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Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

License: MIT License

Python 82.69% Shell 0.45% Jupyter Notebook 16.86%

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pocket2mol's Issues

Skipping in training

Hi! After unzipping the dataset, I ran the train.py file directly, but it seems that all the data is skipped. I'm not sure if I'm overlooking something. Thank you for your help! A screenshot of my data folder is attached below.

can't convert negative int to unsigned

Hi, I'm using sample_for_pdb.py with the default setting, but it raised an error: OverflowError: can't convert negative int to unsigned which happened in Chem.SanitizeModel part, wondering why could this happen?

The initial ligand

Nice Paper，could you tell me how to add the atom and atomic coordinate of the initial ligand in the sample of protein pocket? Just like smiles seq2seq, we give the prompt of sequence like CCCC. In this work, we give the initial ligand like atom and xyz.

Non-Equivariant BatchNorm

Hi there,

I've been playing with your codebase to see how equivariant features propagate through different layer types and I think there might be an error in your code. The AttentionInteractionBlockVN normalizes the vector representation with a standard nn.LayerNorm layer which breaks the equivariance of the vector representations inside the encoder. Was this intended? I'm not sure how much of an effect it will have on the rest of the model as the ligand and pocket are jointly encoded. Similarly, a standard nn.Linear layer is used to embed the initial atomic vector representation which also breaks the initial maintenance of equivariance between the atomic coordinates and the machine learned embeddings.

ValueError in Loading dataset...

Hello,

Thank you for sharing this fantastic work, and I have faced some issues reproducing your work. The error statement is in the following, the dataset is not well set up. I am following your instruction to download the dataset archive crossdocked_pocket10.tar.gz and the split file split_by_name.pt from (https://drive.google.com/drive/folders/1CzwxmTpjbrt83z_wBzcQncq84OVDPurM). And extracting the TAR archive.

Could you help to fix this issue? Any suggestion will be grateful.

Is it required to train the models before using or can use pre-trained model?

Hello!

thanks for uploading the code.
I'm working on Mac so I can't install cuda drivers in general. I was wondering if there's a way to test sampling a pdb pocket or have to install it on a machine that is cuda capable and run through the previous training steps first.

Many thanks

I want to use your tools but i found that there was a missing file named 'crossdocked_pocket10_name2id.pt' from the folder './data/'

sample issue

I got error, when I ran samply.py as following:

Pocket2Mol-main$ python sample.py --data_id 1 --outdir ./outputs
[2022-09-19 16:56:38,371::sample::INFO] Namespace(config='./configs/sample.yml', data_id='1', device='cuda', outdir='./outputs')
[2022-09-19 16:56:38,371::sample::INFO] {'data': {'data_name': 'test', 'dataset': {'name': 'pl', 'path': './data/crossdocked_pocket10', 'split': './data/split_by_name.pt'}}, 'model': {'checkpoint': './home/amar/Pocket2Mol-main/ckpt/pretrained_Pocket2Mol.pt'}, 'sample': {'seed': 2020, 'mask_init': True, 'num_samples': 100, 'beam_size': 500, 'max_steps': 50, 'threshold': {'focal_threshold': 0.5, 'pos_threshold': 0.25, 'element_threshold': 0.3, 'hasatom_threshold': 0.6, 'bond_threshold': 0.4}}}
[2022-09-19 16:56:38,371::sample::INFO] Loading data...
Traceback (most recent call last):
File "sample.py", line 211, in
base_data = testset[data_id]
File "/home/amar/anaconda3/envs/Pocket2Mol/lib/python3.8/site-packages/torch/utils/data/dataset.py", line 363, in getitem
return self.dataset[self.indices[idx]]
IndexError: list index out of range

Could you please help me to solve this problem?

TypeError: cannot pickle 'generator' object

您好，
很感谢您的工作，我收获很多。但是我在使用GRK4受体测试的时候，TypeError: cannot pickle 'generator' object这个错误是什么情况呀

Controlling Ligand Size

This is a great network and one that works smoothly! However, is there an option to control the ligand size? It often generates 2-4 atom molecules for a couple of rounds.

Evaluation with different training poses?

Wonderful work!

Found that it was trained using 100 k poses, is there significant gain with more poses included in training set?

I am trying to scale the data and see what happens? Is the code with filtering logic from crossdocked available.

