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+license: cc-by-nc-sa-2.0
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+<div align='center'>
+<h1> MolCRAFT Series for Drug Design </h1>
+
+[![Github](https://img.shields.io/badge/Github-4d8cd8?style=for-the-badge&logo=googledrive&logoColor=white)](https://github.com/AlgoMole/MolCRAFT/tree/master)
+[![Blog](https://img.shields.io/badge/Blog-3858bf?style=for-the-badge&logo=homepage&logoColor=white)](https://MolCRAFT-GenSI.github.io/)
+[![Data](https://img.shields.io/badge/Data-4d8cd8?style=for-the-badge&logo=googledrive&logoColor=white)](https://drive.google.com/drive/folders/16KiwfMGUIk4a6mNU20GnUd0ah-mjNlhC?usp=share_link)
+
+</div>
+
+
+Welcome to the official repository for the MolCRAFT series of projects! This series focuses on developing and improving deep learning models for **structure-based drug design (SBDD)** and **molecule optimization (SBMO)**. Our goal is to create molecules with high binding affinity and plausible 3D conformations.
+
+This repository contains the source code for the following projects:
+
+* [**MolCRAFT**: Structure-Based Drug Design in Continuous Parameter Space](https://arxiv.org/abs/2404.12141) (ICML'24)
+* [**MolJO**: Empower Structure-Based Molecule Optimization with Gradient Guidance](https://arxiv.org/abs/2411.13280) (ICML'25)
+* [**MolPilot**: Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule](https://arxiv.org/abs/2505.07286) (ICML'25)
+
+## 📜 Overview
+
+The MolCRAFT series addresses critical challenges in generative models for SBDD, including modeling molecular geometries, handling hybrid continuous-discrete spaces, and optimizing molecules against protein targets. Each project introduces novel methodologies and achieves **state-of-the-art** performance on relevant benchmarks.
+
+## 🧭 Navigation
+
+| Folder                         | TL, DR               | Description                                                                                                                                                                                             |
+| --------------------------- | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| [MolCRAFT](./MolCRAFT/)      | Unified Space for Molecule Generation  | MolCRAFT is the first SBDD generative model based on Bayesian Flow Network (BFN) operating in the unified continuous parameter space for different modalities, with variance reduction sampling strategy to generate high-quality samples with more than 10x speedup.
+| [MolJO](./MolJO/)            | Gradient-Guided Molecule Optimization   | MolJO is a gradient-based Structure-Based Molecule Optimization (SBMO) framework derived within BFN. It employs joint guidance across continuous coordinates and discrete atom types, alongside a backward correction strategy for effective optimization.
+| [MolPilot](./MolPilot/)      | Optimal Scheduling  | MolPilot enhances SBDD by introducing a VLB-Optimal Scheduling (VOS) strategy for the twisted multimodal probability paths, significantly improving molecular geometries and interaction modeling, achieving 95.9% PB-Valid rate.  |
+
+
+---
+
+## 🚀 Projects
+
+### MolCRAFT (Let's Craft the Molecules)
+
+<p align="center"><img src="asset/molcraft.gif" width="60%"></p>
+
+* **Description**: MolCRAFT is the first SBDD model that employs BFN and operates in a **continuous parameter space**. It introduces a novel noise-reduced sampling strategy to generate molecules with superior binding affinity and more stable 3D structures. MolCRAFT has demonstrated its ability to accurately model interatomic interactions, achieving reference-level Vina Scores.
+* **Key Contributions**:
+    * Operates in continuous parameter space for SBDD within BFN framework.
+    * Novel variance reduction sampling strategy that improves both sample quality and efficiency.
+    * Achieves state-of-the-art binding affinity and structural stability.
+
+---
+
+### MolJO (Molecule Joint Optimization)
+
+![](asset/moljo_framework_all.png)
+
+* **Description**: MolJO is a gradient-based SBMO framework that leverages a continuous and differentiable space derived through Bayesian inference. It facilitates **joint guidance signals across different modalities** (continuous coordinates and discrete atom types) while preserving SE(3)-equivariance. MolJO introduces a novel backward correction strategy for an effective trade-off between exploration and exploitation.
+* **Key Contributions**:
+    * Gradient-based SBMO framework with joint guidance across different modalities.
+    * Backward correction strategy for optimized exploration-exploitation.
+    * State-of-the-art performance in practical optimization tasks, including multi-objective and constrained optimization for R-group redesign, scaffold hopping, etc.
+
+---
+
+### MolPilot (How to Pilot the Aircraft)
+
+![](asset/molpilot_vos.png)
+
+* **Description**: MolPilot addresses challenges in geometric structure modeling by focusing on the **twisted probability path of multi-modalities** (continuous 3D positions and discrete 2D topologies). It proposes a VLB-Optimal Scheduling (VOS) strategy, optimizing the Variational Lower Bound as a path integral for SBDD. MolPilot significantly enhances molecular geometries and interaction modeling.
+* **Key Contributions**:
+    * Addresses multi-modality challenges in SBDD.
+    * Introduces VLB-Optimal Scheduling (VOS) strategy, generally applicable to a wide range of frameworks including diffusions.
+    * Achieves 95.9% PoseBusters passing rate on CrossDock with significantly improved molecular geometries.
+
+---
+
 # MolJO
 Official implementation of ICML 2025 ["Empower Structure-Based Molecule Optimization with Gradient Guidance"](https://arxiv.org/abs/2411.13280).