Unlike systems that use natural language as an intermediary or require explicit 3D geometric networks, LOGOS operates on domain-native representations. Spatial relationships such as protein pocket-ligand contacts are discretized and tokenized so one autoregressive model can learn sequential scientific structure.
LOGOS is useful for scientific AI researchers building cross-domain generative models for chemistry, biology, drug discovery, and materials. The repository provides inference scripts and Hugging Face model links for tasks such as retrosynthesis, binding-site identification, ligand design, and material generation.
