Upload README.md

README.md

@@ -6,9 +6,10 @@ tags:
 - IntFold
 - biomolecular-structure-prediction
 ---
-<!-- ![IntFold Cover](assets/intfold-cover.png) -->
 
-# IntFold
+![IntFold Cover](https://github.com/IntelliGen-AI/IntFold/assets/intfold-cover.png)
+
+# IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction.
 [![HuggingFace Models](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Models-yellow)](https://huggingface.co/GAGABIG/CNN)
 [![PyPI](https://img.shields.io/pypi/v/intfold)](https://pypi.org/project/intfold/)
 [![License](https://img.shields.io/badge/license-Apache%202.0-blue)](LICENSE)
@@ -17,12 +18,11 @@ tags:
 
 <div align="center" style="margin: 20px 0;">
   <span style="margin: 0 10px;">⚡ <a href="https://server.intfold.com">IntFold Server</a></span>
-  &bull; <span style="margin: 0 10px;">📄 <a href="xxxxx">Technical Report</a></span>
+  &bull; <span style="margin: 0 10px;">📄 <a href="https://intfold-server-dev.oss-cn-hongkong.aliyuncs.com/IntFold_Technical_Report.pdf">Technical Report</a></span>
 </div>
 
 
-**IntFold:** An open-source PyTorch implementation of [AlphaFold 3](https://www.nature.com/articles/s41586-024-07487-w). Predict biomolecular structures with state-of-the-art accuracy in a flexible framework.
-
+![IntFold Model](https://github.com/IntelliGen-AI/IntFold/assets/Intfold-Model-Arc.png)
 
 
 ## 🚀 Quick Start
@@ -33,4 +33,63 @@ To quickly get started with IntFold, you can use the following commands:
 pip install intfold
 # Run inference with an example YAML file
 intfold predict ./examples/5S8I_A.yaml --out_dir ./output
-```
+```
+
+## ⚙️ Installation
+
+To more complete installation instructions and usage, please refer to the [Installation Guide](https://github.com/IntelliGen-AI/IntFold/docs/installation.md).
+
+
+## 🔍 Inference
+
+1. **Prepare Input File**: Create a YAML file with your sequences following our [input format specification](https://github.com/IntelliGen-AI/IntFold/docs/input_yaml_format.md)
+
+2. **Run Prediction**:
+   ```bash
+   intfold predict your_input.yaml --out_dir ./results
+   ```
+
+3. **Check Results**: Find predicted structures and confidence scores in the output directory, you can also check the section of **output format** in [output documentation](https://github.com/IntelliGen-AI/IntFold/docs/input_yaml_format.md#output-format).
+
+4. **Optional Optimization**: Enable [custom kernels](https://github.com/IntelliGen-AI/IntFold/docs/kernels.md) for faster inference and reduced memory usage
+
+For comprehensive usage instructions and examples, refer to the [Usage Guide](https://github.com/IntelliGen-AI/IntFold/docs/usage.md).
+
+
+## 📊 Benchmarking
+To comprehensively evaluate the performance of IntFold, we conducted a rigorous evaluation on [FoldBench](https://github.com/BEAM-Labs/FoldBench). We compared IntFold against several leading methods, including [Boltz-1,2](https://github.com/jwohlwend/boltz), [Chai-1](https://github.com/chaidiscovery/chai-lab), [Protenix](https://github.com/bytedance/Protenix) and [Alphafold3](https://github.com/google-deepmind/alphafold3).
+
+For more details on the benchmarking process and results, please refer to our [Technical Report](https://intfold-server-dev.oss-cn-hongkong.aliyuncs.com/IntFold_Technical_Report.pdf).
+
+![Benchmark Metrics](https://github.com/IntelliGen-AI/IntFold/assets/intfold_metrics.png)
+
+
+## 🌐 IntFold Server
+
+**We highly recommend using the [IntFold Server](https://server.intfold.com) for the most accurate, complete, and convenient biomolecular structure predictions.** It requires no installation and provides an intuitive web interface to submit your sequences and visualize results directly in your browser. The server runs the **full, optimized, latest** IntFold implementation for optimal performance.
+
+![IntFold Server](https://github.com/IntelliGen-AI/IntFold/assets/intfold-server-screenshot.png)
+
+
+## 📜 Citation
+
+If you use IntFold in your research, please cite our paper:
+
+the official citation will be available soon.
+
+
+## 🔗 Acknowledgements
+
+- The implementation of **fast layernorm operators** is inspired by [OneFlow](https://github.com/Oneflow-Inc/oneflow) and [FastFold](https://github.com/hpcaitech/FastFold), following [Protenix](https://github.com/bytedance/Protenix)'s usage. 
+- Many components in `intfold/openfold/` are adapted from [OpenFold](https://github.com/aqlaboratory/openfold), with substantial modifications and improvements by our team (except for the `LayerNorm` part).  
+- This repository, the implementation of **Inference Data Pipeline**(Data/Feature Processing and MSA generation tasks) referred to [Boltz-1](https://github.com/jwohlwend/boltz), and modify some codes to adapt to the input of our model.
+
+
+
+## ⚖️ License
+
+The IntFold project, including code and model parameters, is made available under the [Apache 2.0 License](https://github.com/IntelliGen-AI/IntFold/LICENSE), it is free for both academic research and commercial use.
+
+## 📬 Contact Us
+
+If you have any questions or are interested in collaboration, please feel free to contact us at [email protected].