ERROR: Could not sanitize molecule ending on line 77

[16:19:54] Explicit valence for atom # 19 C, 5, is greater than permitted
[16:19:54] ERROR: Could not sanitize molecule ending on line 77
[16:19:54] ERROR: Explicit valence for atom # 19 C, 5, is greater than permitted
Skipping (431) BRD4_HUMAN_42_168_0/5cs8_A_rec_4o7f_2rq_lig_tt_docked_6.sdf

What is the reason for this error? (Please forgive me for my first exposure to this direction)

The loss of MDN is negative

非常棒的工作，感谢作者的分享！
有一个问题想请教一下：我用默认的config训练模型（除了pin_memory设置成False），随着训练的进行其中position_predictor的loss变成负数。
所以想问一下是什么原因导致的，是否会影响模型的收敛？

Give permission to create branches?

Hi @pengxingang , could you please allow contributions? I have updated the documentation of functions, but I cannot push as I don't have permissions to create a new branch.
Thanks

Argparse regression

If the --center argument starts with a negative number argparse fails (see https://bugs.python.org/issue9334)

$ python sample_for_pdb.py --pdb_path ./example/6j00.pdb --center '-42.6,-30.6,-11.6'
usage: sample_for_pdb.py [-h] [--pdb_path PDB_PATH] [--center CENTER] [--bbox_size BBOX_SIZE] [--config CONFIG] [--device DEVICE] [--outdir OUTDIR]
sample_for_pdb.py: error: argument --center: expected one argument

A simple workaround is to place a space as the first character in the --center argument such as

$ python sample_for_pdb.py --pdb_path ./example/6j00.pdb --center ' -42.6,-30.6,-11.6'

Without solving the regression, a horribly hacky way to fix this would be on line 67 of sample_for_pdb.py

if __name__ == '__main__':
    idx = sys.argv.index("--center")+1
    sys.argv[idx] = ' ' + sys.argv[idx]
    parser = argparse.ArgumentParser()
    ...

Clarification on Pocket2Mol model training with CrossDocked2020 dataset

Hello, I appreciate your excellent work on the Pocket2Mol model. I am also attempting to train this model on a larger dataset and have started with the CrossDocked2020-v1.3 dataset, which has an RMSD < 2A. This dataset already includes clustered training and test data distributions, so I'm unsure about the need for mmseqs2 clustering. Could you please explain the reason for this step?

Additionally, the CrossDocked2020 dataset has various docking forms, such as Autodock Vina docked poses of ligands in the receptor and the first and second iterations of CNN-optimized poses. Which of these did you use in your training process?

Lastly, I noticed that the .PDB files in your training dataset are smaller than those in the CrossDocked2020 dataset. Did you perform any extra processing steps to obtain these smaller receptor files?

Thank you in advance for your time and assistance!

Question about CPU usage

First of all, thank you for sharing your wonderful research.
After I run the Pocket2Mol, I have a question about CPU usage.
I think that Pocket2Mol doesn't require large memory in GPU but uses CPU a lot.
Is there any reason for specifying Pocket2Mol use CPU a lot rather than GPU?
Wouldn't it cause a problem if I change the code to run process mainly using GPU?

Question about training time

Hello,
Thank you for your wonderful work!
I am running Pocket2Mol using a TITAN RTX under the default settings. I notice that my training process is time-consuming (4days).
I wonder how long does it approximately take to train in your experiment, and how many V100s are used.

Non E(3) equivariant model issue

Hi, nice paper published. Retarding to your E(3) model, I think your model might be not translational equivariant. I have similar questions with @oriondollar. I think it might be better to open a new issue here.

(1) In your model, normalized absolute coordinates were directly used as input vector features. So, I am wondering your model might not work for other test data having different absolute coordinates. As you mentioned in @oriondollar solution, that we can translate the center of pocket mass to the origin of coordinate. I want to ask could this solve the non-translational equivariance perfectly?

(2) I noticed you used normalization factor=20 to normalize the input absolute coordinate vectors, do you have any purposes to choose 20?

(3) Similar to the question (1), For vertex vectors, I noticed you used normalized absolute coordinates directly, Could this guarantee the equivariance? If we use different normalized absolute coordinates as vertex vectors, will your model work?

(4) You also mentioned that the embedding layer has minor effect on the translational equivariance and the model still learns the translational equivariance since the substraction of vectors remain unchanged. But from my test, it doesn't work if I used a different protein-ligand data outside training data coordinate space.

Do I misunderstand something? Do you have any ideas to solve this issue? Looking forward to your reply. Thank you in advance.

protein_root setting

Hello! I want to use the evaluation program to test on the result generated by the sample.py. However, I do not find the './evaluation/pdb_data' in the /evaluation. Also I try to replace it with /crossdocked_pocket10 but it will report error about some pdb files are missing. So I am wondering how should I set this protein_root

About distribution of angles and dihedral angels

Hi, wonderful work!
I wonder if the mols generated follow the same angle distribution and dihedral angel distribution with real mols.

