https://github.com/tjkessler/smiles-autoencoder
Autoencoder for SMILES strings
https://github.com/tjkessler/smiles-autoencoder
autoencoder computational-chemistry smiles-strings
Last synced: 6 months ago
JSON representation
Autoencoder for SMILES strings
- Host: GitHub
- URL: https://github.com/tjkessler/smiles-autoencoder
- Owner: tjkessler
- License: gpl-3.0
- Created: 2024-07-16T17:12:35.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-20T19:45:13.000Z (about 2 years ago)
- Last Synced: 2025-11-27T20:20:32.127Z (8 months ago)
- Topics: autoencoder, computational-chemistry, smiles-strings
- Language: Python
- Homepage:
- Size: 37.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# smiles-autoencoder
[](https://badge.fury.io/gh/tjkessler%2Fsmiles-autoencoder)
[](https://badge.fury.io/py/smiles-autoencoder)
LSTM-based autoencoders for SMILES strings
# Installation
```
$ pip install smiles-autoencoder
```
or
```
$ git clone https://gitlab.com/tjkessler/smiles-autoencoder
$ cd smiles-autoencoder
$ pip install .
```
# Usage
### One-hot encoding
```python
from smiles_autoencoder.encoding import SmilesEncoder
smiles: List[str] = [...]
encoder = SmilesEncoder()
encoder.fit(smiles)
encoded_smiles: numpy.ndarray = encoder.encode_many(smiles)
# encoded_smiles.shape == (n_smiles_strings, sequence_length, n_unique_characters)
```
### Autoencoding
```python
import torch
import torch.nn as nn
from smiles_autoencoder.model import LSTMAutoencoder
encoded_smiles = torch.tensor(encoded_smiles, dtype=torch.float32)
autoencoder = LSTMAutoencoder(
input_size=encoded_smiles.shape[2],
hidden_size=64,
latent_size=12,
num_lstm_layers=1
)
opt = torch.optim.Adam(autoencoder.parameters(), lr=0.001)
loss_crit = nn.L1Loss(reduction="sum")
for epoch in range(8):
for enc_smiles in encoded_smiles:
opt.zero_grad()
pred = autoencoder(enc_smiles)
loss = loss_crit(pred, enc_smiles)
loss.backward()
opt.step()
```
### Decoding predictions
```python
pred_smiles: torch.Tensor = autoencoder(encoded_smiles[0])
pred_smiles: str = encoder.decode(torch.round(pred_smiles).detach().numpy().astype(int))
```
# Contributing, Reporting Issues and Other Support:
To contribute to smiles-autoencoder, make a pull request. Contributions should include tests for new features added, as well as extensive documentation.
To report problems with the software or feature requests, file an issue. When reporting problems, include information such as error messages, your OS/environment and Python version.
For additional support/questions, contact Travis Kessler (travis.j.kessler@gmail.com).