LOGOS (Language Of Generative Objects in Science) is the first multi-domain generative framework built on a unified scientific grammar. It encodes diverse scientific objects — proteins, antibodies, small molecules, chemical reactions, materials, and their spatial interactions — as token sequences over a shared vocabulary, enabling a single autoregressive model to perform generation, prediction, and design across the natural sciences.
Unlike approaches that rely on natural language as an intermediary or require explicit 3D geometric networks, LOGOS operates directly on domain-native representations. Key spatial relationships (e.g., protein pocket–ligand contacts) are discretized and tokenized into the shared grammar, allowing the model to learn complex structural interactions in a purely sequential manner.
LOGOS Framework Overview
Key Features
Unified Scientific Grammar: A shared representational interface that encodes heterogeneous scientific objects and cross-object relationships into a common discrete token space.
One Model Fits All: A single autoregressive model handles tasks across proteins, small molecules, materials, reactions, antibodies, and their interactions.
No Explicit 3D Geometry Required: Spatial contact and constraint patterns are captured through tokenized representations, without relying on geometric neural networks or explicit coordinates.
Pre-training & Downstream Alignment: The grammar space ensures formal consistency between continued pre-training objectives and downstream task goals.
Data Construction in LOGOS
Supported Tasks
LOGOS achieves competitive or state-of-the-art performance across six representative downstream tasks:
Task
Domain
Description
Interaction-Aware Ligand Design for Binding Pockets
Drug Discovery
Generate ligands capable of specifically binding to a protein binding pocket
Protein Ligand-Binding Site Identification
Structural Biology
Identify binding pockets from protein sequences
Retrosynthesis Prediction
Chemistry
Predict reactants given a target product
Unconditional Material Generation
Materials Science
Generate novel and valid materials
Protein Editing
Protein Engineering
Edit protein sequences for improved functional properties
Antibody CDR Design
Immunology
Design complementarity-determining regions for antibody engineering
Benchmark Comparison
Model Architecture
LOGOS is based on an autoregressive Transformer architecture with continued multi-domain pre-training on a unified scientific grammar. The framework spans a parameter range from 1B to 8B, with stable scaling behavior observed across this range.
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bibtex
1@misc{li2026speakinglanguagesciencegeneralpurpose,
2 title={Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences},
3 author={Mingyang Li and Yurou Liu and Jieping Ye and Bing Su and Ji-Rong Wen and Zheng Wang},
4 year={2026},
5 eprint={2606.16905},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL},
8 url={https://arxiv.org/abs/2606.16905},
9}