Is Pocket2Mol able to generate pockets

I am trying to sample for pockets in the testset, but I couldn't find the "crossdocked_pocket10_name2id.pt" file.

My crossdocked_pocket10 and split_by_name.pt are located at /data/

Problem about gpu memory

Hello, may I ask how much gpu memory is required for retraining? It reported an error during retraining, GPU memory is insufficient. Can I reduce the usage of graphics memory by changing parameters?

[2023-05-16 14:40:37,049::train::ERROR] Runtime Error CUDA out of memory. Tried to allocate 38.00 MiB (GPU 0; 23.69 GiB total capacity; 12.51 GiB already allocated; 36.75 MiB free; 12.58 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
[2023-05-16 14:40:37,050::train::ERROR] Runtime Error Pin memory thread exited unexpectedly
Traceback (most recent call last):
  File "/disk-1Tm2/gm/original_/Pocket2Mol/train.py", line 227, in <module>
    train(it)
  File "/disk-1Tm2/gm/original_/Pocket2Mol/train.py", line 108, in train
    batch = next(train_iterator).to(args.device)
StopIteration

Error training, pre-trained models

Dear Pocket2Mol team,

Thank you for your paper and for this code release! I am wondering firstly if you will be able to provide links to pre-trained models? Let us know if you need help hosting them.

Also, I ran the command for training the model and encountered the following error:

No such file or directory: './data/crossdocked_pocket10_name2id.pt'

Do you know where I can obtain this file? I don't see it in the files linked in data/Readme.md

Thanks for your help!

-Cyrus

sampling issues

hi,

thanks for your great effots on this highly interesting works.

when I try to run the sample_for_pdb.py with configs file below

model:
    checkpoint: ./ckpt/pretrained_Pocket2Mol.pt

sample:
  seed: 2023
  num_samples: 200
  beam_size: 50
  max_steps: 50
  threshold:
    focal_threshold: 0.5
    pos_threshold: 0.25
    element_threshold: 0.3
    hasatom_threshold: 0.6
    bond_threshold: 0.4

the script works but some errors occured,

the error info is below


100%|██████████| 50/50 [00:05<00:00,  8.60it/s]
100%|██████████| 50/50 [00:05<00:00,  8.57it/s]
Traceback (most recent call last):
  File "sample_for_pdb.py", line 209, in <module>
    next_idx = np.random.choice(np.arange(n_tmp), p=prob, size=min(config.sample.beam_size, n_tmp), replace=False)
  File "mtrand.pyx", line 939, in numpy.random.mtrand.RandomState.choice
ValueError: probabilities do not sum to 1

Could you please see the issue and how to fix it up?
Many Thanks,

Best,

Sh-Y

The results of general properties of the molecules

The paper states the LogP of the Pocket2Mol is 1.586±1.82.
However, my reproduction result is 0.146±1.06.
I'm wondering why the mean of the LogP is so different.

Preprocessing required for sampling for a pdb file

Hi, Can you please mention the preprocessing that has to be carried out for a pdb file from PDB RCSB , so that I can sample molecules for that protein using sample_for_pdb.py.The pdb file you have given in examples folder works fine but if i take a pdb file directly from this link:https://www.rcsb.org/structure/1CQP, it shows following error:

taskset -c 0 python3 sample_for_pdb.py --pdb_path ./example/1CQP.pdb --center "32.0,28.0,36.0" --device "cpu"
[2024-01-29 04:27:42,224::sample::INFO] Namespace(bbox_size=23.0, center=[32.0, 28.0, 36.0], config='./configs/sample_for_pdb.yml', device='cpu', outdir='./outputs', pdb_path='./example/1CQP.pdb')
[2024-01-29 04:27:42,224::sample::INFO] {'model': {'checkpoint': './ckpt/pretrained_Pocket2Mol.pt'}, 'sample': {'seed': 2020, 'num_samples': 100, 'beam_size': 100, 'max_steps': 50, 'threshold': {'focal_threshold': 0.5, 'pos_threshold': 0.25, 'element_threshold': 0.3, 'hasatom_threshold': 0.6, 'bond_threshold': 0.4}}}
[2024-01-29 04:27:42,224::sample::INFO] Loading data...
[2024-01-29 04:27:42,297::sample::INFO] Loading main model...
[2024-01-29 04:27:42,416::sample::INFO] Initialization
InitSample: 1%|██▏ | 1/100 [00:00<00:28, 3.50it/s]
[2024-01-29 04:27:42,703::sample::INFO] [Pool] Queue 1 | Finished 0 | Failed 0
[2024-01-29 04:27:42,703::sample::INFO] Saving samples...
[2024-01-29 04:27:42,705::sample::INFO] Start sampling
0%| | 0/1 [00:00<?, ?it/s][2024-01-29 04:27:42,929::sample::INFO] Success:
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4.46it/s]
Traceback (most recent call last):
File "sample_for_pdb.py", line 209, in
next_idx = np.random.choice(np.arange(n_tmp), p=prob, size=min(config.sample.beam_size, n_tmp), replace=False)
File "mtrand.pyx", line 939, in numpy.random.mtrand.RandomState.choice
ValueError: probabilities do not sum to 1

