Active Learning Sequence Exploration via Bayesian Optimization
ALSEBO is a Python framework for navigating protein sequence space using a closed-loop active learning strategy. It combines a Variational Autoencoder (VAE) that generates a continuous latent landscape of sequences with Bayesian Optimisation (BO) to iteratively propose the most promising candidates for experimental testing.
Closed-loop active learning pipeline: a VAE-derived latent landscape feeds sequence selection, evaluation, and Bayesian optimisation, which in turn proposes the next batch of sequences.
MSA
│
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VAE ──────────────────────────────────────────────────────────────┐
│ generates a latent landscape & samples diverse sequences │
▼ │
Sequence Space │
│ featurized via DCA · ESM · latent coordinates │
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Initial Training Set │
│ diverse subset selected by t-SNE/PCA + k-means clustering │
▼ │
Wet-lab / in silico evaluation │
│ measure objective(s): fitness, stability, activity … │
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Gaussian Process Regression │
│ fits a surrogate model per objective │
▼ │
Acquisition Function (UCB) │
│ scores the unexplored sequence space │
▼ │
Next Batch ────────────────────────────────────────────────────────┘
top-k sequences recommended for the next experiment round
- Multi-objective BO — weighted scalarisation of multiple fitness objectives with configurable direction (maximise / minimise).
- Pluggable featurisation — swap between DCA (Direct Coupling Analysis), ESM protein language model embeddings, or raw VAE latent coordinates.
- Diversity-aware initialisation — t-SNE or PCA projection followed by k-means ensures the first experimental batch covers the full sequence landscape.
- Append-friendly data model — each experimental round appends to a single CSV, making it easy to resume or inspect the optimisation history.
git clone https://github.com/dulithaprasanna/ALSEBO.git
cd ALSEBO
pip install -e .See the installation guide for full details including the DCA dependency.