torch-molecule 0.1.7
Deep learning packages for molecular discovery with a simple sklearn-style interface
License: MIT
Requires Python: >=3.8

<p align="center">
  <img src="https://github.com/liugangcode/torch-molecule/blob/main/assets/logo.png?raw=true" alt="torch-molecule logo" width="600"/>
</p>

<p align="center">
  <a href="https://github.com/liugangcode/torch-molecule">
    <img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub Repository">
  </a>
  <a href="https://liugangcode.github.io/torch-molecule/">
    <img src="https://img.shields.io/badge/Documentation-Online-brightgreen?logo=readthedocs" alt="Documentation">
  </a>
</p>

<p align="center">
  <b>Deep learning for molecular discovery with a simple sklearn-style interface</b>
</p>

---

`torch-molecule` is a package that facilitates molecular discovery through deep learning, featuring a user-friendly, `sklearn`-style interface. It includes model checkpoints for efficient deployment and benchmarking across a range of molecular tasks. The package focuses on three main components: **Predictive Models**, **Generative Models**, and **Representation Models**, which make molecular AI models easy to implement and deploy. 

<p align="center">
  <img src="https://github.com/liugangcode/torch-molecule/blob/v0.1.7/assets/cover.png?raw=true" alt="scikit-learn vs torch-molecule comparison" width="800"/>
</p>

See the [List of Supported Models](#list-of-supported-models) section for all available models.

## Installation

1. **Create a Conda environment**:
   ```bash
   conda create --name torch_molecule python=3.11.7
   conda activate torch_molecule
   ```

2. **Install using pip**:
   ```bash
   pip install torch-molecule
   ```

3. **Install from source for the latest version**:

   Clone the repository:
   ```bash
   git clone https://github.com/liugangcode/torch-molecule
   cd torch-molecule
   ```

   Install:
   ```bash
   pip install .
   ```

### Additional Packages

| Model | Required Packages |
|-------|-------------------|
| HFPretrainedMolecularEncoder | transformers |
| BFGNNMolecularPredictor | torch-scatter |
| GRINMolecularPredictor | torch-scatter |
| GRINMolecularPredictor (if enable `repetition_augmentation=True`) | CombineMols |

**For models that require `torch-scatter`**: Install using the following command: `pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html`, e.g.,

> `pip install torch-scatter -f https://data.pyg.org/whl/torch-2.7.1+cu128.html`

**For models that require `transformers`:** `pip install transformers`

## Usage

> More examples can be found in the `examples` and `tests` folders.

`torch-molecule` supports applications in broad domains from chemistry, biology, to materials science. To get started, you can load prepared datasets from `torch_molecule.datasets` (updated after v0.1.3):
| Dataset | Description | Function |
|---------|-------------|----------|
| qm9 | Quantum chemical properties (DFT level) | `load_qm9` |
| chembl2k | Bioactive molecules with drug-like properties | `load_chembl2k` |
| broad6k | Bioactive molecules with drug-like properties | `load_broad6k` |
| toxcast | Toxicity of chemical compounds | `load_toxcast` |
| admet | Chemical absorption, distribution, metabolism, excretion, and toxicity | `load_admet` |
| gasperm | Six gas permeability properties for polymeric materials | `load_gasperm` |
| zinc250k | A common subset of ZINC dataset, which does not have labels and could be used for unconditional generation or virtual screening | `load_zinc250k` |

```python
from torch_molecule.datasets import load_qm9

# local_dir is the local path where the dataset will be saved
molecular_data = load_qm9(local_dir='torchmol_data')
smiles_list, property_np_array = molecular_data.data, molecular_data.target

# len(smiles_list): 133885
# Property array shape: (133885, 1)

# load_qm9 returns the target "gap" by default, but you can adjust it by passing new target_cols
target_cols = ['homo', 'lumo', 'gap']
molecular_data = load_qm9(local_dir='torchmol_data', target_cols=target_cols)
smiles_list, property_np_array = molecular_data.data, molecular_data.target

# the target could be None if loading an unlabeled dataset
from torch_molecule.datasets import load_zinc250k
molecular_data = load_zinc250k(local_dir='torchmol_data')
smiles_list = molecular_data.data
assert molecular_data.target is None
```

(We are actively adding more datasets. We welcome your suggestions and contributions on your datasets!)

