I ran the sample.py script and from the pool.finished object I get a list of data. For each data I call reconstruct_from_generated to get out a rdkit molecule with a 3D conformation.

However, it seems the conformation is not in the same coordinate space as the protein pocket:

I sampled idx 50 and looked at the samples_all.pt file, the corresponding protein pocket I loaded is thus: FKB1A_HUMAN_2_108_0/1d7j_A_rec_1tco_fk5_lig_tt_docked_0_pocket10.pdb

I see the ligand (left) is far away from the pocket (right)

Am I doing something wrong?

Hi excuse me, I can't run the script sample.py the error is No such file or directory: './data/crossdocked_pocket10_name2id.pt', i have no idea, could you tell how can i solve this problem? thank you!

Sorry to bother you，I ran the test model successfully and got the smiles structures of generated molecules, however, I have no idea about how to run our own example, though we replace the test system with our own one and I also wonder the meaning of the number in the last part of this list ('YPKA_YERPS_90_433_0/5ce3_B_rec_5ce3_adp_lig_tt_min_0_pocket10.pdb', 'YPKA_YERPS_90_433_0/5ce3_B_rec_5ce3_adp_lig_tt_min_0.sdf', 'YPKA_YERPS_90_433_0/5ce3_B_rec.pdb', 0.552965)

Hi, I am trying to train the model on a new dataset, so I run train.py and sample.py with new configuration files.

My training and sampling commands:

python train.py my_train_main_model.yml
python train.py my_train_frontier_model.yml
python sample.py my_sample.yml -i 0

my_train_main_model.yml, my_train_frontier_model.yml only change dataset path, and my_sample.yml change checkpoint and dataset paths.

However, I got following error message:

[2022-02-22 07:56:33,069::sample::INFO] Namespace(config='configs/fy_sample.yml', data_id=0, device='cuda', outdir='./outputs')
[2022-02-22 07:56:33,069::sample::INFO] {'dataset': {'name': 'pl', 'path': '/home/t-yafan/workspace/data/Tgt2Drug', 'split': '/home/t-yafan/workspace/data/Tgt2Drug/Binary/v4-2/src/split_by_name.pt'}, 'model': {'main': {'checkpoint': './logs/fy_train_main_model_2022_02_21__07_57_11/checkpoints/100000.pt'}, 'frontier': {'checkpoint': './logs/fy_train_frontier_model_2022_02_21__14_30_45/checkpoints/80000.pt'}}, 'sample': {'seed': 2020, 'num_samples': 100, 'beam_size': 300, 'logp_thres': -inf, 'num_retry': 5, 'max_steps': 50}}
[2022-02-22 07:56:33,070::sample::INFO] Loading data...
[2022-02-22 07:56:33,632::sample::INFO] Loading main model...
[2022-02-22 07:56:36,019::sample::INFO] Loading frontier model...
Traceback (most recent call last):
  File "sample.py", line 194, in <module>
    ftnet.load_state_dict(ckpt_ft['model'])
  File "/anaconda/envs/SBDD-3D/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1482, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for FrontierNetwork:
        Missing key(s) in state_dict: "frontier_pred.layers.0.weight", "frontier_pred.layers.0.bias", "frontier_pred.layers.1.weight", "frontier_pred.layers.1.bias", "frontier_pred.layers.2.weight", "frontier_pred.layers.2.bias", "frontier_pred.layers.3.weight", "frontier_pred.layers.3.bias". 
        Unexpected key(s) in state_dict: "field.lin1.weight", "field.lin2.weight", "field.lin2.bias", "field.nn.0.weight", "field.nn.0.bias", "field.nn.2.weight", "field.nn.2.bias", "field.classifier.0.weight", "field.classifier.0.bias", "field.classifier.2.weight", "field.classifier.2.bias", "field.property_pred.0.weight", "field.property_pred.0.bias", "field.property_pred.2.weight", "field.property_pred.2.bias", "field.distance_expansion.offset".

Which step goes wrong? How can I sample on trained models?

Thanks!

We replaced the empty ligand_dict with docked fragments in the script 'samples_for_pdb.py', but the outputs of SDF files do not contain our prepared fragments anymore. It seems your model still starts from scratch not considering the input fragments at all.