Thankyou

Regarding the evaluation

Hi, thanks for the awesome work.
The code for sampling is provided,
but I guess the evaluation code for Vina score, QED, SA, etc. (i.e., Table 1 of the paper), is missing.
For the reproducibility of your work, may I ask you to kindly include the evaluation code?

About reproduction of evaluation

Good work, the details on evaluation are lack, when using
bash ./evaluation/batch_eval.sh 1 0 0
to do evaluation, the affinity calculation would produce errors by ourselves. We'd like to ask if convenient to release some information on evaluation.

Sub-structure evaluation

Hi, thanks for your nice work!
It seems that the evaluation code for the distributions of bond angles and dihedral angles (Table 3 in the paper) is missing.
Would you mind providing related codes for reproducing? Please let me know if I miss it in the repo.

Error Encountered in Molecule Sampling Using PDB File

Hello @pengxingang，,I hope you are doing well. When I was sampling molecules for a target protein using a PDB file, I ran the sample_for_pdb.py script. Unfortunately, I encountered some issues during the execution, and the following error occurred:

Thankyou

Differences between sample.py and sample_for_pdb.py

Hi, thank you for the great piece of work!

I was comparing sample.py and sample_for_pdb.py and I noticed that only the second file had queue_weight. Is there any reason for that? I guess this helps generating more diverse molecules.

I also noticed another difference: the possibility in sample.py (but not in sample_for_pdb.py) to use LigandBFSMask masking instead of LigandMaskAll: what is the use of it?

Thank you very much!

about the KL divergence

Hi, thanks for the awesome works!

I met a problem when caculating the KL divergence of molecular bond angles between training and testing sets. As I noticed that you caculated the KL divergence of the bond angles and dihedral angles with the test set. May I ask you to kindly provide the corresponding codes?

AttributeError: 'NoneType' object has no attribute 'knn_graph'

When I run python sample_for_pdb.py --pdb_path ./example/4yhj.pdb --center "32.0,28.0,36.0", I got this:

[2023-06-06 15:24:11,627::sample::INFO] Namespace(bbox_size=23.0, center=[32.0, 28.0, 36.0], config='./configs/sample_for_pdb.yml', device='cuda', outdir='./outputs', pdb_path='./example/4yhj.pdb')
[2023-06-06 15:24:11,628::sample::INFO] {'model': {'checkpoint': './ckpt/pretrained_Pocket2Mol.pt'}, 'sample': {'seed': 2020, 'num_samples': 100, 'beam_size': 300, 'max_steps': 50, 'threshold': {'focal_threshold': 0.5, 'pos_threshold': 0.25, 'element_threshold': 0.3, 'hasatom_threshold': 0.6, 'bond_threshold': 0.4}}}
[2023-06-06 15:24:11,629::sample::INFO] Loading data...
[2023-06-06 15:24:12,419::sample::INFO] Loading main model...
[2023-06-06 15:24:14,959::sample::INFO] Initialization
InitSample:   0%|                                                                                                                                                    | 0/300 [00:00<?, ?it/s]Traceback (most recent call last):
  File "sample_for_pdb.py", line 139, in <module>
    data = transform_data(data, atom_composer)
  File "/home/data/yingchun/Applic/Pocket2Mol-main/utils/datasets/__init__.py", line 44, in transform_data
    data = transform(data)
  File "/home/data/yingchun/Applic/Pocket2Mol-main/utils/transforms.py", line 657, in __call__
    data = self.get_knn_graph(data, self.knn, len_ligand_ctx, len_compose, num_workers=16)
  File "/home/data/yingchun/Applic/Pocket2Mol-main/utils/transforms.py", line 662, in get_knn_graph
    data.compose_knn_edge_index = knn_graph(data.compose_pos, knn, flow='target_to_source', num_workers=num_workers)
  File "/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch_geometric/nn/pool/__init__.py", line 130, in knn_graph
    return torch_cluster.knn_graph(x, k, batch, loop, flow, cosine,
AttributeError: 'NoneType' object has no attribute 'knn_graph'
InitSample:   0%|                                                                                                                                                    | 0/300 [00:00<?, ?it/s]

