LiTEN

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Equivariant neural network featuring Tensorized Quadrangle Attention

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# LiTEN
![Static Badge](https://img.shields.io/badge/release-v%200.1.2-blue?label=release&color=blue) ![Static Badge](https://img.shields.io/badge/License-MIT%202.0-%23006400) [![DOI](https://zenodo.org/badge/1006324923.svg)](https://doi.org/10.5281/zenodo.18607448)

## 🌟 About LiTEN
**LiTEN (Learning Interactions with Tensorized Equivariant Networks)** is a novel equivariant neural network designed for high-precision, efficient 3D molecular property prediction. It introduces the **Tensorized Quadrangle Attention (TQA)** mechanism, which operates directly in Cartesian space, enabling accurate modeling of many-body interactions **without relying on spherical harmonics**, leading to significant computational savings.

We further provide **LiTEN-FF**, a **foundation model for biomolecular force fields**, pretrained and fine-tuned to support a wide range of tasks in both **vacuum and solvated environments**.(https://github.com/lingcon01/LiTEN-FF/tree/master)
![workflow](https://github.com/user-attachments/assets/ffc2d515-a0c0-4a5f-ab85-a9edc284f8f9)

## 🔬 Applications

LiTEN-FF is designed to support **end-to-end molecular simulation and drug discovery pipelines**, and can be readily applied to:

* 🧬 **Conformer geometry optimization**
* 📈 **Free energy surface (FES) construction**
* 🔍 **Dihedral angle scanning**
* 🧩 **Batch conformer search**
* 📐 **Internal coordinate distribution analysis**
* ⚛️ **Large-scale molecular dynamics with ab initio accuracy**

More application scenarios are being explored and will be continuously added to this repository.

## 🧠 Future Plans

In upcoming versions of LiTEN-FF, we aim to:

* 🔄 Incorporate **knowledge distillation** for model compression and acceleration.
* ⚡ Enhance inference speed and scalability.
* 🔧 Expand compatibility with **protein folding** and **molecular generation models**.

## 📦 Installation

You can set up the **LiTEN** environment in one of the following ways.

### ⚡ **Option 1. Restore from pre-packed environment (Recommended)**

You can download the pre-packed conda environment from [Zenodo](https://zenodo.org/records/17149388) and restore it manually under your conda environments directory to ensure full reproducibility and consistency across different systems.

```bash
# 1. Locate your conda environments path
# (Usually: ~/miniconda3/envs or ~/anaconda3/envs)
cd ~/miniconda3/envs

# 2. Create a new directory for the environment
mkdir liten
cd liten

# 3. Extract the environment archive into this directory
tar -xzf /path/to/liten_env.tar.gz

# 4. Activate the restored environment
conda activate ~/miniconda3/envs/liten
```
> 🧩 This method avoids dependency resolution and ensures bitwise reproducibility of the LiTEN environment.

### 🧠 **Option 2. Create from `liten_env.sh`**

This is the standard and most reproducible way to install all dependencies.

```bash
# 1. Clone the repository
git clone https://github.com/lingcon01/LiTEN.git
cd LiTEN

# 2. Create and activate the conda environment
bash liten_env.sh
conda activate liten
```

###  **Option 3. Create from `environment.yml`**

This is the standard and most reproducible way to install all dependencies.

```bash
# 1. Clone the repository
git clone https://github.com/lingcon01/LiTEN.git
cd LiTEN

# 2. Create and activate the conda environment
conda env create -f environment.yml
conda activate liten
```

## 📁 Project Structure

```
LiTEN/
├── config/                # training params of rmd17, md22 and chignolin
├── model/                 # Model architecture (LiTEN)
├── dataset/               # Datasets and loaders
├── scripts/               # Training and inference scripts
├── utils/                 # Need for use
└── README.md
```

## 📊 Reproduce Our Results

To reproduce the benchmark results reported in our paper (on datasets such as **rMD17**, **MD22**, and **Chignolin**), we provide the corresponding training scripts in the repository.
Please download the datasets from [Zenodo](https://zenodo.org/records/17142012), create a `data` directory under the `LiTEN` root folder, and extract all files into this directory.