# ligand_dict = {
#     'element': torch.empty([0,], dtype=torch.long),
#     'pos': torch.empty([0, 3], dtype=torch.float),

#     'atom_feature': torch.empty([0, 8], dtype=torch.float),
#     'bond_index': torch.empty([2, 0], dtype=torch.long),
#     'bond_type': torch.empty([0,], dtype=torch.long),
# }

path='ZINC_2_frag.sdf'
ligand_dict = parse_sdf_file(path)
ligand_dict=torchify_dict(ligand_dict)

Please share your codes or pipeline about the Linker Prediction of your paper.

Hi, thanks for sharing,

could you please tell me how to use SBDD to generate molecules on MY protein (which already have a ligand) ? not the given protein from crossdock dataset

Thank you very much!

Hi @luost26,

Thank you for this interesting work. I have a question about the bond inference process.

After sampling, we have the generated atom types and their positions, and our goal is to infer the bonds among these atoms. In this line, it seems that we can decide if an added bond should be aromatic or not. For this line, I am confused about what is the indicator for test data used in generation. From my understanding, what we have after sampling are generated atom types and their positions. How can we determine if the generated atoms/bonds should be aromatic or not?

Thank you in advance. Any discussions from others are also warmly welcome.

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.

Hi, I've read your paper and the idea is impressive. I successfully run your sample code, but each time I run the same input I got different results. Though the seed is set fixed, it seems that there is still some randomness in the sample procedure. This problem appears on both CPU and CUDA environments. Could you explain which step may cause this? If possible, how can we fix this so that same results can be reproduced? Thanks! Kris

Hi!

generated molecules from my personal pdb as input(not seems to be in crossdocked file) overall seem to be short. I have 2 questions

How can we start with a given scaffold?

If i increase the pocket size, would it increase? Because my results show that they typically don't

Thanks

File "/sample.py", line 180, in data = testset[args.data_id] File "/conda/envs/mol/lib/python3.9/site-packages/torch/utils/data/dataset.py", line 311, in getitem return self.dataset[self.indices[idx]] IndexError: list index out of range When I ran the sample, it threw the error. I have checked the dataset and subdataset, both the length of them are 0.

--------------------------------------------- Error Information --------------------------------------

(SBDD-3D) root@10-90-43-152:/home/jenkins/zhuyuxiao/SBDD_Project/3D-Generative-SBDD# python sample.py ./configs/sample.yml --data_id {i} Traceback (most recent call last): File "sample.py", line 7, in from torch_geometric.data import Batch File "/home/miniconda3/envs/SBDD-3D/lib/python3.8/site-packages/torch_geometric/init.py", line 4, in import torch_geometric.data File "/home/miniconda3/envs/SBDD-3D/lib/python3.8/site-packages/torch_geometric/data/init.py", line 1, in from .data import Data File "/home/miniconda3/envs/SBDD-3D/lib/python3.8/site-packages/torch_geometric/data/data.py", line 3, in from torch_geometric.typing import OptTensor, NodeType, EdgeType File "/home/miniconda3/envs/SBDD-3D/lib/python3.8/site-packages/torch_geometric/typing.py", line 4, in from torch_sparse import SparseTensor File "/home/miniconda3/envs/SBDD-3D/lib/python3.8/site-packages/torch_sparse/init.py", line 19, in torch.ops.load_library(spec.origin) File "/home/miniconda3/envs/SBDD-3D/lib/python3.8/site-packages/torch/_ops.py", line 110, in load_library ctypes.CDLL(path) File "/home/miniconda3/envs/SBDD-3D/lib/python3.8/ctypes/init.py", line 373, in init self._handle = _dlopen(self._name, mode) OSError: libc10_cuda.so: cannot open shared object file: No such file or directory

--------------------------------------------- My Conda Env --------------------------------------

packages in environment at /home/miniconda3/envs/SBDD-3D:

