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random-zhang amelie-iska greenrace66 gsgithub17 ashtawy sacombs rosstewart zhengzha2000 xinheng-he yanbosmu xiongb-lab feigeliudan01 shunsunsun zhichenml forcefield 1saacrankin yannickshaofengsun zhangzhishuopmdm's Issues
Description of cut pocket process
Hi - I could not find this in the Methods section of the paper either, so asking here.
What is / was the process for generating the cut pocket for pdb structures, e.g. 2VUK:
data/2VUKcut10/2VUKcut10_pocket.pdb
What do cut10 and cut10_pocket refer to exactly?
Thanks,
RLab
Read ligand sdf file error
Thanks for your amazing work!
When handling ligand molecules with RDKit, discrepancies in bond types may arise, such as carbon atoms exhibiting pentavalency and nitrogen atoms displaying tetravalent. Why these errors happen and how to rectify them for accurate molecular processing?
The example reading code,:
mol = Chem.MolFromMolFile('2v5z_A_rec_2v5z_fad_lig_tt_min_0.sdf')
The sdf file is attached.
The log I got: "Explicit valence for atom # 13 C, 5, is greater than permitted"
No matching distribution found for mkl==2017.0.3
Hello,
Thank you for your excellent work. I got an error when I built the environment of adt.
ERROR: Could not find a version that satisfies the requirement mkl==2017.0.3 (from versions: 2018.0.0, 2018.0.3, 2019.0, 2021.1.1, 2021.2.0, 2021.3.0, 2021.4.0, 2022.0.1, 2022.0.2, 2022.1.0, 2022.2.0, 2022.2.1, 2023.0.0, 2023.1.0, 2023.2.0, 2024.0.0, 2024.1.0)
ERROR: No matching distribution found for mkl==2017.0.3
"2017.0.3" seems is a old version and I can't download it from pypi. Also, I can't find a website to get mkl-2017.0.3. So, my question is that:
Can I use a newer version? Such as 2018.0.0.
And when I checked the package list in the adt environment by "conda list", I found that mkl was in the list - mkl-2022.0.1. So, here is my question:
Is there any problem if I ues the newer mkl of 2022.0.1?
Is it necessary for this environment to use the version of 2017.0.3?
checkpoint file for testing
Hey @Layne-Huang,
Great work!
I'm trying to follow by testing a few example commands in the repo, for example the first command under "Inference", python -u sample_batch.py --ckpt <checkpoint> --num_samples <number of samples> --sampling_type generalized, is there a checkpoint file I need to provide?
I tried without it but I got error here below:
Entropy of n_nodes: H[N] -1.3862943649291992
Traceback (most recent call last):
File "~/softwares/mambaforge3/envs/pmdm/lib/python3.9/site-packages/torch/serialization.py", line 541, in _check_seekable
f.seek(f.tell())
AttributeError: 'NoneType' object has no attribute 'seek'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "~/softwares/PMDM/sample_batch.py", line 130, in <module>
ckpt = torch.load(args.ckpt)
File "~/softwares/mambaforge3/envs/pmdm/lib/python3.9/site-packages/torch/serialization.py", line 998, in load
with _open_file_like(f, 'rb') as opened_file:
File "~/softwares/mambaforge3/envs/pmdm/lib/python3.9/site-packages/torch/serialization.py", line 450, in _open_file_like
return _open_buffer_reader(name_or_buffer)
File "~/softwares/mambaforge3/envs/pmdm/lib/python3.9/site-packages/torch/serialization.py", line 435, in __init__
_check_seekable(buffer)
File "~/softwares/mambaforge3/envs/pmdm/lib/python3.9/site-packages/torch/serialization.py", line 544, in _check_seekable
raise_err_msg(["seek", "tell"], e)
File "~/softwares/mambaforge3/envs/pmdm/lib/python3.9/site-packages/torch/serialization.py", line 537, in raise_err_msg
raise type(e)(msg)
AttributeError: 'NoneType' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.
There is no evaluate.py function
Hi - there is no evaluate.py script that I could find.
Thanks,
RLab
sample_frag.py doesn't keep the seed frag fixed
My input molecular and fixed segment is as followed:
For some sample results, the atom type changed.
And others may not have the seed frag.
What can I do to fix it?
About mol.yml
I can't find the mol.yml file when installing the environment
How to sample small molecules for a specific pocket in a PDB file？
Hi Dr. Huang
Thank you for your great work.
If I want to sample small molecules for a specific pocket of a protein, how should I use the script of sample_for_pdb.py？ Currently, sample_for_pdb.py only seems to be able to sample whole proteins, or use split_pocket_ligand.py to get known pockets where small molecules are known to bind.
Best,
Yuhan
Code Issue
Hi，Layne-Huang
Thank you very much for your work, but while reading the code, at line 155 of the utils/protein_ligand.py file, the code reads as follows,
def to_dict_atom_cutoff(self, ligand_pos, cutoff):
and I would like to know why your code sets cutoff to 8 here, is this a priori knowledge?
ValueError: too many values to unpack (expected 2)
Hi,
After reading your paper on NC, I downloaded and tried your code. However, there is an error showing ValueError: too many values to unpack (expected 2). It seems like instead of returning two tuples for hidden_out and coors_out, the function outputs a tuple with a length of 2880.
My input is python -u sample_for_pdb.py --ckpt 500.pt --pdb_path 6M1I_Pocket_pdb.pdb --num_atom 30 --num_samples 10 --sampling_type generalized, the whole error meassage is
h: 1: module: not found Entropy of n_nodes: H[N] -1.3862943649291992 [2024-04-12 14:01:23,597::test::INFO] Namespace(pdb_path='6M1I_Pocket_pdb.pdb', sdf_path=None, num_atom=30, build_method='reconstruct', config=None, cuda=True, ckpt='500.pt', save_traj=False, num_samples=10, batch_size=10, resume=None, tag='', clip=1000.0, n_steps=1000, global_start_sigma=inf, w_global_pos=1.0, w_local_pos=1.0, w_global_node=1.0, w_local_node=1.0, sampling_type='generalized', eta=1.0) [2024-04-12 14:01:23,597::test::INFO] {'model': {'type': 'diffusion', 'network': 'MDM_full_pocket_coor_shared', 'hidden_dim': 128, 'protein_hidden_dim': 128, 'num_convs': 3, 'num_convs_local': 3, 'protein_num_convs': 2, 'cutoff': 3.0, 'g_cutoff': 6.0, 'encoder_cutoff': 6.0, 'time_emb': True, 'atom_num_emb': False, 'mlp_act': 'relu', 'beta_schedule': 'sigmoid', 'beta_start': 1e-07, 'beta_end': 0.002, 'num_diffusion_timesteps': 1000, 'edge_order': 3, 'edge_encoder': 'mlp', 'smooth_conv': False, 'num_layer': 9, 'feats_dim': 5, 'soft_edge': True, 'norm_coors': True, 'm_dim': 128, 'context': 'None', 'vae_context': False, 'num_atom': 10, 'protein_feature_dim': 31}, 'train': {'seed': 2021, 'batch_size': 16, 'val_freq': 250, 'max_iters': 500, 'max_grad_norm': 10.0, 'num_workers': 4, 'anneal_power': 2.0, 'optimizer': {'type': 'adam', 'lr': 0.001, 'weight_decay': 0.0, 'beta1': 0.95, 'beta2': 0.999}, 'scheduler': {'type': 'plateau', 'factor': 0.6, 'patience': 10, 'min_lr': 1e-06}, 'transform': {'mask': {'type': 'mixed', 'min_ratio': 0.0, 'max_ratio': 1.2, 'min_num_masked': 1, 'min_num_unmasked': 0, 'p_random': 0.5, 'p_bfs': 0.25, 'p_invbfs': 0.25}, 'contrastive': {'num_real': 50, 'num_fake': 50, 'pos_real_std': 0.05, 'pos_fake_std': 2.0}}}, 'dataset': {'name': 'crossdock', 'type': 'pl', 'path': './data/crossdocked_pocket10', 'split': './data/split_by_name.pt'}} [2024-04-12 14:01:23,597::test::INFO] Loading crossdock data... Entropy of n_nodes: H[N] -3.543935775756836 [2024-04-12 14:01:23,597::test::INFO] Loading data... [2024-04-12 14:01:23,615::test::INFO] Building model... [2024-04-12 14:01:23,615::test::INFO] MDM_full_pocket_coor_shared {'type': 'diffusion', 'network': 'MDM_full_pocket_coor_shared', 'hidden_dim': 128, 'protein_hidden_dim': 128, 'num_convs': 3, 'num_convs_local': 3, 'protein_num_convs': 2, 'cutoff': 3.0, 'g_cutoff': 6.0, 'encoder_cutoff': 6.0, 'time_emb': True, 'atom_num_emb': False, 'mlp_act': 'relu', 'beta_schedule': 'sigmoid', 'beta_start': 1e-07, 'beta_end': 0.002, 'num_diffusion_timesteps': 1000, 'edge_order': 3, 'edge_encoder': 'mlp', 'smooth_conv': False, 'num_layer': 9, 'feats_dim': 5, 'soft_edge': True, 'norm_coors': True, 'm_dim': 128, 'context': 'None', 'vae_context': False, 'num_atom': 10, 'protein_feature_dim': 31} sdf idr: generate_ref Entropy of n_nodes: H[N] -3.543935775756836 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 314.14it/s] 0%| | 0/2 [00:00<?, ?it/s]1 /home/yang2531/Documents/Project/PMDM/models/common.py:485: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated. Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:623.) bgraph_adj = torch.sparse.LongTensor( sample: 0it [00:00, ?it/s] 0%| | 0/2 [00:00<?, ?it/s] Traceback (most recent call last): File "/home/yang2531/Documents/Project/PMDM/sample_for_pdb.py", line 350, in <module> pos_gen, pos_gen_traj, atom_type, atom_traj = model.langevin_dynamics_sample( File "/home/yang2531/Documents/Project/PMDM/models/epsnet/MDM_pocket_coor_shared.py", line 790, in langevin_dynamics_sample net_out = self.net( File "/home/yang2531/Documents/Project/PMDM/models/epsnet/MDM_pocket_coor_shared.py", line 478, in net node_attr_global, pos_attr_global = self.encoder_global( File "/home/yang2531/anaconda3/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/home/yang2531/anaconda3/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/home/yang2531/Documents/Project/PMDM/models/encoders/egnn.py", line 476, in forward x = layer(x, edge_index, edge_attr, batch=batch, ligand_batch=ligand_batch, size=bsize, linker_mask=linker_mask) File "/home/yang2531/anaconda3/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/home/yang2531/anaconda3/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/home/yang2531/Documents/Project/PMDM/models/encoders/egnn.py", line 215, in forward hidden_out, coors_out = self.propagate(edge_index, x=feats, edge_attr=edge_attr_feats, ValueError: too many values to unpack (expected 2)
Any help is hugely appreciated, your research is so fancinating that I really want to try to apply it!