### Fit a Model

After preparing the dataset, we can easily fit a model similar to how we use sklearn (actually, the coding is even simpler than sklearn, as we still need to do feature engineering in sklearn to convert molecule SMILES into vectors):

```python
from torch_molecule import GREAMolecularPredictor

split = int(0.8 * len(smiles_list))

grea = GREAMolecularPredictor(
    num_task=num_task,
    task_type="regression",
    evaluate_higher_better=False,
    verbose="progress_bar" #or "print_statement" recommended for jupyter notebooks, or "none"
)

# Fit with automatic hyperparameter tuning with 10 attempts, or implement .fit() with the default/manual hyperparameters
grea.autofit(
    X_train=smiles_list[:split],
    y_train=property_np_array[:split],
    X_val=smiles_list[split:],
    y_val=property_np_array[split:],
    n_trials=10,
)
```

### Checkpoints

`torch-molecule` provides checkpoint functions that can be interacted with on Hugging Face:

```python
from torch_molecule import GREAMolecularPredictor

repo_id = "user/repo_id"  # replace with your own Hugging Face username and repo_id

# Save the trained model to Hugging Face
grea.save_to_hf(
    repo_id=repo_id,
    task_id="qm9_grea",
    commit_message="Upload qm9_grea",
    private=False
)

# Load a pretrained checkpoint from Hugging Face
model = GREAMolecularPredictor()
model.load_from_hf(repo_id=repo_id, local_cache=f"{model_dir}/GREA_{task_name}.pt")

# Adjust model parameters and make predictions
model.set_params(verbose='none')
predictions = model.predict(smiles_list)
```

Or you can save the model to a local path:

```python
grea.save_to_local("qm9_grea.pt")

new_model = GREAMolecularPredictor()
new_model.load_from_local("qm9_grea.pt")
```

## List of Supported Models

### Predictive Models

| Model                | Reference           |
|----------------------|---------------------|
| GRIN                 | [Learning Repetition-Invariant Representations for Polymer Informatics. NeurIPS 2025.](https://arxiv.org/abs/2505.10726) |
| BFGNN                 | [Graph neural networks extrapolate out-of-distribution for shortest paths. March 2025](https://arxiv.org/abs/2503.19173) |
| SGIR                 | [Semi-Supervised Graph Imbalanced Regression. KDD 2023](https://dl.acm.org/doi/10.1145/3580305.3599497) |
| GREA                | [Graph Rationalization with Environment-based Augmentations. KDD 2022](https://dl.acm.org/doi/abs/10.1145/3534678.3539347) |
| DIR                  | [Discovering Invariant Rationales for Graph Neural Networks. ICLR 2022](https://arxiv.org/abs/2201.12872) |
| SSR                  | [SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks. NeurIPS 2022](https://arxiv.org/abs/2206.07096) |
| IRM                  | [Invariant Risk Minimization (2019)](https://arxiv.org/abs/1907.02893) |
| RPGNN                | [Relational Pooling for Graph Representations. ICML 2019](https://arxiv.org/abs/1903.02541) |
| GNNs                 | [Graph Convolutional Networks. ICLR 2017](https://arxiv.org/abs/1609.02907) and [Graph Isomorphism Network. ICLR 2019](https://arxiv.org/abs/1810.00826) |
| Transformer (SMILES) | [Transformer (Attention is All You Need. NeurIPS 2017)](https://arxiv.org/abs/1706.03762) based on SMILES strings |
| LSTM (SMILES)        | [Long short-term memory (Neural Computation 1997)](https://ieeexplore.ieee.org/abstract/document/6795963) based on SMILES strings |

