ChemSpace Copilot is an AI-powered assistant for Generative Topographic Mapping (GTM) based chemical space analysis.
ChemSpace Copilot uses a multi-agent system powered by DeepSeek LLM, coordinating specialized agents:
| Agent | Purpose |
|---|---|
| ChEMBL Downloader | Downloads bioactivity data from ChEMBL database |
| GTM Optimization | Builds and optimizes Generative Topographic Maps |
| GTM Density Analysis | Analyzes compound distributions on GTM maps |
| GTM Activity Analysis | Creates activity-density landscapes for SAR |
| GTM Loading | Loads pre-existing GTM models from storage |
| GTM Chemotype Analysis | Analyzes scaffold distributions and chemotypes |
| Autoencoder | Molecular generation via LSTM autoencoders |
| Autoencoder GTM Sampling | Combines autoencoder with GTM for targeted generation |
- Data Retrieval: Download chemical data from ChEMBL
- Dimensionality Reduction: Build GTM models for chemical space visualization
- Property Analysis: Map molecular properties onto chemical space landscapes
- SAR Analysis: Analyze activity distributions across chemical space
- Molecular Generation: Generate novel molecules using autoencoders
- Chemotype Discovery: Identify and analyze scaffold families
Type your request in natural language:
- "Download bioactivity data for EGFR from ChEMBL"
- "Build a GTM model from the downloaded data"
- "Show activity landscape for IC50 values"
- "Generate molecules similar to aspirin"
Upload molecular data files (CSV, SDF) directly in the chat for analysis.
Chemography is the science of mapping chemical space using dimensionality reduction techniques, particularly Generative Topographic Mapping (GTM).
Generative Topographic Mapping is a probabilistic dimensionality reduction method that projects high-dimensional molecular descriptor space onto a 2D manifold, enabling visualization and analysis of chemical space.
- Chemical Space — The abstract multidimensional space of all possible molecules
- Molecular Descriptors — Numerical representations of molecular properties
- Activity Landscapes — Maps showing how biological activity varies across chemical space
- Chemotypes — Families of molecules sharing common scaffolds
Chemography turns high-dimensional molecular data into navigable maps. With Generative Topographic Mapping (GTM), these maps are probabilistic, interpretable, and directly useful for medicinal chemistry workflows.
- Organize millions of compounds on a smooth 2D manifold
- Detect global chemotype neighborhoods and local subseries clusters
- Compare libraries, projects, or campaigns in a single reference space
- Project potency, selectivity, and ADMET endpoints onto GTM landscapes
- Reveal activity cliffs, smooth SAR trends, and underexplored regions
- Support hypothesis generation before expensive synthesis cycles
- Identify map zones enriched in known actives
- Rank candidates by neighborhood context rather than single-point similarity
- Focus triage on compounds balancing novelty and expected activity
- Quantify scaffold occupancy and chemotype diversity over the map
- Track which structural families dominate or are missing in a dataset
- Guide scaffold hopping toward productive but less crowded regions
- Monitor how analog series move through chemical space over iterations
- Connect structural edits with shifts in potency/property landscapes
- Prioritize directions likely to improve multiparameter profiles
- Couple molecular generators with GTM coordinates to sample targeted zones
- Steer generation toward desirable activity/property neighborhoods
- Filter ideas by map-consistency for better design robustness
- Benchmark projects using comparable GTM representations
- Detect redundancy across internal collections
- Highlight white spaces for strategic exploration
In short: GTM-powered chemography is not just visualization—it is a decision framework for discovery, optimization, and molecular innovation.
Reference: Bishop, C. M., Svensén, M., & Williams, C. K. (1998). GTM: The generative topographic mapping. Neural computation, 10(1), 215-234.