merge

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}
+}
 ```