How to solve it?

train::ERROR] Runtime Error Pin memory thread exited unexpectedly

I tried to train a new model by running train.py, but I got this:

[2023-06-28 10:32:08,821::train::INFO] Namespace(config='./configs/train.yml', device='cuda', logdir='./logs')
[2023-06-28 10:32:08,821::train::INFO] {'model': {'vn': 'vn', 'hidden_channels': 256, 'hidden_channels_vec': 64, 'encoder': {'name': 'cftfm', 'hidden_channels': 256, 'hidden_channels_vec': 64, 'edge_channels': 64, 'key_channels': 128, 'num_heads': 4, 'num_interactions': 6, 'cutoff': 10.0, 'knn': 48}, 'field': {'name': 'classifier', 'num_filters': 128, 'num_filters_vec': 32, 'edge_channels': 64, 'num_heads': 4, 'cutoff': 10.0, 'knn': 32}, 'position': {'num_filters': 128, 'n_component': 3}}, 'train': {'seed': 2023, 'use_apex': False, 'batch_size': 8, 'num_workers': 8, 'pin_memory': True, 'max_iters': 500000, 'val_freq': 5000, 'pos_noise_std': 0.1, 'max_grad_norm': 100.0, 'optimizer': {'type': 'adam', 'lr': 0.0002, 'weight_decay': 0, 'beta1': 0.99, 'beta2': 0.999}, 'scheduler': {'type': 'plateau', 'factor': 0.6, 'patience': 8, 'min_lr': 1e-05}, 'transform': {'mask': {'type': 'mixed', 'min_ratio': 0.0, 'max_ratio': 1.1, 'min_num_masked': 1, 'min_num_unmasked': 0, 'p_random': 0.15, 'p_bfs': 0.6, 'p_invbfs': 0.25}, 'contrastive': {'num_real': 20, 'num_fake': 20, 'pos_real_std': 0.05, 'pos_fake_std': 2.0}, 'edgesampler': {'k': 8}}}, 'dataset': {'name': 'pl', 'path': './data/crossdocked_pocket10', 'split': './data/split_by_name.pt'}}
[2023-06-28 10:32:08,823::train::INFO] Loading dataset...
[2023-06-28 10:32:09,280::train::INFO] Building model...
Num of parameters is 3711167
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3190.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Exception in thread Thread-2:
Traceback (most recent call last):
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/threading.py", line 926, in _bootstrap_inner
    self.run()
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/utils/data/_utils/pin_memory.py", line 49, in _pin_memory_loop
    do_one_step()
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/utils/data/_utils/pin_memory.py", line 26, in do_one_step
    r = in_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/multiprocessing/queues.py", line 113, in get
    return _ForkingPickler.loads(res)
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/site-packages/torch/multiprocessing/reductions.py", line 305, in rebuild_storage_fd
    fd = df.detach()
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/multiprocessing/resource_sharer.py", line 58, in detach
    return reduction.recv_handle(conn)
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/multiprocessing/reduction.py", line 185, in recv_handle
    return recvfds(s, 1)[0]
  File "/data/sdb/opt/miniconda3/envs/aidd/lib/python3.7/multiprocessing/reduction.py", line 161, in recvfds
    len(ancdata))
RuntimeError: received 0 items of ancdata

[2023-06-28 10:32:12,068::train::INFO] [Train] Iter 1 | Loss 10.276641 | Loss(Fron) 0.631725 | Loss(Pos) 3.812413 | Loss(Cls) 1.901050 | Loss(Edge) 1.675482 | Loss(Real) 0.126777 | Loss(Fake) 2.129193 | Loss(Surf) 0.000000
[2023-06-28 10:32:12,073::train::ERROR] Runtime Error Pin memory thread exited unexpectedly
Traceback (most recent call last):
  File "train.py", line 227, in <module>
    train(it)
  File "train.py", line 108, in train
    batch = next(train_iterator).to(args.device)
StopIteration

Issues about the vina score

Hi,

I have used your vina score script "docking.py" recently. However, I have met the following error,

Traceback (most recent call last):
File "/sample_geo.py", line 519, in
g_vina_score = vina_task.run_sync()
File "/evaluation/docking.py", line 168, in run_sync
while self.get_results() is None:
File "/evaluation/docking.py", line 182, in get_results
self.results = parse_qvina_outputs(self.docked_sdf_path)
File "/evaluation/docking.py", line 24, in parse_qvina_outputs
suppl = Chem.SDMolSupplier(docked_sdf_path)
OSError: File error: Invalid input file /tmp/olzbpempbkdjotcpxfbqvwjfnxltvc_ligand_out.sdf

I found that error was caused by the missing script “prepare_receptor4.py ”.

