| | --- |
| | license: cc-by-nc-sa-2.0 |
| | --- |
| | <div align='center'> |
| | <h1> MolCRAFT Series for Drug Design: MolPilot </h1> |
| |
|
| | [](https://github.com/AlgoMole/MolCRAFT/tree/master) |
| | [](https://MolCRAFT-GenSI.github.io/) |
| | [](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 Guided Bayesian Flow Networks](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](https://huggingface.co/GenSI/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](https://huggingface.co/GenSI/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](https://huggingface.co/GenSI/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. | |
| |
|
| |
|
| | --- |
| |
|
| | # 🚀 MolPilot |
| | Official implementation of ICML 2025 ["Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule"](https://arxiv.org/abs/2505.07286). |
| |
|
| | <!--  --> |
| |
|
| | We propose VLB-Optimal Scheduling (VOS) and demonstrate its generality on the popular diffusion-based models (TargetDiff, with the code in `targetdiff` folder) and BFN-based models (our MolPilot). |
| |
|
| | In fact, VOS can be easily integrated into other frameworks, with only minor changes w.r.t. training: |
| |
|
| | ```python |
| | # Example: TargetDiff molopt_score_model.py |
| | |
| | class ScorePosNet3D(nn.Module): |
| | def get_diffusion_loss(...): |
| | ##### Original Training Loss ##### |
| | time_step, pt = self.sample_time(num_graphs, protein_pos.device, self.sample_time_method) |
| | # Xt = a.sqrt() * X0 + (1-a).sqrt() * eps |
| | ligand_pos_perturbed = a_pos.sqrt() * ligand_pos + (1.0 - a_pos).sqrt() * pos_noise # pos_noise * std |
| | |
| | ##### VOS Generalized Loss ##### |
| | time_step_v, pt = self.sample_time(num_graphs, protein_pos.device, self.sample_time_method) |
| | # Vt = a * V0 + (1-a) / K |
| | log_ligand_v0 = index_to_log_onehot(ligand_v, self.num_classes) |
| | ligand_v_perturbed, log_ligand_vt = self.q_v_sample(log_ligand_v0, time_step_v, batch_ligand) |
| | kl_v = self.compute_v_Lt(log_v_model_prob=log_v_model_prob, log_v0=log_ligand_v0, |
| | log_v_true_prob=log_v_true_prob, t=time_step_v, batch=batch_ligand) |
| | |
| | ``` |
| |
|
| | The optimal test-time noise schedule can be obtained by first storing the gridded loss surface values, and then running the dynamic programming script in `test/test_geodesic_budget.py`. |
| |
|
| | <!--  --> |
| |
|
| | ## Environment |
| | It is highly recommended to install via docker if a Linux server with NVIDIA GPU is available. |
| |
|
| | Otherwise, you might check [README for env](docker/README.md) for further details of docker or conda setup. |
| |
|
| | ### Prerequisite |
| | A docker with `nvidia-container-runtime` enabled on your Linux system is required. |
| |
|
| | > [!TIP] |
| | > - This repo provides an easy-to-use script to install docker and nvidia-container-runtime, in `./docker` run `sudo ./setup_docker_for_host.sh` to set up your host machine. |
| | > - For details, please refer to the [install guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). |
| | |
| | |
| | ### Install via Docker |
| | We highly recommend you to set up the environment via docker, since all you need to do is a simple `make` command. |
| | ```bash |
| | cd ./docker |
| | make |
| | ``` |
| | |
| | ----- |
| | ## Data |
| | We use the same data as [TargetDiff](https://github.com/guanjq/targetdiff/tree/main?tab=readme-ov-file#data). Data used for training / evaluating the model should be put in the `data` folder by default, and accessible in the [data](https://drive.google.com/drive/folders/1j21cc7-97TedKh_El5E34yI8o5ckI7eK?usp=share_link) Google Drive folder. |
| | |
| | To train the model from scratch, download the lmdb file and split file into data folder: |
| | * `crossdocked_v1.1_rmsd1.0_pocket10_processed_final.lmdb` |
| | * `crossdocked_pocket10_pose_split.pt` |
| |
|
| | To evaluate the model on the test set, download _and_ unzip the `test_set.zip` into data folder. It includes the original PDB files that will be used in Vina Docking. |
| |
|
| | ```yaml |
| | data: |
| | name: pl # [pl, pl_tr] where tr means offline-transformed |
| | ``` |
| |
|
| | --- |
| | ## Training |
| | ```bash |
| | python train_bfn_twisted.py --exp_name ${EXP_NAME} --revision ${REVISION} --config_file configs/crossdock_train_test.yaml --time_decoupled |
| | ``` |
| |
|
| | where the default values should be set the same as: |
| | ```bash |
| | python train_bfn_twisted.py --sigma1_coord 0.05 --beta1 1.5 --beta1_bond 1.5 --lr 5e-4 --time_emb_dim 0 --self_condition --epochs 30 --batch_size 16 --max_grad_norm Q --scheduler plateau --destination_prediction True --use_discrete_t True --num_samples 10 --sampling_strategy end_back_pmf --sample_num_atoms ref --ligand_atom_mode add_aromatic |
| | ``` |
| |
|
| | ### Debugging |
| | ```bash |
| | python train_bfn_twisted.py --no_wandb --debug --epochs 1 |
| | ``` |
| |
|
| | ## Sampling |
| | We provide the pretrained MolPilot checkpoint [here](https://drive.google.com/file/d/1c-lD3yfRx6JlbTWq-jAdirrK6sK2lGLq/view?usp=share_link). |
| |
|
| |
|
| | ### Sampling for pockets in the testset |
| | To sample for CrossDock, set the `CONFIG` to `configs/crossdock_train_test.yaml`. For PoseBusters, set it to `configs/posebusters_test.yaml`. |
| |
|
| | ```bash |
| | # Sample with time scheduler |
| | python train_bfn_twisted.py --config_file ${CONFIG} --ckpt_path ${CKPT_PATH} --time_scheduler_path ${TIME_SCHEDULER} --test_only --exp_name ${EXP_NAME} --revision ${REVISION} --num_samples ${NUM_MOLS_PER_POCKET} --sample_steps 100 --eval_batch_size ${BATCH_SIZE} |
| | ``` |
| |
|
| | ### Sampling from pdb file |
| | To sample from a whole protein pdb file, we need the corresponding reference ligand to clip the protein pocket (a 10A region around the reference position). |
| |
|
| | ```bash |
| | python sample_for_pocket.py --protein_path ${PDB_PATH} --ligand_path ${SDF_PATH} --time_scheduler_path ${TIME_SCHEDULER} --num_samples ${NUM_MOLS_PER_POCKET} |
| | ``` |
| |
|
| | ## Evaluation |
| |
|
| | ### Evaluating meta files |
| | We provide our samples as `molpilot_ref_vina_docked.pt` on CrossDock in the [sample](https://drive.google.com/drive/folders/1A3Mthm9ksbfUnMCe5T2noGsiEV1RfChH?usp=sharing) Google Drive folder. |
| |
|
| | <!-- TODO:, together with all the baseline results on PoseBusters in the [sample_posebusters]() folder. --> |
| |
|
| |
|
| | ## Citation |
| |
|
| | ``` |
| | @article{qiu2025piloting, |
| | title={Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule}, |
| | author={Qiu, Keyue and Song, Yuxuan and Fan, Zhehuan and Liu, Peidong and Zhang, Zhe and Zheng, Mingyue and Zhou, Hao and Ma, Wei-Ying}, |
| | journal={ICML 2025}, |
| | year={2025} |
| | } |
| | ``` |
