Merge branch 'yves' of https://github.com/GT4SD/gt4sd-core into yves

examples/enzeptional/README.md

@@ -1,97 +1,22 @@
-# Enzyme Optimization Experiment
+# Enzyme Optimization in Biocatalytic Reactions
 
-## Description
-This script performs an optimization experiment for enzyme sequences using different mutation strategies.
+This repository provides an exmaple on how ro run the framework for the optimization of enzymes within the context of biocatalytic reactions.
 
-## Import modules
-```python
-import logging
-import pandas as pd
-from gt4sd.frameworks.enzeptional.processing import HFandTAPEModelUtility
-from gt4sd.frameworks.enzeptional.core import SequenceMutator, EnzymeOptimizer
-from gt4sd.configuration import sync_algorithm_with_s3
-from gt4sd.configuration import GT4SDConfiguration
-configuration = GT4SDConfiguration.get_instance()
-```
+## Prerequisites
 
-## Load datasets and scorers
-```python
-sync_algorithm_with_s3("proteins/enzeptional/scorers", module="properties")
-```
-Feasibility scorer path
-```python
-scorer_path = f"{configuration.gt4sd_local_cache_path}/properties/proteins/enzeptional/scorers/feasibility/model.pkl"
-```
-## Set embedding model/tokenizer paths
-```python
-language_model_path = "facebook/esm2_t33_650M_UR50D"
-tokenizer_path = "facebook/esm2_t33_650M_UR50D"
-unmasking_model_path = "facebook/esm2_t33_650M_UR50D"
-chem_model_path = "seyonec/ChemBERTa-zinc-base-v1"
-chem_tokenizer_path = "seyonec/ChemBERTa-zinc-base-v1"
-```
-## Load protein embedding model
-```python
-protein_model = HFandTAPEModelUtility(
-        embedding_model_path=language_model_path, tokenizer_path=tokenizer_path
-    )
-```
-## Create mutation config 
-```python
-mutation_config = {
-        "type": "language-modeling",
-        "embedding_model_path": language_model_path,
-        "tokenizer_path": tokenizer_path,
-        "unmasking_model_path": unmasking_model_path,
-    }
-```
-## Set key parameters
-```python
-intervals = [(5, 10), (20, 25)]
-batch_size = 5
-top_k = 3
-substrate_smiles = "NC1=CC=C(N)C=C1"
-product_smiles = "CNC1=CC=C(NC(=O)C2=CC=C(C=C2)C(C)=O)C=C1"
+Before initiating the enzyme optimization process, execute the following command in your terminal to activate the environment:
 
-sample_sequence = "MSKLLMIGTGPVAIDQFLTRYEASCQAYKDMHQDQQLSSQFNTNLFEGDKALVTKFLEINRTLS"
+```console
+conda activate gt4sd
 ```
-## Load mutator
-```python
-mutator = SequenceMutator(sequence=sample_sequence, mutation_config=mutation_config)
-```
-## Set Optimizer
-```python
-optimizer = EnzymeOptimizer(
-    sequence=sample_sequence,
-    protein_model=protein_model,
-    substrate_smiles=substrate_smiles,
-    product_smiles=product_smiles,
-    chem_model_path=chem_model_path,
-    chem_tokenizer_path=chem_tokenizer_path,
-    scorer_filepath=scorer_path,
-    mutator=mutator,
-    intervals=intervals,
-    batch_size=batch_size,
-    top_k=top_k,
-    selection_ratio=0.25,
-    perform_crossover=True,
-    crossover_type="single_point",
-    concat_order=["substrate", "sequence", "product"],
-)
-```
-## Define optmization parameters
-```python
-num_iterations = 3
-num_sequences = 5
-num_mutations = 5
-time_budget = 3600
-```
-## Optimize
-```python
-optimized_sequences, iteration_info = optimizer.optimize(
-    num_iterations=num_iterations,
-    num_sequences=num_sequences,
-    num_mutations=num_mutations,
-    time_budget=time_budget,
-)
+
+## Citation
+
+```bibtex
+@inproceedings{teukam2023enzyme,
+  title={Enzyme optimization via a generative language modeling-based evolutionary algorithm},
+  author={Teukam, Yves Gaetan Nana and Grisoni, Francesca and Manica, Matteo and Zipoli, Federico and Laino, Teodoro},
+  booktitle={American Chemical Society (ACS) Spring Meeting},
+  year={2023}
+}
 ```