Inquiry on Utilizing Your Trained Model for Molecule Generation in Protein Pockets
Hello,Thank you for sharing your excellent work! Could you please guide me on how to use your trained model to generate molecules for my protein pockets?
Best regards
generate novel molecules from a seed fragment using sample_frag.py
Hey @Layne-Huang,
Is there a example mole file for generating novel molecules from a seed fragment? I'm using sample_frag.py with command python -u sample_frag.py --ckpt data/500.pt --pdb_path receptor.pdb --mol_file frag.sdf --keep_index 1 --num_atom 20 --num_samples 10 --sampling_type generalized
Also what do I put after the --keep_index option? Do you also have an example command line working for a real world case?
Thanks!
ImportError, libtorch_cuda.so: undefined symbol
Hi,
After reading your article published in NC, I have created and downloaded relative files to generate molecules for my protein. However, when I used python -u sample_for_pdb.py --ckpt 500.pt --pdb_path protein/mypro_nolig.pdb --num_atoms 70 --num_samples 100 --sampling_type generalized, an error message showed in the screen
Traceback (most recent call last): File "/media/data/software/conda/PMDM-main/sample_for_pdb.py", line 10, in <module> from evaluation import * File "/media/data/software/conda/PMDM-main/evaluation/__init__.py", line 1, in <module> from .evaluation_metrics import * File "/media/data/software/conda/PMDM-main/evaluation/evaluation_metrics.py", line 7, in <module> import torch File "/home/anaconda3/envs/mol/lib/python3.9/site-packages/torch/__init__.py", line 235, in <module> from torch._C import * # noqa: F403 ImportError: /home/anaconda3/envs/mol/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so: undefined symbol: cudaLaunchKernelExC, version libcudart.so.11.0
Can you please help me to solve this problem? Thanks!
Missing File for training
Dear Author,
I sincerely thank you for your great work.
Could you please add train.py to the utils folder?
It was missed in the current repo. Thank you!
Some packages in mol.yml are incorrect, such as cudatoolkit, geometric, scatter, etc
Below is a Conda environment that can run PMDM correctly:
name: pmdm
channels:
- nvidia
- conda-forge
- pytorch
- defaults
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_gnu
- acpype=2022.7.21=pyhd8ed1ab_1
- ambertools=22.4=py39h804d063_0
- arpack=3.7.0=hdefa2d7_2
- blas=1.0=mkl
- boost=1.78.0=py39hda80f44_5
- boost-cpp=1.78.0=h2c5509c_4
- bottleneck=1.3.7=py39ha9d4c09_0
- brotli=1.0.9=h5eee18b_7
- brotli-bin=1.0.9=h5eee18b_7
- brotli-python=1.0.9=py39h6a678d5_7
- bzip2=1.0.8=hd590300_5
- c-ares=1.19.1=h5eee18b_0
- ca-certificates=2024.3.11=h06a4308_0
- cairo=1.18.0=h3faef2a_0
- certifi=2024.2.2=py39h06a4308_0
- charset-normalizer=2.0.4=pyhd3eb1b0_0
- contourpy=1.2.0=py39hdb19cb5_0
- cuda-cudart=11.8.89=0
- cuda-cupti=11.8.87=0
- cuda-libraries=11.8.0=0
- cuda-nvrtc=11.8.89=0
- cuda-nvtx=11.8.86=0
- cuda-runtime=11.8.0=0
- cuda-version=11.8=h70ddcb2_3
- cudatoolkit=11.8.0=h4ba93d1_13
- cudatoolkit-dev=11.7.0=h1de0b5d_6
- cudnn=8.9.7.29=hbc23b4c_3
- curl=8.5.0=hdbd6064_0
- cycler=0.11.0=pyhd3eb1b0_0
- cython=3.0.10=py39h5eee18b_0
- expat=2.5.0=h6a678d5_0
- ffmpeg=4.3=hf484d3e_0
- fftw=3.3.10=nompi_hc118613_108
- filelock=3.13.1=py39h06a4308_0
- font-ttf-dejavu-sans-mono=2.37=hd3eb1b0_0
- font-ttf-inconsolata=2.001=hcb22688_0
- font-ttf-source-code-pro=2.030=hd3eb1b0_0
- font-ttf-ubuntu=0.83=h8b1ccd4_0
- fontconfig=2.14.2=h14ed4e7_0
- fonts-anaconda=1=h8fa9717_0
- fonts-conda-ecosystem=1=hd3eb1b0_0
- freetype=2.12.1=h4a9f257_0
- gdbm=1.18=hd4cb3f1_4
- glib=2.78.4=h6a678d5_0
- glib-tools=2.78.4=h6a678d5_0
- gmp=6.2.1=h295c915_3
- gmpy2=2.1.2=py39heeb90bb_0
- gnutls=3.6.15=he1e5248_0
- greenlet=3.0.1=py39h6a678d5_0
- hdf4=4.2.15=h9772cbc_5
- hdf5=1.12.2=nompi_h4df4325_101
- icu=73.2=h59595ed_0
- idna=3.4=py39h06a4308_0
- importlib_resources=6.1.1=py39h06a4308_1
- intel-openmp=2023.1.0=hdb19cb5_46306
- jinja2=3.1.3=py39h06a4308_0
- jpeg=9e=h5eee18b_1
- kiwisolver=1.4.4=py39h6a678d5_0
- krb5=1.20.1=h143b758_1
- lame=3.100=h7b6447c_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.38=h1181459_1
- lerc=3.0=h295c915_0
- libaec=1.1.3=h59595ed_0
- libblas=3.9.0=1_h86c2bf4_netlib
- libbrotlicommon=1.0.9=h5eee18b_7
- libbrotlidec=1.0.9=h5eee18b_7
- libbrotlienc=1.0.9=h5eee18b_7
- libcublas=11.11.3.6=0
- libcufft=10.9.0.58=0
- libcufile=1.9.1.3=0
- libcurand=10.3.5.147=0
- libcurl=8.5.0=h251f7ec_0
- libcusolver=11.4.1.48=0
- libcusparse=11.7.5.86=0
- libdeflate=1.17=h5eee18b_1
- libedit=3.1.20230828=h5eee18b_0
- libev=4.33=h7f8727e_1
- libffi=3.4.4=h6a678d5_0
- libgcc-ng=13.2.0=h807b86a_5
- libgfortran-ng=13.2.0=h69a702a_5
- libgfortran5=13.2.0=ha4646dd_5
- libglib=2.78.4=hdc74915_0
- libgomp=13.2.0=h807b86a_5
- libiconv=1.17=hd590300_2
- libidn2=2.3.4=h5eee18b_0
- libjpeg-turbo=2.0.0=h9bf148f_0
- liblapack=3.9.0=5_h92ddd45_netlib
- libnetcdf=4.9.1=nompi_h34a3ff0_101
- libnghttp2=1.57.0=h2d74bed_0
- libnpp=11.8.0.86=0
- libnsl=2.0.1=hd590300_0
- libnvjpeg=11.9.0.86=0
- libpng=1.6.39=h5eee18b_0
- libsqlite=3.45.2=h2797004_0
- libssh2=1.10.0=hdbd6064_2
- libstdcxx-ng=13.2.0=h7e041cc_5
- libtasn1=4.19.0=h5eee18b_0
- libtiff=4.5.1=h6a678d5_0
- libunistring=0.9.10=h27cfd23_0