Name Version Build Channel _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge absl-py 1.0.0 pyhd8ed1ab_0 conda-forge aiohttp 3.7.4.post0 py38h497a2fe_0 conda-forge async-timeout 3.0.1 py_1000 conda-forge attrs 21.4.0 pyhd8ed1ab_0 conda-forge biopython 1.79 py38h497a2fe_0 conda-forge blas 1.0 mkl conda-forge blinker 1.4 py_1 conda-forge boost 1.74.0 py38hc10631b_3 conda-forge boost-cpp 1.74.0 h9359b55_0 conda-forge brotlipy 0.7.0 py38h497a2fe_1001 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.18.1 h7f8727e_0 defaults ca-certificates 2022.07.19 h06a4308_0 defaults cachetools 4.2.4 pyhd8ed1ab_0 conda-forge cairo 1.16.0 h3fc0475_1005 conda-forge certifi 2022.6.15 py38h578d9bd_0 conda-forge cffi 1.14.6 py38ha65f79e_0 conda-forge chardet 4.0.0 py38h578d9bd_3 conda-forge charset-normalizer 2.0.10 pyhd8ed1ab_0 conda-forge click 8.0.3 py38h578d9bd_1 conda-forge colorama 0.4.4 pyh9f0ad1d_0 conda-forge cpuonly 2.0 0 pytorch cryptography 35.0.0 py38ha5dfef3_0 conda-forge cudatoolkit 11.3.1 h2bc3f7f_2 defaults cycler 0.11.0 pyhd8ed1ab_0 conda-forge dataclasses 0.8 pyhc8e2a94_3 conda-forge decorator 4.4.2 py_0 conda-forge easydict 1.9 py_0 conda-forge ffmpeg 4.3 hf484d3e_0 pytorch fontconfig 2.13.1 hba837de_1005 conda-forge freetype 2.11.0 h70c0345_0 defaults gettext 0.19.8.1 h0b5b191_1005 conda-forge giflib 5.2.1 h36c2ea0_2 conda-forge glib 2.69.1 h4ff587b_1 defaults gmp 6.2.1 h58526e2_0 conda-forge gnutls 3.6.15 he1e5248_0 defaults google-auth 2.3.3 pyh6c4a22f_0 conda-forge google-auth-oauthlib 0.4.6 pyhd8ed1ab_0 conda-forge googledrivedownloader 0.4 pyhd3deb0d_1 conda-forge grpcio 1.42.0 py38hce63b2e_0 defaults icu 67.1 he1b5a44_0 conda-forge idna 3.1 pyhd3deb0d_0 conda-forge importlib-metadata 4.10.0 py38h578d9bd_0 conda-forge intel-openmp 2021.4.0 h06a4308_3561 defaults jinja2 3.0.3 pyhd8ed1ab_0 conda-forge joblib 1.1.0 pyhd8ed1ab_0 conda-forge jpeg 9d h36c2ea0_0 conda-forge kiwisolver 1.3.1 py38h1fd1430_1 conda-forge lame 3.100 h7f98852_1001 conda-forge lcms2 2.12 hddcbb42_0 conda-forge ld_impl_linux-64 2.35.1 hea4e1c9_2 conda-forge libffi 3.3 h58526e2_2 conda-forge libgcc-ng 12.1.0 h8d9b700_16 conda-forge libgfortran-ng 7.5.0 h14aa051_20 conda-forge libgfortran4 7.5.0 h14aa051_20 conda-forge libgomp 12.1.0 h8d9b700_16 conda-forge libiconv 1.15 h516909a_1006 conda-forge libidn2 2.3.2 h7f98852_0 conda-forge libpng 1.6.37 h21135ba_2 conda-forge libprotobuf 3.15.8 h780b84a_0 conda-forge libstdcxx-ng 9.3.0 h6de172a_19 conda-forge libtasn1 4.16.0 h27cfd23_0 defaults libtiff 4.2.0 hbd63e13_2 conda-forge libunistring 0.9.10 h7f98852_0 conda-forge libuuid 2.32.1 h7f98852_1000 conda-forge libuv 1.40.0 h7f98852_0 conda-forge libwebp 1.2.0 h3452ae3_0 conda-forge libwebp-base 1.2.0 h7f98852_2 conda-forge libxcb 1.13 h7f98852_1003 conda-forge libxml2 2.9.10 h72b56ed_2 conda-forge libzlib 1.2.11 h36c2ea0_1013 conda-forge lz4-c 1.9.3 h9c3ff4c_1 conda-forge markdown 3.3.6 pyhd8ed1ab_0 conda-forge markupsafe 2.0.1 py38h497a2fe_0 conda-forge matplotlib-base 3.3.4 py38h0efea84_0 conda-forge mkl 2021.4.0 h06a4308_640 defaults mkl-service 2.4.0 py38h497a2fe_0 conda-forge mkl_fft 1.3.1 py38hd3c417c_0 defaults mkl_random 1.2.2 py38h1abd341_0 conda-forge multidict 5.2.0 py38h7f8727e_2 defaults ncurses 6.3 h7f8727e_2 defaults nettle 3.7.3 hbbd107a_1 defaults networkx 2.5.1 pyhd8ed1ab_0 conda-forge numpy 