examples/enzeptional/example_enzeptional.py

@@ -0,0 +1,84 @@
+import logging
+import pandas as pd
+from gt4sd.frameworks.enzeptional.processing import HFandTAPEModelUtility
+from gt4sd.frameworks.enzeptional.core import SequenceMutator, EnzymeOptimizer
+from gt4sd.configuration import GT4SDConfiguration, sync_algorithm_with_s3
+
+
+def initialize_environment():
+    """Synchronize with GT4SD S3 storage and set up the environment."""
+    # NOTE: For those interested in optimizing kcat values, it is important to adjust the scorer path to reflect this focus, thereby selecting the appropriate model for kcat optimization. The specification of the scaler, located within the same directory as the `scorer.pkl`, is mandatory for accurate model performance.
+    configuration = GT4SDConfiguration.get_instance()
+    sync_algorithm_with_s3("proteins/enzeptional/scorers", module="properties")
+    return f"{configuration.gt4sd_local_cache_path}/properties/proteins/enzeptional/scorers/feasibility/model.pkl"
+
+
+def load_experiment_parameters():
+    """Load experiment parameters from a CSV file."""
+    df = pd.read_csv("data.csv").iloc[1]
+    return df["substrates"], df["products"], df["sequences"], eval(df["intervals"])
+
+
+def setup_optimizer(
+    substrate_smiles, product_smiles, sample_sequence, intervals, scorer_path
+):
+    """Set up and return the optimizer with all necessary components configured."""
+    model_tokenizer_paths = "facebook/esm2_t33_650M_UR50D"
+    chem_paths = "seyonec/ChemBERTa-zinc-base-v1"
+
+    protein_model = HFandTAPEModelUtility(
+        embedding_model_path=model_tokenizer_paths, tokenizer_path=model_tokenizer_paths
+    )
+    mutation_config = {
+        "type": "language-modeling",
+        "embedding_model_path": model_tokenizer_paths,
+        "tokenizer_path": model_tokenizer_paths,
+        "unmasking_model_path": model_tokenizer_paths,
+    }
+
+    mutator = SequenceMutator(sequence=sample_sequence, mutation_config=mutation_config)
+    optimizer_config = {
+        "sequence": sample_sequence,
+        "protein_model": protein_model,
+        "substrate_smiles": substrate_smiles,
+        "product_smiles": product_smiles,
+        "chem_model_path": chem_paths,
+        "chem_tokenizer_path": chem_paths,
+        "scorer_filepath": scorer_path,
+        "mutator": mutator,
+        "intervals": intervals,
+        "batch_size": 5,
+        "top_k": 3,
+        "selection_ratio": 0.25,
+        "perform_crossover": True,
+        "crossover_type": "single_point",
+        "concat_order": ["substrate", "sequence", "product"],
+    }
+    return EnzymeOptimizer(**optimizer_config)
+
+
+def optimize_sequences(optimizer):
+    """Optimize sequences using the configured optimizer."""
+    return optimizer.optimize(
+        num_iterations=3, num_sequences=5, num_mutations=5, time_budget=3600
+    )
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    scorer_path = initialize_environment()
+    (
+        substrate_smiles,
+        product_smiles,
+        sample_sequence,
+        intervals,
+    ) = load_experiment_parameters()
+    optimizer = setup_optimizer(
+        substrate_smiles, product_smiles, sample_sequence, intervals, scorer_path
+    )
+    optimized_sequences, iteration_info = optimize_sequences(optimizer)
+    logging.info("Optimization completed.")
+
+
+if __name__ == "__main__":
+    main()