- libuuid=2.38.1=h0b41bf4_0
- libwebp-base=1.3.2=h5eee18b_0
- libxcb=1.15=h7f8727e_0
- libxcrypt=4.4.36=hd590300_1
- libxml2=2.12.6=h232c23b_1
- libzip=1.10.1=h2629f0a_3
- libzlib=1.2.13=hd590300_5
- llvm-openmp=14.0.6=h9e868ea_0
- lz4-c=1.9.4=h6a678d5_0
- markupsafe=2.1.3=py39h5eee18b_0
- mkl=2023.1.0=h213fc3f_46344
- mkl-service=2.4.0=py39h5eee18b_1
- mkl_fft=1.3.8=py39h5eee18b_0
- mkl_random=1.2.4=py39hdb19cb5_0
- mpc=1.1.0=h10f8cd9_1
- mpfr=4.0.2=hb69a4c5_1
- mpmath=1.3.0=py39h06a4308_0
- munkres=1.1.4=py_0
- ncurses=6.4=h6a678d5_0
- netcdf-fortran=4.6.0=nompi_heb5813c_103
- nettle=3.7.3=hbbd107a_1
- networkx=3.1=py39h06a4308_0
- numexpr=2.8.7=py39h85018f9_0
- numpy=1.23.5=py39hf6e8229_1
- numpy-base=1.23.5=py39h060ed82_1
- openbabel=3.1.1=py39h2d01fe1_9
- openh264=2.1.1=h4ff587b_0
- openjpeg=2.4.0=h3ad879b_0
- openssl=3.2.1=hd590300_1
- packaging=23.2=py39h06a4308_0
- packmol=20.010=h86c2bf4_0
- pandas=2.1.3=py39hddac248_0
- parmed=3.4.4=py39h227be39_0
- pcre2=10.42=hebb0a14_0
- perl=5.34.0=h5eee18b_2
- pillow=10.2.0=py39h5eee18b_0
- pip=23.3.1=py39h06a4308_0
- pixman=0.43.2=h59595ed_0
- pycairo=1.23.0=py39hd1222b9_0
- pyparsing=3.0.9=py39h06a4308_0
- pysocks=1.7.1=py39h06a4308_0
- python=3.9.18=h0755675_1_cpython
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-tzdata=2023.3=pyhd3eb1b0_0
- python_abi=3.9=4_cp39
- pytorch=2.1.1=py3.9_cuda11.8_cudnn8.7.0_0
- pytorch-cuda=11.8=h7e8668a_5
- pytorch-mutex=1.0=cuda
- pytz=2023.3.post1=py39h06a4308_0
- pyyaml=6.0.1=py39h5eee18b_0
- rdkit=2022.09.1=py39hb00a813_2
- readline=8.2=h5eee18b_0
- reportlab=3.5.67=py39hfdd840d_1
- requests=2.31.0=py39h06a4308_1
- scipy=1.12.0=py39h5f9d8c6_0
- setuptools=68.2.2=py39h06a4308_0
- six=1.16.0=pyhd3eb1b0_1
- sqlalchemy=2.0.25=py39h5eee18b_0
- sqlite=3.41.2=h5eee18b_0
- sympy=1.12=py39h06a4308_0
- tbb=2021.8.0=hdb19cb5_0
- tk=8.6.13=noxft_h4845f30_101
- torchaudio=2.1.1=py39_cu118
- torchtriton=2.1.0=py39
- torchvision=0.16.1=py39_cu118
- typing-extensions=4.9.0=py39h06a4308_1
- typing_extensions=4.9.0=py39h06a4308_1
- tzdata=2024a=h04d1e81_0
- urllib3=2.1.0=py39h06a4308_1
- wheel=0.41.2=py39h06a4308_0
- xorg-kbproto=1.0.7=h7f98852_1002
- xorg-libice=1.1.1=hd590300_0
- xorg-libsm=1.2.4=h7391055_0
- xorg-libx11=1.8.9=h8ee46fc_0
- xorg-libxext=1.3.4=h0b41bf4_2
- xorg-libxrender=0.9.11=hd590300_0
- xorg-libxt=1.3.0=hd590300_1
- xorg-renderproto=0.11.1=h7f98852_1002
- xorg-xextproto=7.3.0=h0b41bf4_1003
- xorg-xproto=7.0.31=h27cfd23_1007
- xz=5.4.6=h5eee18b_0
- yaml=0.2.5=h7b6447c_0
- zipp=3.17.0=py39h06a4308_0
- zlib=1.2.13=hd590300_5
- zstd=1.5.5=hc292b87_0
- pip:
- aiohttp==3.9.4
- aiosignal==1.3.1
- amberlite==22.0
- amberutils==21.0
- async-timeout==4.0.3
- attrs==23.2.0
- biopython==1.83
- easydict==1.13
- einops==0.7.0
- fonttools==4.45.0
- frozenlist==1.4.1
- fsspec==2024.3.1
- joblib==1.4.0
- lmdb==1.4.1
- matplotlib==3.8.2
- mmpbsa-py==16.0
- multidict==6.0.5
- packmol-memgen==1.2.3rc0
- pdb4amber==22.0
- psutil==5.9.8
- pytraj==2.0.6
- sander==22.0
- scikit-learn==1.4.2
- threadpoolctl==3.4.0
- torch-cluster==1.6.3+pt21cu118
- torch-geometric==2.4.0
- torch-scatter==2.1.2+pt21cu118
- torch-sparse==0.6.18+pt21cu118
- torch-spline-conv==1.2.2+pt21cu118
- tqdm==4.66.2
- yarl==1.9.4
prefix: /home/user/miniconda3/envs/pmdm
Missing File in Training Process
Hello, I encountered a small issue during the training process. It seems that line 17 in the train.py file attempts to import a function named get_model from models.epsnet, but I couldn't locate the models/epsnet.py file. Could you kindly verify if the file is missing? Thank you very much for your assistance.
Inquiry about GPU Memory Requirements for Running Pre-trained Model
Hello @Layne-Huang,
First of all, thank you for developing and sharing such an impactful project！
I am interested in leveraging the pre-trained model you provided for sample novel molecules given seed fragments using the following command as mentioned in the documentation:
python -u sample_frag.py --ckpt 500.pt --pdb_path ./mol_data/receptor.pdb --mol_file ./mol_data/receptor_ligand.sdf --keep_index 1 2 3 4 5 6 7 --num_atom 16 --num_samples 50 --sampling_type generalized
However, I encountered a CUDA out of memory error:
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 243.00 GiB. GPU 0 has a total capacty of 10.75 GiB of which 8.49 GiB is free. Including non-PyTorch memory, this process has 2.26 GiB memory in use. Of the allocated memory 503.59 MiB is allocated by PyTorch, and 1.58 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
It seems that the model requires a significant amount of GPU memory, and I'm curious about the typical GPU memory requirements for running this pre-trained model with the provided command in common scenarios.
Could you please provide some guidance on the expected GPU memory usage for running the pre-trained model with the provided command? Any suggestions or recommendations for managing GPU memory effectively would also be greatly appreciated.
Thank you very much for your time and assistance!
sample_linker doesn't add atoms totally in the broken site
Hi. I use sample_linker.py to generate linker, but the added atoms don't build a new connection between fragments.