1.21.2 py38h20f2e39_0 defaults numpy-base 1.21.2 py38h79a1101_0 defaults oauthlib 3.1.1 pyhd8ed1ab_0 conda-forge olefile 0.46 pyh9f0ad1d_1 conda-forge openbabel 3.1.1 py38hf4b5c11_1 conda-forge openh264 2.1.1 h780b84a_0 conda-forge openssl 1.1.1q h166bdaf_0 conda-forge pandas 1.2.5 py38h1abd341_0 conda-forge pcre 8.45 h9c3ff4c_0 conda-forge pillow 8.4.0 py38h5aabda8_0 defaults pip 21.2.4 pyhd8ed1ab_0 conda-forge pixman 0.38.0 h516909a_1003 conda-forge protobuf 3.15.8 py38h709712a_0 conda-forge pthread-stubs 0.4 h36c2ea0_1001 conda-forge pyasn1 0.4.8 py_0 conda-forge pyasn1-modules 0.2.7 py_0 conda-forge pycairo 1.20.1 py38hf61ee4a_0 conda-forge pycparser 2.21 pyhd8ed1ab_0 conda-forge pyg 2.0.3 py38_torch_1.10.0_cpu pyg pyjwt 2.3.0 pyhd8ed1ab_1 conda-forge pyopenssl 21.0.0 pyhd8ed1ab_0 conda-forge pyparsing 3.0.6 pyhd8ed1ab_0 conda-forge pysocks 1.7.1 py38h578d9bd_5 conda-forge python 3.8.12 h12debd9_0 defaults python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge python-lmdb 0.99 py38h709712a_0 conda-forge python-louvain 0.15 pyhd8ed1ab_1 conda-forge python_abi 3.8 2_cp38 conda-forge pytorch 1.10.1 py3.8_cpu_0 pytorch pytorch-cluster 1.5.9 py38_torch_1.10.0_cu113 pyg pytorch-mutex 1.0 cpu pytorch pytorch-scatter 2.0.9 py38_torch_1.10.0_cu113 pyg pytorch-sparse 0.6.12 py38_torch_1.10.0_cu113 pyg pytorch-spline-conv 1.2.1 py38_torch_1.10.0_cu113 pyg pytz 2021.3 pyhd8ed1ab_0 conda-forge pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge pyyaml 5.4.1 py38h497a2fe_0 conda-forge rdkit 2020.09.5 py38h2bca085_0 conda-forge readline 8.1.2 h7f8727e_1 defaults reportlab 3.5.68 py38hadf75a6_0 conda-forge requests 2.27.1 pyhd8ed1ab_0 conda-forge requests-oauthlib 1.3.0 pyh9f0ad1d_0 conda-forge rsa 4.8 pyhd8ed1ab_0 conda-forge scikit-learn 1.0.2 py38h51133e4_1 defaults scipy 1.7.3 py38hc147768_0 defaults setuptools 58.0.4 py38h578d9bd_2 conda-forge six 1.16.0 pyh6c4a22f_0 conda-forge sqlalchemy 1.3.23 py38h497a2fe_0 conda-forge sqlite 3.37.0 hc218d9a_0 defaults tensorboard 2.7.0 pyhd8ed1ab_0 conda-forge tensorboard-data-server 0.6.0 py38h2b97feb_0 conda-forge tensorboard-plugin-wit 1.8.1 pyhd8ed1ab_0 conda-forge threadpoolctl 3.0.0 pyh8a188c0_0 conda-forge tk 8.6.11 h21135ba_0 conda-forge torchaudio 0.10.1 py38_cu113 pytorch torchvision 0.11.2 py38_cu113 pytorch tornado 6.1 py38h497a2fe_1 conda-forge tqdm 4.62.3 pyhd8ed1ab_0 conda-forge typing-extensions 3.10.0.2 hd8ed1ab_0 conda-forge typing_extensions 3.10.0.2 pyha770c72_0 conda-forge urllib3 1.26.8 pyhd8ed1ab_1 conda-forge werkzeug 2.0.1 pyhd8ed1ab_0 conda-forge wheel 0.37.1 pyhd8ed1ab_0 conda-forge xorg-kbproto 1.0.7 h7f98852_1002 conda-forge xorg-libice 1.0.10 h7f98852_0 conda-forge xorg-libsm 1.2.3 hd9c2040_1000 conda-forge xorg-libx11 1.7.2 h7f98852_0 conda-forge xorg-libxau 1.0.9 h7f98852_0 conda-forge xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge xorg-libxext 1.3.4 h7f98852_1 conda-forge xorg-libxrender 0.9.10 h7f98852_1003 conda-forge xorg-renderproto 0.11.1 h7f98852_1002 conda-forge xorg-xextproto 7.3.0 h7f98852_1002 conda-forge xorg-xproto 7.0.31 h7f98852_1007 conda-forge xz 5.2.5 h516909a_1 conda-forge yacs 0.1.6 py_0 conda-forge yaml 0.2.5 h516909a_0 conda-forge yarl 1.6.3 py38h497a2fe_2 conda-forge zipp 3.7.0 pyhd8ed1ab_1 conda-forge zlib 1.2.11 h36c2ea0_1013 conda-forge zstd 1.4.9 ha95c52a_0 conda-forge

