### Install Package

Source: https://context7.com/lucidrains/evolutionary-design-molecules/llms.txt

Install the library using pip.

```bash
pip install evolutionary-design-molecules
```

--------------------------------

### Install from Source

Source: https://context7.com/lucidrains/evolutionary-design-molecules/llms.txt

Clone the repository and install from source.

```bash
git clone https://github.com/lucidrains/evolutionary-design-molecules
cd evolutionary-design-molecules
pip install -e .
```

--------------------------------

### EvolveDesignMoleculesInSilico Initialization

Source: https://context7.com/lucidrains/evolutionary-design-molecules/llms.txt

Initialize the main class for end-to-end molecular design, combining autoencoder and genetic algorithm. The full pipeline is pending implementation.

```python
import torch
from evolutionary_design_molecules import EvolveDesignMoleculesInSilico

# Initialize the complete molecular design system
designer = EvolveDesignMoleculesInSilico()

# The full pipeline (work-in-progress):
# 1. Encode initial molecules to latent binary codes
# 2. Run genetic algorithm evolution with fitness evaluation
# 3. Decode evolved latent codes back to molecular structures

# Example usage pattern (implementation pending):
# initial_molecules = load_molecules("seed_compounds.sdf")
# optimized_molecules = designer(
#     initial_molecules,
#     fitness_fn=binding_affinity_predictor,
#     generations=10000
# )
```

--------------------------------

### Genetic Algorithm Evolution

Source: https://context7.com/lucidrains/evolutionary-design-molecules/llms.txt

Run the genetic algorithm to evolve molecular representations. Requires an initial population pool and a fitness calculation function.

```python
import torch
from evolutionary_design_molecules import evolve

# Initialize a random population pool of molecular representations
# Shape: (population_size, gene_length) where genes are integer codes 0-255
population_size = 100
gene_length = 64
init_pool = torch.randint(0, 256, (population_size, gene_length))

# Define a fitness function that evaluates molecular properties
# Higher fitness values indicate better molecules
def calc_fitness(pool: torch.Tensor) -> torch.Tensor:
    # Example: fitness based on some molecular property score
    # In practice, this would use a trained model or docking simulation
    # Returns tensor of shape (population_size,)
    return pool.float().mean(dim=-1)  # Placeholder fitness

# Run the evolutionary algorithm
final_population = evolve(
    init_pool=init_pool,
    calc_fitness=calc_fitness,
    generations=1000,           # Number of generations to evolve
    population=100,             # Population size per generation
    mutation_rate=0.04,         # Probability of gene mutation
    frac_survive_fittest=0.25,  # Fraction of fittest individuals to keep
    frac_tournament=0.25,       # Fraction for tournament selection
    frac_elite=0.05,            # Fraction of elite individuals (pass unchanged)
)

# final_population contains the evolved molecular representations
print(f"Final population shape: {final_population.shape}")
```

--------------------------------

### Molecular Autoencoder Initialization

Source: https://context7.com/lucidrains/evolutionary-design-molecules/llms.txt

Initialize the MolecularAutoencoder for encoding and decoding molecular structures. The full implementation is a work-in-progress.

```python
import torch
from torch import nn
from evolutionary_design_molecules import MolecularAutoencoder

# Initialize the molecular autoencoder
autoencoder = MolecularAutoencoder()

# Example molecular input (implementation-specific format)
# Could be SMILES embeddings, molecular graphs, or 3D coordinates
batch_size = 32
input_dim = 128
molecular_input = torch.randn(batch_size, input_dim)

# Encode molecules to latent space and decode back
# Note: Full implementation is work-in-progress
# reconstructed = autoencoder(molecular_input)
```

--------------------------------

### Check for Value Existence

Source: https://context7.com/lucidrains/evolutionary-design-molecules/llms.txt

The `exists` utility function checks if a value is not None. It's useful for handling optional parameters.

```python
from evolutionary_design_molecules.evolutionary_design_molecules import exists

# Check if a value exists (is not None)
value = None
if not exists(value):
    value = "default"

optional_param = torch.randn(10)
if exists(optional_param):
    print(f"Parameter shape: {optional_param.shape}")
```

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