Could you please help me solve it or provide this script?

Thank you for your attention.

Initialization failed issue

Hello @pengxingang ,I hope you are doing well.I am trying to sample some molecules for a target protein using the pdb file.But I am getting the following error:"Initilization failed" repeatedly.
Actually there are some messages such as applying np.mean to an empty slice and divide by a zero or nan value but the error message doesn't specify the line of code sample_for_pdb.py so that I can try to debug and they are always there if the model samples molecules so I think that is not a severe warning.
Also If I change the y_coordinate of the ligand box from -3.10 to -2.61 for this protein(1lqf), model is able to sample molecules.
If you are aware of such issue,can you please tell me how to work around it.
For your reference, I am providing the error information below:

(pmol) root@a5a5e5d:/workspace/pocketmol# taskset -c 0 python3 sample_for_pdb.py --pdb_path ./example/1lqf.pdb --center " 17.01,-3.1,27.47" --device "cpu"
random_seed is 9424
config is {'model': {'checkpoint': './ckpt/pretrained_Pocket2Mol.pt'}, 'sample': {'seed': 9424, 'num_samples': 100, 'beam_size': 100, 'max_steps': 50, 'threshold': {'focal_threshold': 0.5, 'pos_threshold': 0.25, 'element_threshold': 0.3, 'hasatom_threshold': 0.6, 'bond_threshold': 0.4}}}
[2024-03-03 20:13:21,574::sample::INFO] Namespace(bbox_size=23.0, center=[17.01, -3.1, 27.47], config='./configs/sample_for_pdb.yml', device='cpu', outdir='./outputs', pdb_path='./example/1lqf.pdb')
[2024-03-03 20:13:21,574::sample::INFO] {'model': {'checkpoint': './ckpt/pretrained_Pocket2Mol.pt'}, 'sample': {'seed': 9424, 'num_samples': 100, 'beam_size': 100, 'max_steps': 50, 'threshold': {'focal_threshold': 0.5, 'pos_threshold': 0.25, 'element_threshold': 0.3, 'hasatom_threshold': 0.6, 'bond_threshold': 0.4}}}
[2024-03-03 20:13:21,575::sample::INFO] Loading data...
[2024-03-03 20:13:21,909::sample::INFO] Loading main model...
[2024-03-03 20:13:22,003::sample::INFO] Initialization
InitSample: 0%| | 0/100 [00:00<?, ?it/s]/opt/conda/envs/pmol/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3441: RuntimeWarning: Mean of empty slice.
out=out, **kwargs)
/opt/conda/envs/pmol/lib/python3.7/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in true_divide
ret = ret.dtype.type(ret / rcount)
Initialization failed.
Initialization failed.
Initialization failed.
Initialization failed.
Initialization failed.

Thankyou

GPU not used when running sample_for_pdb.py

Hello,

Thank you for sharing your code! I found GPU was not used after executing sample_for_pdb.py

The first two lines of log show:
[2023-07-31 17:13:34,403::sample::INFO] Namespace(bbox_size=23.0, center=[32.0, 28.0, 36.0], config='./configs/sample_for_pdb.yml', device='cuda', outdir='./outputs', pdb_path='./example/4yhj.pdb')
[2023-07-31 17:13:34,403::sample::INFO] {'model': {'checkpoint': './ckpt/pretrained_Pocket2Mol.pt'}, 'sample': {'seed': 2020, 'num_samples': 100, 'beam_size': 300, 'max_steps': 50, 'threshold': {'focal_threshold': 0.5, 'pos_threshold': 0.25, 'element_threshold': 0.3, 'hasatom_threshold': 0.6, 'bond_threshold': 0.4}}}
...

Since it takes forever long to use the first choice, I installed manually following the instruction. And below is my conda list:

