Rapid and Reproducible Multimodal Biological Foundation Model Development with AIDO.ModelGenerator

Abstract

Foundation models (FMs) for DNA, RNA, proteins, cells, and tissues have begun to close long-standing performance gaps in biological prediction tasks, yet each modality is usually studied in isolation. Bridging them requires software that can ingest heterogeneous data, apply large pre-trained backbones from various sources, and perform multimodal benchmarking studies at scale. We present AIDO.ModelGenerator, an open-source toolkit that turns these needs into declarative experiment recipes through a structured experimental framework. AIDO.ModelGenerator provides (i) 300+ datasets covering DNA, RNA, protein, cell, spatial, and multimodal data types; (ii) 30+ pretrained FMs ranging from 3M to 16B parameters; (iii) 10+ plug-and-play use-cases covering inference, adaptation, prediction, generation, and zero-shot evaluation; and (iv) YAML-driven experiment recipes that enable exact reproducibility. On a sequence-to-expression prediction task, AIDO.ModelGenerator systematically builds and tests unimodal and multimodal models, achieving a new SOTA by combining DNA and RNA FMs that outperforms unimodal baselines by over 10%. In a Crohns disease case-study, the frameworks simulated knockout protocol ranks the clinically implicated target SOX4 6,000 positions higher than differential-expression baselines, illustrating its utility for therapeutic target discovery. We release code, tutorials, checkpoints, datasets, and API reference to accelerate multimodal FM research in the life sciences1.