My molecular is like this:
and its index is like this:
so I use this command to generate linker:
python -u sample_linker.py --ckpt 500.pt --pdb_path 9AX6_ligand_pocket10.pdb --mol_file 9AX6_ligand.sdf --mask 1 2 3 43 51 57 --num_atom 6 --num_samples 50 --sampling_type generalized -build_method reconstruct
but I get generated molecular like this(the atoms that I want to link are labeled):
its 3D view is like this:
Could you offer me some help? Thank.
my 9AX6_ligand_pocket10.pdb is like this:
TITLE 9AX6_ligand_pocket10
REMARK 4 COMPLIES WITH FORMAT V. 3.0, 1-DEC-2006
REMARK 888
REMARK 888 WRITTEN BY MAESTRO (A PRODUCT OF SCHRODINGER, LLC)
ATOM 1 N ASP A 12 27.818 -14.945 -5.534 1.00 31.44 N1+
ATOM 2 CA ASP A 12 26.823 -15.970 -5.303 1.00 30.78 C
ATOM 3 C ASP A 12 26.159 -15.774 -3.935 1.00 28.50 C
ATOM 4 O ASP A 12 26.821 -15.471 -2.933 1.00 31.55 O
ATOM 5 CB ASP A 12 27.422 -17.369 -5.430 1.00 35.38 C
ATOM 6 CG ASP A 12 28.541 -17.599 -4.503 1.00 47.47 C
ATOM 7 OD1 ASP A 12 29.611 -16.977 -4.725 1.00 51.81 O
ATOM 8 OD2 ASP A 12 28.370 -18.402 -3.546 1.00 52.89 O1-
ATOM 9 N GLU A 31 14.633 -20.649 -0.016 1.00 46.76 N1+
ATOM 10 CA GLU A 31 15.583 -21.719 -0.265 1.00 39.69 C
ATOM 11 C GLU A 31 16.629 -21.238 -1.256 1.00 38.63 C
ATOM 12 O GLU A 31 16.432 -20.258 -1.981 1.00 38.02 O
ATOM 13 CB GLU A 31 14.867 -22.968 -0.784 1.00 35.05 C
ATOM 14 CG GLU A 31 13.847 -23.490 0.235 1.00 45.90 C
ATOM 15 CD GLU A 31 12.965 -24.624 -0.268 1.00 47.81 C
ATOM 16 OE1 GLU A 31 12.742 -24.728 -1.490 1.00 50.00 O
ATOM 17 OE2 GLU A 31 12.473 -25.404 0.576 1.00 51.86 O1-
ATOM 18 N TYR A 32 17.752 -21.932 -1.276 1.00 33.96 N
ATOM 19 CA TYR A 32 18.862 -21.558 -2.158 1.00 32.43 C
ATOM 20 C TYR A 32 18.876 -22.470 -3.384 1.00 27.54 C
ATOM 21 O TYR A 32 19.167 -23.666 -3.268 1.00 31.25 O
ATOM 22 CB TYR A 32 20.185 -21.650 -1.394 1.00 34.50 C
ATOM 23 CG TYR A 32 21.353 -21.198 -2.256 1.00 38.29 C
ATOM 24 CD1 TYR A 32 21.172 -20.234 -3.219 1.00 38.20 C
ATOM 25 CD2 TYR A 32 22.618 -21.782 -2.121 1.00 45.63 C
ATOM 26 CE1 TYR A 32 22.208 -19.805 -4.021 1.00 38.48 C
ATOM 27 CE2 TYR A 32 23.673 -21.375 -2.932 1.00 47.40 C
ATOM 28 CZ TYR A 32 23.460 -20.367 -3.871 1.00 39.91 C
ATOM 29 OH TYR A 32 24.495 -19.934 -4.700 1.00 44.55 O
ATOM 30 N ASP A 33 18.547 -21.906 -4.547 1.00 27.99 N
ATOM 31 CA ASP A 33 18.501 -22.600 -5.828 1.00 33.59 C
ATOM 32 C ASP A 33 19.536 -21.881 -6.689 1.00 27.38 C
ATOM 33 O ASP A 33 19.201 -20.901 -7.366 1.00 25.61 O
ATOM 34 CB ASP A 33 17.096 -22.528 -6.441 1.00 32.37 C
ATOM 35 CG ASP A 33 16.956 -23.367 -7.719 1.00 41.66 C
ATOM 36 OD1 ASP A 33 17.891 -24.145 -8.010 1.00 38.49 O
ATOM 37 OD2 ASP A 33 15.908 -23.259 -8.411 1.00 38.24 O1-
ATOM 38 N PRO A 34 20.818 -22.268 -6.611 1.00 26.61 N
ATOM 39 CA PRO A 34 21.860 -21.508 -7.337 1.00 27.86 C
ATOM 40 C PRO A 34 21.549 -21.461 -8.821 1.00 27.40 C
ATOM 41 O PRO A 34 21.136 -22.456 -9.412 1.00 30.87 O
ATOM 42 CB PRO A 34 23.161 -22.283 -7.051 1.00 30.32 C
ATOM 43 CG PRO A 34 22.765 -23.592 -6.447 1.00 33.72 C
ATOM 44 CD PRO A 34 21.372 -23.389 -5.836 1.00 33.65 C
ATOM 45 N THR A 35 21.748 -20.287 -9.415 1.00 23.44 N1+
ATOM 46 CA THR A 35 21.320 -20.036 -10.805 1.00 21.25 C
ATOM 47 C THR A 35 22.354 -20.532 -11.808 1.00 28.44 C
ATOM 48 O THR A 35 23.569 -20.441 -11.570 1.00 23.25 O
ATOM 49 CB THR A 35 21.105 -18.547 -10.982 1.00 19.86 C
ATOM 50 OG1 THR A 35 20.095 -18.150 -10.040 1.00 21.78 O
ATOM 51 CG2 THR A 35 20.632 -18.159 -12.432 1.00 22.55 C
ATOM 52 N ILE A 36 21.863 -21.093 -12.914 1.00 22.23 N
ATOM 53 CA ILE A 36 22.705 -21.315 -14.093 1.00 22.54 C
ATOM 54 C ILE A 36 22.540 -20.053 -14.929 1.00 25.61 C
ATOM 55 O ILE A 36 23.463 -19.234 -15.071 1.00 24.52 O
ATOM 56 CB ILE A 36 22.292 -22.564 -14.899 1.00 23.20 C
ATOM 57 CG1 ILE A 36 22.232 -23.825 -14.043 1.00 25.92 C
ATOM 58 CG2 ILE A 36 23.233 -22.814 -16.130 1.00 23.76 C
ATOM 59 CD1 ILE A 36 23.534 -24.332 -13.457 1.00 26.39 C
ATOM 60 N GLU A 37 21.356 -19.889 -15.502 1.00 20.32 N
ATOM 61 CA GLU A 37 21.029 -18.619 -16.141 1.00 22.54 C
ATOM 62 C GLU A 37 19.517 -18.613 -16.356 1.00 23.03 C
ATOM 63 O GLU A 37 18.975 -19.629 -16.788 1.00 28.28 O
ATOM 64 CB GLU A 37 21.755 -18.491 -17.482 1.00 25.18 C
ATOM 65 CG GLU A 37 21.662 -17.099 -18.060 1.00 25.47 C
ATOM 66 CD GLU A 37 22.290 -17.057 -19.419 1.00 42.62 C
ATOM 67 OE1 GLU A 37 21.584 -17.434 -20.361 1.00 41.81 O
ATOM 68 OE2 GLU A 37 23.483 -16.719 -19.515 1.00 51.13 O1-
ATOM 69 N ASP A 38 18.837 -17.533 -16.003 1.00 21.12 N1+
ATOM 70 CA ASP A 38 17.398 -17.474 -16.266 1.00 22.38 C
ATOM 71 C ASP A 38 17.025 -16.114 -16.825 1.00 27.35 C
ATOM 72 O ASP A 38 17.645 -15.093 -16.517 1.00 24.16 O
ATOM 73 CB ASP A 38 16.556 -17.740 -14.990 1.00 20.76 C
ATOM 74 CG ASP A 38 16.698 -19.154 -14.487 1.00 26.21 C
ATOM 75 OD1 ASP A 38 16.106 -20.100 -15.068 1.00 29.61 O
ATOM 76 OD2 ASP A 38 17.435 -19.306 -13.486 1.00 30.66 O1-
ATOM 77 N THR A 58 21.792 -13.839 -13.533 1.00 17.83 N
ATOM 78 CA THR A 58 23.037 -14.527 -13.904 1.00 21.76 C
ATOM 79 C THR A 58 23.588 -15.330 -12.733 1.00 23.14 C
ATOM 80 O THR A 58 23.161 -15.150 -11.594 1.00 24.73 O