_libgcc_mutex             0.1                 conda_forge    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
_openmp_mutex             4.5                       2_gnu    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
absl-py                   1.4.0              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
aiohttp                   3.8.5            py38h01eb140_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
aiosignal                 1.3.1              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
async-timeout             4.0.2              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
attrs                     23.1.0             pyh71513ae_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
beautifulsoup4            4.12.2                   pypi_0    pypi
biopython                 1.81             py38h1de0b5d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
blas                      1.0                         mkl    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
blinker                   1.6.2              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
boost                     1.74.0           py38h2b96118_5    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
boost-cpp                 1.74.0               h6cacc03_7    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
brotlipy                  0.7.0           py38h0a891b7_1005    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
bzip2                     1.0.8                h7f98852_4    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
c-ares                    1.19.1               hd590300_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ca-certificates           2023.7.22            hbcca054_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cachetools                5.3.1              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cairo                     1.16.0            ha00ac49_1009    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
certifi                   2023.7.22                pypi_0    pypi
cffi                      1.15.1           py38h4a40e3a_3    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
charset-normalizer        3.2.0              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
click                     8.1.6           unix_pyh707e725_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
colorama                  0.4.6              pyhd8ed1ab_0    conda-forge
cryptography              41.0.2           py38hcdda232_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cudatoolkit               11.3.1              hb98b00a_12    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cycler                    0.11.0             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
easydict                  1.10                     pypi_0    pypi
filelock                  3.12.2                   pypi_0    pypi
font-ttf-dejavu-sans-mono 2.37                 hab24e00_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
font-ttf-inconsolata      3.000                h77eed37_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
font-ttf-source-code-pro  2.038                h77eed37_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
font-ttf-ubuntu           0.83                 hab24e00_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
fontconfig                2.14.1               hef1e5e3_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
fonts-conda-ecosystem     1                             0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
fonts-conda-forge         1                             0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
freetype                  2.10.4               hca18f0e_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
frozenlist                1.4.0            py38h01eb140_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
gdown                     4.7.1                    pypi_0    pypi
gettext                   0.21.1               h27087fc_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
giflib                    5.2.1                h0b41bf4_3    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
google-auth               2.22.0             pyh1a96a4e_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
google-auth-oauthlib      1.0.0              pyhd8ed1ab_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
googledrivedownloader     0.4                      pypi_0    pypi
greenlet                  2.0.2            py38h17151c0_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
grpcio                    1.56.2           py38h94a1851_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
icu                       69.1                 h9c3ff4c_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
idna                      3.4                pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
importlib-metadata        6.8.0              pyha770c72_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
intel-openmp              2023.1.0         hdb19cb5_46305    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
isodate                   0.6.1                    pypi_0    pypi
jinja2                    3.1.2              pyhd8ed1ab_1    conda-forge
joblib                    1.3.1                    pypi_0    pypi
jpeg                      9e                   h0b41bf4_3    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
kiwisolver                1.4.4            py38h43d8883_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lcms2                     2.15                 hfd0df8a_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ld_impl_linux-64          2.40                 h41732ed_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lerc                      4.0.0                h27087fc_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libabseil                 20230125.3      cxx17_h59595ed_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libblas                   3.9.0           1_h86c2bf4_netlib    conda-forge
libcblas                  3.9.0           5_h92ddd45_netlib    conda-forge
libdeflate                1.17                 h5eee18b_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libffi                    3.4.2                h7f98852_5    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgcc-ng                 13.1.0               he5830b7_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgfortran-ng            13.1.0               h69a702a_0    conda-forge
libgfortran5              13.1.0               h15d22d2_0    conda-forge
libglib                   2.76.4               hebfc3b9_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgomp                   13.1.0               he5830b7_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgrpc                   1.56.2               h3905398_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libiconv                  1.17                 h166bdaf_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
liblapack                 3.9.0           5_h92ddd45_netlib    conda-forge
libnsl                    2.0.0                h7f98852_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libpng                    1.6.39               h753d276_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libprotobuf               4.23.3               hd1fb520_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libsqlite                 3.42.0               h2797004_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libstdcxx-ng              13.1.0               hfd8a6a1_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libtiff                   4.5.0                h6adf6a1_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libuuid                   2.38.1               h0b41bf4_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libuv                     1.44.2               hd590300_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libwebp                   1.2.4                h1daa5a0_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libwebp-base              1.2.4                h5eee18b_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libxcb                    1.15                 h0b41bf4_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libxml2                   2.9.14               haae042b_4    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libzlib                   1.2.13               hd590300_5    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
lmdb                      1.4.1                    pypi_0    pypi
markdown                  3.4.4              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
markupsafe                2.1.3            py38h01eb140_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
matplotlib-base           3.4.3            py38hf4fb855_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mkl                       2023.1.0         h6d00ec8_46342    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl-service               2.4.0            py38h5eee18b_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_fft                   1.3.6            py38h417a72b_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl_random                1.2.2            py38h417a72b_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
