Chemical information lives in many forms: SMILES strings, spectral signals, microscopy images, and decades of published literature. Traditional tools handle these separately, losing the connections between them.
Multimodal machine learning changes that. By aligning representations across data types, models can answer questions like finding molecules matching a spectrum snippet or surfacing literature relevant to a structural motif.
Building these capabilities responsibly requires careful evaluation across modalities, which is precisely the focus of our current research partnerships.