ATOM 81 CB THR A 58 24.137 -13.558 -14.354 1.00 24.53 C
ATOM 82 OG1 THR A 58 24.476 -12.697 -13.236 1.00 23.78 O
ATOM 83 CG2 THR A 58 23.593 -12.701 -15.486 1.00 22.87 C
ATOM 84 N ALA A 59 24.500 -16.244 -13.048 1.00 19.79 N
ATOM 85 CA ALA A 59 25.171 -17.044 -12.015 1.00 23.52 C
ATOM 86 C ALA A 59 26.187 -16.236 -11.225 1.00 26.34 C
ATOM 87 O ALA A 59 26.979 -15.484 -11.783 1.00 26.94 O
ATOM 88 CB ALA A 59 25.878 -18.252 -12.616 1.00 25.79 C
ATOM 89 N GLY A 60 26.204 -16.451 -9.911 1.00 24.98 N
ATOM 90 CA GLY A 60 27.263 -15.901 -9.095 1.00 31.77 C
ATOM 91 C GLY A 60 28.515 -16.748 -8.967 1.00 31.44 C
ATOM 92 O GLY A 60 29.408 -16.368 -8.205 1.00 35.85 O
ATOM 93 N GLN A 61 28.646 -17.872 -9.681 1.00 31.26 N
ATOM 94 CA GLN A 61 29.825 -18.727 -9.501 1.00 35.34 C
ATOM 95 C GLN A 61 31.101 -18.038 -10.010 1.00 43.54 C
ATOM 96 O GLN A 61 31.219 -17.746 -11.200 1.00 38.79 O
ATOM 97 CB GLN A 61 29.632 -20.067 -10.222 1.00 39.41 C
ATOM 98 CG GLN A 61 28.350 -20.846 -9.864 1.00 33.81 C
ATOM 99 CD GLN A 61 28.267 -21.182 -8.359 1.00 42.17 C
ATOM 100 OE1 GLN A 61 29.199 -21.739 -7.778 1.00 41.31 O
ATOM 101 NE2 GLN A 61 27.151 -20.815 -7.734 1.00 41.87 N
ATOM 102 N GLU A 63 33.824 -19.436 -10.551 1.00 38.24 N
ATOM 103 CA GLU A 63 34.484 -20.376 -11.449 1.00 41.01 C
ATOM 104 C GLU A 63 34.282 -20.022 -12.915 1.00 35.20 C
ATOM 105 O GLU A 63 35.174 -20.282 -13.740 1.00 38.39 O
ATOM 106 CB GLU A 63 33.969 -21.797 -11.196 1.00 41.39 C
ATOM 107 CG GLU A 63 34.403 -22.816 -12.271 1.00 41.18 C
ATOM 108 CD GLU A 63 35.855 -23.261 -12.137 1.00 48.98 C
ATOM 109 OE1 GLU A 63 36.353 -23.271 -10.994 1.00 56.88 O
ATOM 110 OE2 GLU A 63 36.494 -23.632 -13.159 1.00 52.15 O1-
ATOM 111 N TYR A 64 33.138 -19.429 -13.254 1.00 34.95 N
ATOM 112 CA TYR A 64 32.773 -19.136 -14.645 1.00 36.20 C
ATOM 113 C TYR A 64 32.846 -17.640 -14.943 1.00 38.83 C
ATOM 114 O TYR A 64 32.139 -17.138 -15.823 1.00 31.64 O
ATOM 115 CB TYR A 64 31.380 -19.710 -14.959 1.00 34.18 C
ATOM 116 CG TYR A 64 31.283 -21.165 -14.568 1.00 36.59 C
ATOM 117 CD1 TYR A 64 32.012 -22.101 -15.250 1.00 31.38 C
ATOM 118 CD2 TYR A 64 30.469 -21.596 -13.501 1.00 32.30 C
ATOM 119 CE1 TYR A 64 32.016 -23.444 -14.899 1.00 33.38 C
ATOM 120 CE2 TYR A 64 30.457 -22.946 -13.133 1.00 29.09 C
ATOM 121 CZ TYR A 64 31.181 -23.861 -13.843 1.00 31.16 C
ATOM 122 OH TYR A 64 31.250 -25.212 -13.560 1.00 33.42 O
ATOM 123 N SER A 65 33.718 -16.913 -14.215 1.00 38.52 N
ATOM 124 CA SER A 65 33.872 -15.470 -14.423 1.00 41.07 C
ATOM 125 C SER A 65 34.241 -15.140 -15.864 1.00 33.77 C
ATOM 126 O SER A 65 33.871 -14.078 -16.375 1.00 40.36 O
ATOM 127 CB SER A 65 34.953 -14.919 -13.480 1.00 38.73 C
ATOM 128 OG SER A 65 36.116 -15.706 -13.535 1.00 41.95 O
ATOM 129 N ALA A 66 34.915 -16.059 -16.533 1.00 36.04 N
ATOM 130 CA ALA A 66 35.378 -15.832 -17.900 1.00 41.85 C
ATOM 131 C ALA A 66 34.241 -15.844 -18.913 1.00 45.62 C
ATOM 132 O ALA A 66 34.433 -15.430 -20.059 1.00 52.92 O
ATOM 133 CB ALA A 66 36.418 -16.885 -18.281 1.00 47.40 C
ATOM 134 N MET A 67 33.082 -16.353 -18.548 1.00 36.94 N
ATOM 135 CA MET A 67 31.977 -16.505 -19.477 1.00 40.67 C
ATOM 136 C MET A 67 30.942 -15.401 -19.351 1.00 40.98 C
ATOM 137 O MET A 67 29.859 -15.505 -19.942 1.00 38.32 O
ATOM 138 CB MET A 67 31.328 -17.866 -19.265 1.00 41.03 C
ATOM 139 CG MET A 67 32.345 -18.965 -19.510 1.00 47.85 C
ATOM 140 SD MET A 67 31.614 -20.553 -19.176 1.00 46.91 S
ATOM 141 CE MET A 67 30.200 -20.437 -20.310 1.00 38.99 C
ATOM 142 N ARG A 68 31.235 -14.357 -18.573 1.00 36.22 N
ATOM 143 CA ARG A 68 30.286 -13.264 -18.427 1.00 38.65 C
ATOM 144 C ARG A 68 30.310 -12.311 -19.609 1.00 34.36 C
ATOM 145 O ARG A 68 29.347 -11.542 -19.797 1.00 31.79 O
ATOM 146 CB ARG A 68 30.572 -12.482 -17.151 1.00 31.59 C
ATOM 147 CG ARG A 68 30.490 -13.344 -15.922 1.00 36.28 C
ATOM 148 CD ARG A 68 30.760 -12.550 -14.677 1.00 34.04 C
ATOM 149 NE ARG A 68 31.091 -13.423 -13.555 1.00 40.26 N
ATOM 150 CZ ARG A 68 31.986 -13.155 -12.612 1.00 40.60 C
ATOM 151 NH1 ARG A 68 32.825 -12.121 -12.703 1.00 40.26 N
ATOM 152 NH2 ARG A 68 32.045 -13.943 -11.536 1.00 44.34 N1+
ATOM 153 N TYR A 71 27.105 -12.011 -22.092 1.00 34.41 N
ATOM 154 CA TYR A 71 25.879 -11.348 -21.653 1.00 28.61 C
ATOM 155 C TYR A 71 26.137 -9.895 -21.256 1.00 25.39 C
ATOM 156 O TYR A 71 25.222 -9.069 -21.332 1.00 27.97 O
ATOM 157 CB TYR A 71 25.180 -12.150 -20.542 1.00 26.00 C
ATOM 158 CG TYR A 71 25.826 -12.192 -19.171 1.00 28.41 C
ATOM 159 CD1 TYR A 71 25.999 -11.029 -18.422 1.00 26.71 C
ATOM 160 CD2 TYR A 71 26.218 -13.411 -18.609 1.00 32.32 C
ATOM 161 CE1 TYR A 71 26.556 -11.066 -17.118 1.00 30.15 C
ATOM 162 CE2 TYR A 71 26.761 -13.463 -17.305 1.00 31.65 C
ATOM 163 CZ TYR A 71 26.925 -12.286 -16.575 1.00 29.74 C
ATOM 164 OH TYR A 71 27.451 -12.325 -15.281 1.00 32.36 O
TER 165 TYR A 71
ATOM 166 N HIS D 54 16.401 -33.143 -12.082 1.00 20.78 N
ATOM 167 CA HIS D 54 15.907 -31.778 -12.191 1.00 23.62 C
ATOM 168 C HIS D 54 16.717 -30.899 -13.123 1.00 23.48 C
ATOM 169 O HIS D 54 16.202 -29.858 -13.584 1.00 25.51 O
ATOM 170 CB HIS D 54 15.823 -31.104 -10.832 1.00 25.81 C