thanks for your great work!

i got the error when running sample.py:

ValueError: zero-size array to reduction operation maximum which has no identity

the reason is query_tmp = [], after sampling. https://github.com/luost26/3D-Generative-SBDD/blob/fabc98612d06713d5bab8e58bf5c62f229e718f2/sample.py#L282

do you know how to fix it? thanks

Hi, your work is very fantastic! May I ask two questions? What is the meaning of the numbers (for example 0.367042 as below) in the data/crossdocked_pocket10/index.pkl? The index is like the following: [('1B57_HUMAN_25_300_0/5u98_D_rec_5u98_1kx_lig_tt_min_0_pocket10.pdb', '1B57_HUMAN_25_300_0/5u98_D_rec_5u98_1kx_lig_tt_min_0.sdf', '1B57_HUMAN_25_300_0/5u98_D_rec.pdb', 0.367042), ...,]

The index implies that we need to give the receptor protein .pdb file. However in the cross docked_pocked10, we only saw two pocked substructure and didn't protein .pdb file.

Hi, Can you please explain the data structure of crossdocked_pocket10? For example: 1eqc_A_rec_1eqc_cts_lig_tt_docked_0_pocket10.pdb So here if we split using _ what are the terms like 1eqc, cts etc.

Same for .sdf files like 4m81_A_rec_4m81_glf_lig_tt_min_0.sdf

Also how did you convert crossdocked original dataset to this pocket dataset and then how to dump data into index.pkl file? Thanks

I read your paper which looks great. I tried to run according to the instruction, sample.py, using the sample.yml and the two model ckpts downloaded from google drive. I waited for a long time (like half an hour) , and it still cannot produce a valid molecule, it shows [Pool] Queue 300 | Finished 0 | Failed 55.

I tried to debug, I found that most generated graph does not pass the if statement "if data_next.status == STATUS_FINISHED:". Even for those few that can pass the if statement, it failed during "rdmol = reconstruct_from_generated(data_next)", which throws exception shown as "Ignoring, because reconstruction error encountered."