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Can you please share the Validity and ∆Binding metrics you used to evaluate the performance of your generative model?

Hi, I met with the error while running: CUDA_VISIBLE_DEVICES=0 python main_gen.py

Traceback (most recent call last):
  File "main_gen.py", line 6, in <module>
    runner = Runner(conf)
  File "/home/yipyewmun/GitHub/GraphBP/GraphBP/runner.py", line 25, in __init__
    self.model = GraphBP(**conf['model'])
  File "/home/yipyewmun/GitHub/GraphBP/GraphBP/model/graphbp.py", line 40, in __init__
    self.feat_net = self.feat_net.to('cuda')
  File "/home/yipyewmun/anaconda3/envs/gen/lib/python3.8/site-packages/torch/nn/modules/module.py", line 852, in to
    return self._apply(convert)
  File "/home/yipyewmun/anaconda3/envs/gen/lib/python3.8/site-packages/torch/nn/modules/module.py", line 530, in _apply
    module._apply(fn)
  File "/home/yipyewmun/anaconda3/envs/gen/lib/python3.8/site-packages/torch/nn/modules/module.py", line 552, in _apply
    param_applied = fn(param)
  File "/home/yipyewmun/anaconda3/envs/gen/lib/python3.8/site-packages/torch/nn/modules/module.py", line 850, in convert
    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
RuntimeError: CUDA error: the provided PTX was compiled with an unsupported toolchain.
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

Any possible ideas on how I can resolve this? :)

Hi，thanks for your work!
When I run the main_gen.py , the following ERROR message is displayed:

nohup: ignoring input
Epoch: 33
Traceback (most recent call last):
File "main_gen.py", line 30, in
all_mol_dicts = runner.generate(num_gen, temperature=[node_temp, dist_temp, angle_temp, torsion_temp], max_atoms=max_atoms, min_atoms=min_atoms, focus_th=focus_th, contact_th=contact_th, add_final=True, known_binding_site=known_binding_site)
File "/public/thw/GraphBP/GraphBP/runner.py", line 120, in generate
data_lines = pd.read_csv(
File "/home/anaconda/software/envs/GraphBP/lib/python3.8/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/home/anaconda/software/envs/GraphBP/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 680, in read_csv
return _read(filepath_or_buffer, kwds)
File "/home/anaconda/software/envs/GraphBP/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 575, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/home/anaconda/software/envs/GraphBP/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 933, in init
self._engine = self._make_engine(f, self.engine)
File "/home/anaconda/software/envs/GraphBP/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 1217, in _make_engine
self.handles = get_handle( # type: ignore[call-overload]
File "/home/anaconda/software/envs/GraphBP/lib/python3.8/site-packages/pandas/io/common.py", line 789, in get_handle
handle = open(
FileNotFoundError: [Errno 2] No such file or directory: './data/crossdock2020/selected_test_targets.types'

Would you kindly help me to solve the problem ? Thanks !

Hi, thanks for the awesome work.
I did not find the code to evaluate the generated molecules, e.g., the code to compute the affinity.
Would you kindly provide this part of code?

Hi,

I have noticed that the dataset consists of 500K samples after filtering which is described in your paper. However, the it2_tt_0_lowrmsd_mols_test0_fixed.types file includes more than 480K samples while it2_tt_0_lowrmsd_mols_test0_fixed.types file includes more than 230K samples.

You have selected 10 target proteins as LiGAN for test evaluation, but 90 protein-ligand pairs in the test set as reference. Does it mean that generated 100 samples for each target protein would be compared with these pairs?

Could you please tell me how many training samples can obtain the results in your paper?

Besides, why did you not compare the model with Luo's model (Luo, S., Guan, J., Ma, J., and Peng, J. A 3d generative model for structure-based drug design. In Thirty-Fifth Conference on Neural Information Processing Systems, 2021a), which you mentioned in the related works?

Thank you very much!

Thanks for the great work and sharing the code.

I run it according to the instruction using the provided train_epoch33 model.
I test it on one pocket, I picked a line from the train file and put into the test_selected_targets.type as below:
1 0 1.90832 1A1C_MALDO_2_433_0/1m7y_A_rec.pdb 1A1C_MALDO_2_433_0/1m7y_ppg_uff2.sdf.gz #-4.20313

I run main_gen then main_eval.py. But main_eval will raise exception for every generated molecule by the line in bond_adder:
Chem.SanitizeMol(rd_mol,Chem.SANITIZE_ALL^Chem.SANITIZE_KEKULIZE)
So I modify it to
Chem.SanitizeMol(rd_mol)
Then everything is smooth.

However, the results look really strange. I can post some here for your reference.

Is there something I do it wrong in the step described above? Looking forward to your reply.

#

Dear Authors, when we run your code "python scripts/split_sdf.py data/crossdock2020/it2_tt_0_lowrmsd_mols_train0_fixed.types data/crossdock2020", we have downloaded and replaced the old train0_fixed.types file, but we also got one BUG as shown in the screen shoot.
How can I solve this? Ask for help, thank you a lot!

Hello! I use main_gen.py to generate ligands for the data in [https://github.com/pengxingang/Pocket2Mol/blob/main/data/test_list.tsv], which is also in the crossdocked2020 dataset, but lots of the ligands I generate are not in the protein pocket. (Figure 1 shows the structure of protein pocket with the reference ligand, and Figure 2 shows the structure of protein pocket with the generated ligands) Are these results correct?

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.