ATOM 171 CG HIS D 54 17.139 -30.924 -10.141 1.00 25.88 C
ATOM 172 ND1 HIS D 54 17.784 -31.944 -9.470 1.00 22.99 N
ATOM 173 CD2 HIS D 54 17.923 -29.830 -10.012 1.00 20.88 C
ATOM 174 CE1 HIS D 54 18.920 -31.486 -8.963 1.00 22.72 C
ATOM 175 NE2 HIS D 54 18.994 -30.195 -9.224 1.00 23.14 N
ATOM 176 N ARG D 55 17.953 -31.279 -13.416 1.00 22.87 N1+
ATOM 177 CA ARG D 55 18.809 -30.443 -14.236 1.00 23.55 C
ATOM 178 C ARG D 55 19.506 -31.310 -15.268 1.00 22.07 C
ATOM 179 O ARG D 55 20.330 -32.160 -14.921 1.00 23.40 O
ATOM 180 CB ARG D 55 19.820 -29.717 -13.357 1.00 23.73 C
ATOM 181 CG ARG D 55 20.783 -28.870 -14.127 1.00 22.69 C
ATOM 182 CD ARG D 55 21.753 -28.166 -13.175 1.00 20.89 C
ATOM 183 NE ARG D 55 21.022 -27.113 -12.482 1.00 25.58 N
ATOM 184 CZ ARG D 55 21.399 -26.668 -11.302 1.00 27.51 C
ATOM 185 NH1 ARG D 55 22.470 -27.170 -10.697 1.00 26.07 N
ATOM 186 NH2 ARG D 55 20.708 -25.689 -10.726 1.00 27.90 N1+
ATOM 187 N ILE D 56 19.217 -31.088 -16.556 1.00 19.09 N
ATOM 188 CA ILE D 56 19.877 -31.846 -17.603 1.00 18.60 C
ATOM 189 C ILE D 56 20.417 -30.868 -18.622 1.00 21.97 C
ATOM 190 O ILE D 56 19.648 -30.117 -19.215 1.00 21.24 O
ATOM 191 CB ILE D 56 18.907 -32.820 -18.297 1.00 22.61 C
ATOM 192 CG1 ILE D 56 18.390 -33.820 -17.283 1.00 24.58 C
ATOM 193 CG2 ILE D 56 19.594 -33.516 -19.519 1.00 22.13 C
ATOM 194 CD1 ILE D 56 17.412 -34.791 -17.813 1.00 26.63 C
ATOM 195 N ILE D 57 21.732 -30.860 -18.810 1.00 21.57 N
ATOM 196 CA ILE D 57 22.325 -29.908 -19.742 1.00 21.77 C
ATOM 197 C ILE D 57 23.080 -30.713 -20.780 1.00 21.35 C
ATOM 198 O ILE D 57 24.142 -31.294 -20.492 1.00 21.46 O
ATOM 199 CB ILE D 57 23.240 -28.903 -19.042 1.00 22.99 C
ATOM 200 CG1 ILE D 57 22.409 -28.172 -17.982 1.00 23.13 C
ATOM 201 CG2 ILE D 57 23.777 -27.913 -20.104 1.00 24.35 C
ATOM 202 CD1 ILE D 57 23.236 -27.149 -17.147 1.00 22.29 C
ATOM 203 N PRO D 58 22.571 -30.743 -21.999 1.00 22.36 N
ATOM 204 CA PRO D 58 23.220 -31.540 -23.041 1.00 23.84 C
ATOM 205 C PRO D 58 24.647 -31.087 -23.276 1.00 22.58 C
ATOM 206 O PRO D 58 24.947 -29.892 -23.363 1.00 23.59 O
ATOM 207 CB PRO D 58 22.338 -31.284 -24.281 1.00 22.47 C
ATOM 208 CG PRO D 58 20.954 -30.922 -23.694 1.00 25.39 C
ATOM 209 CD PRO D 58 21.312 -30.109 -22.463 1.00 25.16 C
ATOM 210 N GLY D 59 25.540 -32.070 -23.420 1.00 24.08 N
ATOM 211 CA GLY D 59 26.926 -31.786 -23.578 1.00 23.91 C
ATOM 212 C GLY D 59 27.715 -31.787 -22.273 1.00 24.90 C
ATOM 213 O GLY D 59 28.945 -31.709 -22.319 1.00 23.48 O
ATOM 214 N PHE D 60 27.026 -31.883 -21.123 1.00 21.44 N
ATOM 215 CA PHE D 60 27.677 -31.727 -19.834 1.00 21.18 C
ATOM 216 C PHE D 60 27.261 -32.802 -18.855 1.00 21.73 C
ATOM 217 O PHE D 60 28.100 -33.652 -18.507 1.00 21.00 O
ATOM 218 CB PHE D 60 27.453 -30.325 -19.225 1.00 20.58 C
ATOM 219 CG PHE D 60 28.152 -29.232 -20.036 1.00 22.70 C
ATOM 220 CD1 PHE D 60 29.480 -28.955 -19.864 1.00 20.95 C
ATOM 221 CD2 PHE D 60 27.449 -28.562 -21.010 1.00 22.46 C
ATOM 222 CE1 PHE D 60 30.153 -27.967 -20.665 1.00 25.83 C
ATOM 223 CE2 PHE D 60 28.099 -27.597 -21.834 1.00 27.37 C
ATOM 224 CZ PHE D 60 29.426 -27.288 -21.606 1.00 26.34 C
ATOM 225 N MET D 61 26.010 -32.765 -18.352 1.00 21.50 N
ATOM 226 CA MET D 61 25.700 -33.681 -17.266 1.00 19.84 C
ATOM 227 C MET D 61 24.195 -33.744 -17.048 1.00 20.80 C
ATOM 228 O MET D 61 23.434 -32.882 -17.528 1.00 21.41 O
ATOM 229 CB MET D 61 26.414 -33.265 -15.949 1.00 17.82 C
ATOM 230 CG MET D 61 25.505 -32.382 -15.112 1.00 29.58 C
ATOM 231 SD MET D 61 25.488 -30.688 -15.789 1.00 24.21 S
ATOM 232 CE MET D 61 24.043 -30.056 -14.898 1.00 27.84 C
ATOM 233 N CYS D 62 23.785 -34.804 -16.338 1.00 17.99 N
ATOM 234 CA CYS D 62 22.440 -35.024 -15.819 1.00 20.05 C
ATOM 235 C CYS D 62 22.530 -35.003 -14.300 1.00 20.25 C
ATOM 236 O CYS D 62 23.298 -35.795 -13.740 1.00 21.34 O
ATOM 237 CB CYS D 62 21.924 -36.413 -16.225 1.00 19.04 C
ATOM 238 SG CYS D 62 21.675 -36.424 -18.052 1.00 25.85 S
ATOM 239 N GLN D 63 21.726 -34.161 -13.641 1.00 18.60 N
ATOM 240 CA GLN D 63 21.807 -34.002 -12.186 1.00 17.93 C
ATOM 241 C GLN D 63 20.457 -34.311 -11.534 1.00 20.03 C
ATOM 242 O GLN D 63 19.392 -33.935 -12.068 1.00 20.98 O
ATOM 243 CB GLN D 63 22.240 -32.581 -11.860 1.00 18.96 C
ATOM 244 CG GLN D 63 22.447 -32.308 -10.308 1.00 20.73 C
ATOM 245 CD GLN D 63 22.931 -30.865 -10.043 1.00 22.29 C
ATOM 246 OE1 GLN D 63 23.512 -30.223 -10.927 1.00 22.06 O
ATOM 247 NE2 GLN D 63 22.675 -30.333 -8.827 1.00 20.51 N
ATOM 248 N GLY D 64 20.508 -34.957 -10.369 1.00 19.64 N
ATOM 249 CA GLY D 64 19.282 -35.376 -9.697 1.00 21.02 C
ATOM 250 C GLY D 64 19.564 -35.566 -8.220 1.00 22.67 C
ATOM 251 O GLY D 64 20.571 -35.092 -7.701 1.00 22.57 O
ATOM 252 N GLY D 65 18.618 -36.201 -7.529 1.00 19.88 N1+
ATOM 253 CA GLY D 65 18.872 -36.629 -6.156 1.00 21.62 C
ATOM 254 C GLY D 65 18.201 -35.820 -5.068 1.00 24.29 C
ATOM 255 O GLY D 65 18.471 -36.090 -3.888 1.00 24.91 O
ATOM 256 N ASP D 66 17.379 -34.828 -5.402 1.00 24.00 N
ATOM 257 CA ASP D 66 16.669 -34.057 -4.373 1.00 23.51 C
ATOM 258 C ASP D 66 15.390 -34.804 -4.046 1.00 27.62 C
ATOM 259 O ASP D 66 14.353 -34.591 -4.664 1.00 26.08 O
