Cleaned Enzeptional

setup.cfg

@@ -84,6 +84,9 @@ gt4sd =
     training_pipelines/tests/*json
     training_pipelines/tests/*smi
     frameworks/crystals_rfc/*.csv
+    frameworks/enzeptional/tests/*pkl
+    frameworks/enzeptional/tests/*json
+    frameworks/enzeptional/mutation_model/*
 
 [options.extras_require]
 extras =

src/gt4sd/frameworks/enzeptional/README.md

@@ -0,0 +1,137 @@
+<!--
+MIT License
+
+Copyright (c) 2023 GT4SD team
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+-->
+
+# Integrating Genetic Algorithms and Language Models for Enhanced Enzyme Design
+
+## Overview
+This study introduces a framework that combines LLMs with genetic algorithms (GAs) to optimize enzymes. LLMs are trained on a large dataset of protein sequences to learn the underlying statistical patterns and relationships. This knowledge is then leveraged by GAs to efficiently search for sequences with improved catalytic performance. It includes the capability to work with transition matrices for suggesting mutations.
+
+## Requirements
+- Python 3.6 or higher
+- PyTorch
+- Hugging Face's Transformers
+- TAPE (Tasks Assessing Protein Embeddings)
+- NumPy
+- Joblib
+- Logging module
+- xgboost (optional)
+
+## Installation
+Ensure all required libraries are installed. You can install them using pip:
+```bash
+pip install torch transformers numpy joblib xgboost
+```
+
+## Usage
+1. **Setting Global Cache Directories**: The script sets cache directories for Transformers and Torch.
+2. **Model Cache**: Handles caching of models to avoid reloading.
+3. **Utility Functions and Classes**:
+    - `get_device`: Determines if CUDA is available for PyTorch.
+    - `StringEmbedding`: An abstract class for embedding strings.
+    - `HFandTAPEModelUtility`: Handles embeddings for both Hugging Face and TAPE models.
+4. **Mutation Strategies**:
+    - `MutationStrategy`: An abstract base class for mutation strategies.
+    - `LanguageModelMutationStrategy`: Implements mutation using language models.
+    - `TransitionMatrixMutationStrategy`: Uses a transition matrix for mutations.
+5. **Sequence Mutator**: Generates mutations for protein sequences.
+6. **Sequence Scorer**: Scores sequences using embeddings from protein and chemical models.
+7. **Protein Sequence Optimizer**: Implements the genetic algorithm for protein sequence optimization. It handles the selection, crossover, and scoring of sequences over multiple iterations.
+
+### Example Usage
+```python
+# Set Up Model Paths
+language_model_path = "Rostlab/prot_bert"
+tokenizer_path = "Rostlab/prot_bert"
+unmasking_model_path = "Rostlab/prot_bert"
+chem_model_path = "Rostlab/prot_bert" 
+chem_tokenizer_path = "Rostlab/prot_bert"
+
+protein_model = HFandTAPEModelUtility(embedding_model_path=language_model_path,
+                                      tokenizer_path=tokenizer_path,)
+
+# Mutation Configuration
+mutation_config = {
+    "type": "language-modeling",
+    "embedding_model_path": language_model_path,
+    "tokenizer_path": tokenizer_path,
+    "unmasking_model_path": unmasking_model_path
+}
+
+# Define Parameters
+intervals = [[5, 10], [20, 25]]
+batch_size = 5
+top_k = 3
+substrate_smiles = "CCCO"  # Replace with actual substrate SMILES
+product_smiles = "CCCO"  # Replace with actual product SMILES
+
+# Initialize Sequence Mutator
+sample_sequence = "WLSNIDMILRSPYSHTGAVLIYKQPDNNEDNIHPSSSMYFDANILIEALSKALVP"
+mutator = SequenceMutator(sequence=sample_sequence, mutation_config=mutation_config)
+
+# Initialize Protein Sequence Optimizer
+optimizer = ProteinSequenceOptimizer(
+    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,
+    mutator=mutator,
+    intervals=intervals,
+    batch_size=batch_size,
+    top_k=top_k,
+    selection_ratio=0.5,
+    perform_crossover=True,
+    crossover_type="single_point",
+    concat_order=["substrate", "sequence", "product"]
+)
+
+# Run optimization
+optimized_sequences, iteration_info = optimizer.optimize(
+    num_iterations=5,
+    num_sequences=50,
+    num_mutations=5,
+    time_budget=3600
+)
+
+# Output results
+for i in optimized_sequences:
+    seq = i["sequence"]
+    score = i["score"]
+    print(f"Sequence: {seq}, Score: {score}")
+
+print(iteration_info)
+```
+
+## Customization
+- Modify `intervals` to specify mutation regions in the sequence.
+- Adjust `batch_size`, `top_k`, `selection_ratio`, and `crossover_type` for different optimization strategies.
+- Change `concat_order` to alter the order of sequence, substrate, and product in the final embedding for scoring.
+- Use `time_budget` to set a maximum time limit for each optimization iteration.
+
+## Notes
+- Ensure the paths to the models and tokenizers are correctly set.
+- The script is designed for flexibility and can be adapted to different models and optimization strategies.
+- For extensive usage, consider parallelizing or distributing the computation, especially for large-scale optimizations.
+

src/gt4sd/frameworks/enzeptional/__init__.py

@@ -1,7 +1,7 @@
 #
 # MIT License
 #
-# Copyright (c) 2022 GT4SD team
+# Copyright (c) 2023 GT4SD team
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
@@ -21,9 +21,9 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 #
-"""enzeptional - ENZymE OPTImizatiON for biocatALysis.
+"""enzeptional - Enzyme Optimization for biocatalysis.
 
 Module for enzyme optimization.
 """
 
-from .optimization import EnzymeOptimizer  # noqa: F401
+from .core import EnzymeOptimizer  # noqa: F401