README
SEGO
SEGO (Sample-Efficient Generative Optimization) is a language model-based framework for de novo small molecule design. It combines autoregressive molecular generation with Bayesian optimization to maximize oracle scores with minimal oracle calls.
Installation
Requirements: Python 3.10, CUDA-enabled GPU (CPU works but is much slower), Linux.
# 1. Install Conda, then clone the repository
git clone <repo-url> && cd sego
# 2. Create and activate the environment
conda env create -f segobabel_env_nobuilds.yaml
conda activate segobabel
Common installation issues
GLIBCXX_3.4.29not found:conda uninstall openbabel conda install gcc_linux-64 gxx_linux-64 conda install -c conda-forge openbabelcausal-conv1d/mamba-ssmbuild errors: see Issue #1.
Running SEGO
SEGO is launched via saturn.py with a JSON configuration file:
python saturn.py config.json
Configuration
The JSON config controls all aspects of the run. Key parameters under reinforcement_learning:
| Parameter | Description |
|---|---|
sego | true to enable SEGO's BO-guided inner loop |
inner_loop_mode | "oracle_anchored" (oracle-supervised reset), "continuous" (full memory), or "fresh" (clean slate each round) |
inner_loop_rounds | Number of rounds where the generator is optimized against the surrogate |
sampled_library_size | Molecules sampled from the agent for selection by the GP |
inner_loop_diversity_filter | Whether to apply diversity filtering inside the inner loop |
Default configs are in configs/: saturn.json for a plain augmented-memory run and gollum.yaml for standalone Gollum BO. Additional example configs and pretrained checkpoint models are in experimental_reproduction/.
Acknowledgements
Saturn code adapted from:
- Saturn (preprint)](https://arxiv.org/abs/2405.17066) — git
fee0179
Gollum code adapted from:
- Gollum (preprint) (https://arxiv.org/abs/2504.06265) - https://github.com/schwallergroup/gollum
Mamba architecture code adapted from:
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README
SEGO
SEGO (Sample-Efficient Generative Optimization) is a language model-based framework for de novo small molecule design. It combines autoregressive molecular generation with Bayesian optimization to maximize oracle scores with minimal oracle calls.
Installation
Requirements: Python 3.10, CUDA-enabled GPU (CPU works but is much slower), Linux.
# 1. Install Conda, then clone the repository
git clone <repo-url> && cd sego
# 2. Create and activate the environment
conda env create -f segobabel_env_nobuilds.yaml
conda activate segobabel
Common installation issues
GLIBCXX_3.4.29not found:conda uninstall openbabel conda install gcc_linux-64 gxx_linux-64 conda install -c conda-forge openbabelcausal-conv1d/mamba-ssmbuild errors: see Issue #1.
Running SEGO
SEGO is launched via saturn.py with a JSON configuration file:
python saturn.py config.json
Configuration
The JSON config controls all aspects of the run. Key parameters under reinforcement_learning:
| Parameter | Description |
|---|---|
sego | true to enable SEGO's BO-guided inner loop |
inner_loop_mode | "oracle_anchored" (oracle-supervised reset), "continuous" (full memory), or "fresh" (clean slate each round) |
inner_loop_rounds | Number of rounds where the generator is optimized against the surrogate |
sampled_library_size | Molecules sampled from the agent for selection by the GP |
inner_loop_diversity_filter | Whether to apply diversity filtering inside the inner loop |
Default configs are in configs/: saturn.json for a plain augmented-memory run and gollum.yaml for standalone Gollum BO. Additional example configs and pretrained checkpoint models are in experimental_reproduction/.
Acknowledgements
Saturn code adapted from:
- Saturn (preprint)](https://arxiv.org/abs/2405.17066) — git
fee0179
Gollum code adapted from:
- Gollum (preprint) (https://arxiv.org/abs/2504.06265) - https://github.com/schwallergroup/gollum
Mamba architecture code adapted from:
Similar repos
Languages
Python
93.2%
Jupyter Notebook
6.8%