ATOM 260 CB ASP D 66 16.351 -32.641 -4.835 1.00 26.78 C
ATOM 261 CG ASP D 66 15.597 -31.836 -3.769 1.00 28.52 C
ATOM 262 OD1 ASP D 66 15.334 -32.384 -2.667 1.00 28.76 O
ATOM 263 OD2 ASP D 66 15.276 -30.661 -4.007 1.00 28.30 O1-
ATOM 264 N ARG D 69 12.961 -32.063 -1.105 1.00 33.64 N
ATOM 265 CA ARG D 69 12.144 -30.958 -1.566 1.00 33.43 C
ATOM 266 C ARG D 69 11.671 -31.088 -2.998 1.00 32.79 C
ATOM 267 O ARG D 69 10.730 -30.380 -3.365 1.00 36.85 O
ATOM 268 CB ARG D 69 12.923 -29.644 -1.437 1.00 33.57 C
ATOM 269 CG ARG D 69 13.298 -29.356 -0.002 1.00 37.02 C
ATOM 270 CD ARG D 69 14.207 -28.186 0.020 1.00 40.51 C
ATOM 271 NE ARG D 69 14.679 -27.854 1.353 1.00 44.85 N
ATOM 272 CZ ARG D 69 15.668 -27.000 1.565 1.00 49.91 C
ATOM 273 NH1 ARG D 69 16.283 -26.389 0.549 1.00 36.00 N
ATOM 274 NH2 ARG D 69 16.035 -26.734 2.818 1.00 56.07 N1+
ATOM 275 N ASN D 71 12.791 -29.991 -5.726 1.00 29.43 N
ATOM 276 CA ASN D 71 12.912 -28.790 -6.533 1.00 32.25 C
ATOM 277 C ASN D 71 14.357 -28.446 -6.855 1.00 31.58 C
ATOM 278 O ASN D 71 14.619 -27.322 -7.306 1.00 36.05 O
ATOM 279 CB ASN D 71 12.212 -27.604 -5.860 1.00 31.90 C
ATOM 280 CG ASN D 71 12.816 -27.224 -4.532 1.00 32.06 C
ATOM 281 OD1 ASN D 71 13.927 -27.643 -4.181 1.00 31.42 O
ATOM 282 ND2 ASN D 71 12.086 -26.388 -3.783 1.00 36.48 N
ATOM 283 N GLY D 72 15.310 -29.360 -6.609 1.00 27.84 N
ATOM 284 CA GLY D 72 16.688 -29.094 -6.903 1.00 26.38 C
ATOM 285 C GLY D 72 17.442 -28.419 -5.800 1.00 30.81 C
ATOM 286 O GLY D 72 18.645 -28.232 -5.938 1.00 26.20 O
ATOM 287 N THR D 73 16.785 -28.046 -4.700 1.00 30.07 N
ATOM 288 CA THR D 73 17.517 -27.368 -3.648 1.00 28.72 C
ATOM 289 C THR D 73 17.820 -28.269 -2.450 1.00 31.17 C
ATOM 290 O THR D 73 18.575 -27.843 -1.565 1.00 30.81 O
ATOM 291 CB THR D 73 16.759 -26.131 -3.130 1.00 33.27 C
ATOM 292 OG1 THR D 73 15.623 -26.537 -2.353 1.00 30.42 O
ATOM 293 CG2 THR D 73 16.300 -25.186 -4.269 1.00 29.76 C
ATOM 294 N GLY D 74 17.261 -29.489 -2.404 1.00 29.27 N
ATOM 295 CA GLY D 74 17.298 -30.267 -1.160 1.00 30.09 C
ATOM 296 C GLY D 74 18.124 -31.537 -1.283 1.00 33.43 C
ATOM 297 O GLY D 74 19.122 -31.598 -2.034 1.00 26.58 O
ATOM 298 N GLY D 75 17.685 -32.571 -0.568 1.00 26.08 N
ATOM 299 CA GLY D 75 18.452 -33.783 -0.400 1.00 28.87 C
ATOM 300 C GLY D 75 19.145 -33.816 0.962 1.00 28.60 C
ATOM 301 O GLY D 75 19.374 -32.797 1.601 1.00 29.65 O
ATOM 302 N GLU D 81 22.436 -33.849 7.392 1.00 30.90 N
ATOM 303 CA GLU D 81 22.029 -33.123 6.186 1.00 32.66 C
ATOM 304 C GLU D 81 23.173 -33.075 5.168 1.00 34.24 C
ATOM 305 O GLU D 81 22.985 -33.331 3.959 1.00 32.89 O
ATOM 306 CB GLU D 81 21.571 -31.717 6.529 1.00 33.52 C
ATOM 307 CG GLU D 81 21.130 -30.999 5.238 1.00 39.45 C
ATOM 308 CD GLU D 81 20.710 -29.540 5.429 1.00 51.28 C
ATOM 309 OE1 GLU D 81 21.412 -28.780 6.153 1.00 51.36 O
ATOM 310 OE2 GLU D 81 19.662 -29.172 4.848 1.00 48.33 O1-
ATOM 311 N LYS D 82 24.371 -32.772 5.627 1.00 25.94 N
ATOM 312 CA LYS D 82 25.532 -32.787 4.741 1.00 28.76 C
ATOM 313 C LYS D 82 26.716 -33.539 5.354 1.00 29.52 C
ATOM 314 O LYS D 82 26.760 -33.814 6.564 1.00 29.92 O
ATOM 315 CB LYS D 82 25.945 -31.355 4.388 1.00 37.08 C
ATOM 316 CG LYS D 82 24.858 -30.581 3.590 1.00 37.89 C
ATOM 317 CD LYS D 82 25.135 -29.069 3.508 1.00 42.19 C
ATOM 318 CE LYS D 82 25.048 -28.425 4.921 1.00 52.21 C
ATOM 319 NZ LYS D 82 25.044 -26.907 4.968 1.00 56.05 N1+
ATOM 320 N HIS D 92 36.989 -36.239 -13.129 1.00 26.62 N
ATOM 321 CA HIS D 92 35.857 -36.235 -14.068 1.00 22.77 C
ATOM 322 C HIS D 92 36.417 -36.438 -15.470 1.00 27.22 C
ATOM 323 O HIS D 92 36.614 -35.482 -16.225 1.00 29.08 O
ATOM 324 CB HIS D 92 35.101 -34.920 -13.983 1.00 23.40 C
ATOM 325 CG HIS D 92 34.564 -34.640 -12.607 1.00 21.55 C
ATOM 326 ND1 HIS D 92 35.326 -34.035 -11.634 1.00 21.35 N
ATOM 327 CD2 HIS D 92 33.344 -34.876 -12.053 1.00 23.79 C
ATOM 328 CE1 HIS D 92 34.614 -33.949 -10.511 1.00 23.26 C
ATOM 329 NE2 HIS D 92 33.397 -34.422 -10.748 1.00 21.15 N
ATOM 330 N SER D 99 29.276 -38.356 -9.713 1.00 20.88 N
ATOM 331 CA SER D 99 30.030 -37.445 -8.840 1.00 21.60 C
ATOM 332 C SER D 99 29.021 -36.677 -7.990 1.00 20.60 C
ATOM 333 O SER D 99 27.864 -36.594 -8.349 1.00 19.14 O
ATOM 334 CB SER D 99 30.924 -36.519 -9.697 1.00 21.31 C
ATOM 335 OG SER D 99 31.593 -35.566 -8.892 1.00 20.77 O
ATOM 336 N MET D 100 29.440 -36.195 -6.809 1.00 17.71 N
ATOM 337 CA MET D 100 28.504 -35.517 -5.907 1.00 18.19 C
ATOM 338 C MET D 100 28.296 -34.053 -6.275 1.00 20.00 C
ATOM 339 O MET D 100 29.270 -33.304 -6.494 1.00 22.17 O
ATOM 340 CB MET D 100 29.017 -35.576 -4.445 1.00 19.98 C
ATOM 341 CG MET D 100 29.015 -36.966 -3.899 1.00 23.96 C
ATOM 342 SD MET D 100 27.364 -37.692 -3.824 1.00 22.94 S
ATOM 343 CE MET D 100 26.484 -36.739 -2.556 1.00 23.05 C
ATOM 344 N ALA D 101 27.025 -33.635 -6.301 1.00 18.14 N
ATOM 345 CA ALA D 101 26.744 -32.205 -6.341 1.00 19.63 C
ATOM 346 C ALA D 101 27.036 -31.558 -4.983 1.00 20.05 C
ATOM 347 O ALA D 101 27.057 -32.229 -3.944 1.00 24.94 O
ATOM 348 CB ALA D 101 25.257 -31.980 -6.717 1.00 21.25 C
ATOM 349 N ASN D 102 27.264 -30.245 -4.979 1.00 21.75 N
ATOM 350 CA ASN D 102 27.441 -29.569 -3.690 1.00 23.55 C