### Generative Models

| Model      | Reference           |
|------------|---------------------|
| DeFoG      | [DeFoG: Discrete Flow Matching for Graph Generation. ICML 2025](https://openreview.net/pdf?id=KPRIwWhqAZ) |
| Graph DiT  | [Graph Diffusion Transformers for Multi-Conditional Molecular Generation. NeurIPS 2024](https://openreview.net/forum?id=cfrDLD1wfO) |
| DiGress    | [DiGress: Discrete Denoising Diffusion for Graph Generation. ICLR 2023](https://openreview.net/forum?id=UaAD-Nu86WX) |
| GDSS       | [Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations. ICML 2022](https://proceedings.mlr.press/v162/jo22a/jo22a.pdf) |
| MolGPT     | [MolGPT: Molecular Generation Using a Transformer-Decoder Model. Journal of Chemical Information and Modeling 2021](https://pubs.acs.org/doi/10.1021/acs.jcim.1c00600) |
| JTVAE      | [Junction Tree Variational Autoencoder for Molecular Graph Generation. ICML 2018.](https://proceedings.mlr.press/v80/jin18a) |
| GraphGA    | [A Graph-Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules. Journal of Chemical Information and Computer Sciences 2004](https://pubs.acs.org/doi/10.1021/ci034290p) |
| LSTM (SMILES)        | [Long short-term memory (Neural Computation 1997)](https://ieeexplore.ieee.org/abstract/document/6795963) based on SMILES strings |

### Representation Models

| Model        | Reference           |
|--------------|---------------------|
| MoAMa        | [Motif-aware Attribute Masking for Molecular Graph Pre-training. LoG 2024](https://arxiv.org/abs/2309.04589) |
| GraphMAE        | [GraphMAE: Self-Supervised Masked Graph Autoencoders. KDD 2022](https://arxiv.org/abs/2205.10803) |
| AttrMasking  | [Strategies for Pre-training Graph Neural Networks. ICLR 2020](https://arxiv.org/abs/1905.12265) |
| ContextPred  | [Strategies for Pre-training Graph Neural Networks. ICLR 2020](https://arxiv.org/abs/1905.12265) |
| EdgePred     | [Strategies for Pre-training Graph Neural Networks. ICLR 2020](https://arxiv.org/abs/1905.12265) |
| InfoGraph    | [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. ICLR 2020](https://arxiv.org/abs/1908.01000) |
| Supervised   | Supervised pretraining |
| Pretrained   | [GPT2-ZINC-87M](https://huggingface.co/entropy/gpt2_zinc_87m): GPT-2 based model (87M parameters) pretrained on ZINC dataset with ~480M SMILES strings. <br> [RoBERTa-ZINC-480M](https://huggingface.co/entropy/roberta_zinc_480m): RoBERTa based model (102M parameters) pretrained on ZINC dataset with ~480M SMILES strings. <br> [UniKi/bert-base-smiles](https://huggingface.co/unikei/bert-base-smiles): BERT model pretrained on SMILES strings. <br> [ChemBERTa-zinc-base-v1](https://huggingface.co/seyonec/ChemBERTa-zinc-base-v1): RoBERTa model pretrained on ZINC dataset with ~100k SMILES strings. <br> ChemBERTa series: Available in multiple sizes and training objectives (MLM/MTR). [ChemBERTa-5M-MLM](https://huggingface.co/DeepChem/ChemBERTa-5M-MLM), [ChemBERTa-5M-MTR](https://huggingface.co/DeepChem/ChemBERTa-5M-MTR), [ChemBERTa-10M-MLM](https://huggingface.co/DeepChem/ChemBERTa-10M-MLM), [ChemBERTa-10M-MTR](https://huggingface.co/DeepChem/ChemBERTa-10M-MTR), [ChemBERTa-77M-MLM](https://huggingface.co/DeepChem/ChemBERTa-77M-MLM), [ChemBERTa-77M-MTR](https://huggingface.co/DeepChem/ChemBERTa-77M-MTR). <br> ChemGPT series: GPT-Neo based models pretrained on PubChem10M dataset with SELFIES strings. [ChemGPT-1.2B](https://huggingface.co/ncfrey/ChemGPT-1.2B), [ChemGPT-4.7B](https://huggingface.co/ncfrey/ChemGPT-4.7M), [ChemGPT-19B](https://huggingface.co/ncfrey/ChemGPT-19M). |

## Acknowledgements

The project template was adapted from [https://github.com/lwaekfjlk/python-project-template](https://github.com/lwaekfjlk/python-project-template). We thank the authors for their contribution to the open-source community.