to in silico perturbations. This new paradigm could launch an era of artificial intelligence-empowered analyses, one that promises to unravel the intricate complexities of molecular cell biology, to support experimental design and, more broadly, to profoundly extend our understanding of life sciences.

Date: 2025
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DOI: 10.1038/s41586-025-08710-y

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