### 🧪 Benchmark Evaluation Example

```bash
# Examples: train and test on rmd17. (unit: meV and meV/A)
python scripts/train_rmd17.py \
    --molecule raspirin \
    --num_train 950 \
    --num_val 50 \
    --device cuda
```
```bash
# Examples: train and test on md22. (unit: meV and meV/A)
python scripts/train_md22.py \
    --molecule md22_AT_AT \
    --num_train 2500 \
    --num_val 500 \
    --device cuda
```
```bash
# Examples: train and test on chignolin. (unit: kcal/mol and kcal/mol/A)
python scripts/train_chignolin.py \
    --molecule chignolin \
    --device cuda
```
## ⚙️ Utilities Based on LiTEN-FF

We provide a suite of practical utilities built upon the pretrained LiTEN-FF model for downstream molecular simulation tasks, including **geometry optimization**, **molecular dynamics**, and **batch conformer generation**. These scripts are based on [ASE (Atomic Simulation Environment)](https://wiki.fysik.dtu.dk/ase/) and can be easily adapted to custom molecules and workflows.

For LiTEN-FF, we have made the corresponding Conda package ([Zenodo link](https://zenodo.org/records/17475560)), with the source code hosted on https://github.com/lingcon01/LiTEN-FF/tree/master.

---

### 🧬 ASE-OPT: Geometry Optimization

**Function**:
Performs energy minimization of molecules using LiTEN-FF as a force field backend via ASE's `BFGS` optimizer or other algorithms.

**Example Usage**:

```bash
# You can choose nablaDFT model or SPICE model
python md_scripts/LiTEN_OPT.py \
    --model_name nablaDFT  \
```

**Key Features**:

* Supports SDF, XYZ, or PDB input.
* Compatible with vacuum or solvated structures.
* Output optimized geometries in the same format.

---

### 🔁 ASE-MD: Molecular Dynamics

**Function**:
Runs molecular dynamics (MD) simulations under NVE, NVT, or Langevin dynamics using LiTEN-FF as the force provider. (Please note that the speed comparisons among different force field models in the article were conducted on single molecules. This is because, under periodic aqueous environments, the main computational bottleneck lies in the neighbor list construction, making it difficult to directly compare the intrinsic speed of each model.)

**Example Usage**:

```bash
# The SPICE model is compatible with both periodic solvent systems and vacuum-phase systems, whereas the nablaDFT model is limited to vacuum systems only.
python md_scripts/LiTEN_MD.py \
    --model_name SPICE  \
    --temperature 300 \
    --timestep 1 \
    --steps 1000000 \
```

**Options**:

* `--temperature`: simulation temperature (in Kelvin)
* `--timestep`: timestep in femtoseconds
* `--steps`: number of simulation steps
* `--thermostat`: use `langevin`, `nvt`, or `nve`

---

### 🧩 Batch-confgen: Conformer Generation

**Function**:
Generates low-energy conformers for multiple molecules using LiTEN-FF with geometry optimization for each initial guess. Suitable for 3D dataset construction and screening.

**Example Usage**:

```bash
python md_scripts/LiTEN_Confgen.py \
    --model_name nablaDFT \
```

**Features**:

* Automatically embeds and optimizes 3D structures.
* Can handle hundreds to thousands of molecules in batch.
* Parallelized for performance on multicore systems.

---

## 🧪 Requirements

These scripts require the following dependencies:

```bash
pip install ase rdkit torch numpy tqdm
```

For GPU acceleration, ensure that PyTorch is installed with CUDA support.

## 🙏 Acknowledgement

This work was supported by **Drug Design and Discovery Laboratory, Zhejiang University**, and inspired by previous developments in equivariant neural networks and deep potential models. We especially acknowledge the creators of **PaiNN**, **MACE**, and **VisNet**, which provided critical insights and technical foundations for our development of LiTEN and LiTEN-FF.

We also thank the maintainers of the [ASE](https://wiki.fysik.dtu.dk/ase/), [RDKit](https://www.rdkit.org/), and [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/) projects for their indispensable tools.

---

## 📖 Citation

If you use **LiTEN** or **LiTEN-FF** in your research or projects, please cite:

```bibtex
@article{su2025liten,
  title   = {A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention},
  author  = {Su, Qun},
  journal = {arXiv preprint arXiv:2507.00884},
  year    = {2025},
  url     = {https://arxiv.org/abs/2507.00884},
  note    = {Available at \url{https://github.com/lingcon01/LiTEN}}
}

```

> 📌 The arXiv link will be updated upon public release of the paper.
> https://arxiv.org/abs/2507.00884
---

## 👥 Contributors

* **Qun Su** ([@lingcon01](https://github.com/lingcon01)) – Lead developer, model architecture, training pipeline, downstream benchmarking
* **Kai Zhu and Jintu Zhang** – downstream benchmarking
* **Qiaolin Gou** – inference utilities

We welcome contributions from the community. If you'd like to contribute, please feel free to open issues or submit pull requests!

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