multidict                 6.0.4            py38h1de0b5d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ncurses                   6.4                  hcb278e6_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
networkx                  3.1                pyhd8ed1ab_0    conda-forge
numpy                     1.24.4                   pypi_0    pypi
numpy-base                1.24.3           py38h060ed82_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
oauthlib                  3.2.2              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openssl                   3.1.1                hd590300_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
packaging                 23.1               pyhd8ed1ab_0    conda-forge
pandas                    2.0.3            py38h01efb38_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pcre2                     10.40                hc3806b6_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pillow                    9.4.0            py38h6a678d5_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
pip                       23.2.1             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pixman                    0.40.0               h36c2ea0_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
platformdirs              3.9.1              pyhd8ed1ab_0    conda-forge
pooch                     1.7.0              pyha770c72_3    conda-forge
protobuf                  4.23.3           py38h830738e_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
psutil                    5.9.5                    pypi_0    pypi
pthread-stubs             0.4               h36c2ea0_1001    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyasn1                    0.4.8                      py_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyasn1-modules            0.2.7                      py_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pycairo                   1.24.0           py38h1a1917b_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pycparser                 2.21               pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyg                       2.0.4           py38_torch_1.10.0_cu113    pyg
pyjwt                     2.8.0              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyopenssl                 23.2.0             pyhd8ed1ab_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyparsing                 3.1.1              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pysocks                   1.7.1              pyha2e5f31_6    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python                    3.8.17          he550d4f_0_cpython    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python-dateutil           2.8.2              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python-louvain            0.16               pyhd8ed1ab_0    conda-forge
python-tzdata             2023.3             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python_abi                3.8                      3_cp38    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pytorch                   1.10.1          py3.8_cuda11.3_cudnn8.2.0_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
pytorch-cluster           1.6.0           py38_torch_1.10.0_cu113    pyg
pytorch-mutex             1.0                        cuda    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch
pytorch-scatter           2.0.9           py38_torch_1.10.0_cu113    pyg
pytorch-sparse            0.6.13          py38_torch_1.10.0_cu113    pyg
pytorch-spline-conv       1.2.1           py38_torch_1.10.0_cu113    pyg
pytz                      2023.3             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyu2f                     0.1.5              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyyaml                    6.0.1                    pypi_0    pypi
rdflib                    6.3.2                    pypi_0    pypi
rdkit                     2022.03.5        py38ha829ea6_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
re2                       2023.03.02           h8c504da_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
readline                  8.2                  h8228510_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
reportlab                 3.6.12           py38h5eee18b_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
requests                  2.31.0             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
requests-oauthlib         1.3.1              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
rocm-smi                  5.6.0                h59595ed_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
rsa                       4.9                pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
scikit-learn              1.3.0            py38hc099248_0    conda-forge
scipy                     1.10.1           py38h59b608b_3    conda-forge
setuptools                68.0.0             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
six                       1.16.0             pyh6c4a22f_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
soupsieve                 2.4.1                    pypi_0    pypi
sqlalchemy                2.0.19           py38h01eb140_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tbb                       2021.8.0             hdb19cb5_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
tensorboard               2.13.0             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tensorboard-data-server   0.7.0            py38h3d167d9_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
threadpoolctl             3.2.0              pyha21a80b_0    conda-forge
tk                        8.6.12               h27826a3_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tornado                   6.3.2            py38h01eb140_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tqdm                      4.65.0             pyhd8ed1ab_1    conda-forge
typing-extensions         4.7.1                hd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
typing_extensions         4.7.1              pyha770c72_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
urllib3                   2.0.4                    pypi_0    pypi
werkzeug                  2.3.6              pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
wheel                     0.41.0             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-kbproto              1.0.7             h7f98852_1002    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libice               1.1.1                hd590300_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libsm                1.2.4                h7391055_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libx11               1.8.6                h8ee46fc_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libxau               1.0.11               hd590300_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libxdmcp             1.1.3                h7f98852_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libxext              1.3.4                h0b41bf4_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-libxrender           0.9.11               hd590300_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-renderproto          0.11.1            h7f98852_1002    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-xextproto            7.3.0             h0b41bf4_1003    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xorg-xproto               7.0.31            h7f98852_1007    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xz                        5.2.6                h166bdaf_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
yacs                      0.1.8              pyhd8ed1ab_0    conda-forge
yaml                      0.2.5                h7f98852_2    conda-forge
yarl                      1.9.2            py38h01eb140_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zipp                      3.16.2             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zlib                      1.2.13               hd590300_5    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zstd                      1.5.2                hfc55251_7    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge

Receptors containing metal ions at binding sites

Thank you very much for providing such a great molecular generation model.

May I ask, does your model support protein receptors containing metal ions at binding site? I have attempted to use these protein as input, and the model can run normally, but have the rationality of these results been confirmed?

If not supported, is there a means to reserve the position of metal ions during the ligand generation process, so as not to generate molecules at this position and avoid metal ions affecting the rationality of molecular generation. I can manually add metal ions after molecular generation.

pengxingang / pocket2mol Goto Github PK

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