ATOM 351 C ASN D 102 27.217 -28.064 -3.834 1.00 24.76 C
ATOM 352 O ASN D 102 27.029 -27.533 -4.929 1.00 24.81 O
ATOM 353 CB ASN D 102 28.830 -29.875 -3.085 1.00 23.88 C
ATOM 354 CG ASN D 102 29.972 -29.349 -3.929 1.00 24.18 C
ATOM 355 OD1 ASN D 102 30.028 -28.157 -4.227 1.00 25.31 O
ATOM 356 ND2 ASN D 102 30.924 -30.219 -4.264 1.00 22.33 N
ATOM 357 N ALA D 103 27.257 -27.375 -2.697 1.00 23.99 N
ATOM 358 CA ALA D 103 27.165 -25.923 -2.634 1.00 27.45 C
ATOM 359 C ALA D 103 28.421 -25.332 -2.012 1.00 25.64 C
ATOM 360 O ALA D 103 28.358 -24.367 -1.244 1.00 31.22 O
ATOM 361 CB ALA D 103 25.948 -25.469 -1.823 1.00 34.59 C
ATOM 362 N GLY D 104 29.577 -25.876 -2.376 1.00 26.84 N
ATOM 363 CA GLY D 104 30.833 -25.464 -1.802 1.00 31.98 C
ATOM 364 C GLY D 104 31.489 -26.591 -1.024 1.00 27.39 C
ATOM 365 O GLY D 104 30.990 -27.710 -0.954 1.00 25.82 O
ATOM 366 N THR D 107 29.847 -29.946 1.598 1.00 26.85 N
ATOM 367 CA THR D 107 28.391 -29.790 1.633 1.00 29.63 C
ATOM 368 C THR D 107 27.709 -30.902 0.835 1.00 28.19 C
ATOM 369 O THR D 107 26.687 -30.672 0.196 1.00 31.67 O
ATOM 370 CB THR D 107 27.944 -28.413 1.109 1.00 24.62 C
ATOM 371 OG1 THR D 107 28.418 -28.233 -0.227 1.00 26.44 O
ATOM 372 CG2 THR D 107 28.369 -27.201 2.040 1.00 25.59 C
ATOM 373 N ASN D 108 28.248 -32.110 0.861 1.00 24.99 N
ATOM 374 CA ASN D 108 27.638 -33.216 0.137 1.00 21.99 C
ATOM 375 C ASN D 108 26.365 -33.666 0.830 1.00 29.16 C
ATOM 376 O ASN D 108 26.360 -33.818 2.048 1.00 28.31 O
ATOM 377 CB ASN D 108 28.594 -34.382 0.085 1.00 25.24 C
ATOM 378 CG ASN D 108 29.888 -34.050 -0.618 1.00 28.30 C
ATOM 379 OD1 ASN D 108 29.897 -33.939 -1.826 1.00 25.25 O
ATOM 380 ND2 ASN D 108 30.991 -33.874 0.141 1.00 22.10 N
ATOM 381 N GLY D 109 25.292 -33.898 0.038 1.00 24.69 N
ATOM 382 CA GLY D 109 24.039 -34.378 0.596 1.00 23.53 C
ATOM 383 C GLY D 109 23.600 -35.653 -0.070 1.00 26.41 C
ATOM 384 O GLY D 109 24.172 -36.727 0.163 1.00 22.67 O
ATOM 385 N SER D 110 22.596 -35.539 -0.956 1.00 22.43 N
ATOM 386 CA SER D 110 22.216 -36.654 -1.799 1.00 23.10 C
ATOM 387 C SER D 110 22.275 -36.313 -3.277 1.00 21.88 C
ATOM 388 O SER D 110 22.334 -37.235 -4.097 1.00 20.69 O
ATOM 389 CB SER D 110 20.797 -37.139 -1.464 1.00 23.01 C
ATOM 390 OG SER D 110 19.903 -36.072 -1.574 1.00 23.84 O
ATOM 391 N GLN D 111 22.313 -35.038 -3.631 1.00 21.79 N
ATOM 392 CA GLN D 111 22.335 -34.701 -5.064 1.00 19.24 C
ATOM 393 C GLN D 111 23.649 -35.144 -5.703 1.00 20.37 C
ATOM 394 O GLN D 111 24.733 -35.070 -5.102 1.00 20.95 O
ATOM 395 CB GLN D 111 22.121 -33.215 -5.278 1.00 18.36 C
ATOM 396 CG GLN D 111 20.638 -32.716 -4.983 1.00 20.29 C
ATOM 397 CD GLN D 111 20.436 -31.287 -5.499 1.00 23.24 C
ATOM 398 OE1 GLN D 111 20.843 -30.966 -6.646 1.00 23.26 O
ATOM 399 NE2 GLN D 111 19.805 -30.429 -4.692 1.00 23.03 N
ATOM 400 N PHE D 112 23.536 -35.564 -6.956 1.00 20.61 N
ATOM 401 CA PHE D 112 24.637 -36.168 -7.677 1.00 20.44 C
ATOM 402 C PHE D 112 24.488 -35.776 -9.138 1.00 19.27 C
ATOM 403 O PHE D 112 23.439 -35.298 -9.569 1.00 18.78 O
ATOM 404 CB PHE D 112 24.608 -37.689 -7.574 1.00 19.84 C
ATOM 405 CG PHE D 112 23.344 -38.290 -8.190 1.00 20.55 C
ATOM 406 CD1 PHE D 112 22.185 -38.373 -7.428 1.00 20.73 C
ATOM 407 CD2 PHE D 112 23.292 -38.645 -9.539 1.00 25.38 C
ATOM 408 CE1 PHE D 112 20.972 -38.882 -7.956 1.00 21.59 C
ATOM 409 CE2 PHE D 112 22.100 -39.118 -10.085 1.00 25.71 C
ATOM 410 CZ PHE D 112 20.941 -39.213 -9.329 1.00 22.34 C
ATOM 411 N PHE D 113 25.531 -36.050 -9.896 1.00 18.26 N
ATOM 412 CA PHE D 113 25.406 -35.834 -11.331 1.00 18.52 C
ATOM 413 C PHE D 113 26.170 -36.915 -12.062 1.00 20.84 C
ATOM 414 O PHE D 113 27.106 -37.506 -11.532 1.00 21.21 O
ATOM 415 CB PHE D 113 25.848 -34.421 -11.719 1.00 17.84 C
ATOM 416 CG PHE D 113 27.301 -34.112 -11.467 1.00 18.31 C
ATOM 417 CD1 PHE D 113 27.766 -33.839 -10.189 1.00 21.81 C
ATOM 418 CD2 PHE D 113 28.204 -34.065 -12.503 1.00 19.25 C
ATOM 419 CE1 PHE D 113 29.118 -33.494 -9.967 1.00 25.02 C
ATOM 420 CE2 PHE D 113 29.548 -33.722 -12.332 1.00 19.48 C
ATOM 421 CZ PHE D 113 30.024 -33.458 -11.054 1.00 19.26 C
ATOM 422 N ILE D 114 25.708 -37.194 -13.280 1.00 18.17 N
ATOM 423 CA ILE D 114 26.327 -38.142 -14.202 1.00 19.41 C
ATOM 424 C ILE D 114 26.866 -37.322 -15.358 1.00 22.46 C
ATOM 425 O ILE D 114 26.081 -36.657 -16.049 1.00 19.83 O
ATOM 426 CB ILE D 114 25.300 -39.177 -14.696 1.00 19.64 C
ATOM 427 CG1 ILE D 114 24.540 -39.830 -13.521 1.00 24.97 C
ATOM 428 CG2 ILE D 114 25.974 -40.263 -15.560 1.00 21.23 C
ATOM 429 CD1 ILE D 114 23.225 -40.554 -13.941 1.00 20.75 C
ATOM 430 N CYS D 115 28.184 -37.376 -15.583 1.00 21.48 N
ATOM 431 CA CYS D 115 28.780 -36.653 -16.700 1.00 21.56 C
ATOM 432 C CYS D 115 28.354 -37.275 -18.029 1.00 22.78 C
ATOM 433 O CYS D 115 28.264 -38.499 -18.167 1.00 22.03 O
ATOM 434 CB CYS D 115 30.282 -36.711 -16.666 1.00 19.76 C
ATOM 435 SG CYS D 115 30.909 -35.921 -15.118 1.00 24.18 S
ATOM 436 N THR D 116 28.123 -36.428 -19.021 1.00 20.81 N
ATOM 437 CA THR D 116 27.992 -36.945 -20.387 1.00 22.59 C
ATOM 438 C THR D 116 29.125 -36.471 -21.268 1.00 23.38 C
ATOM 439 O THR D 116 29.075 -36.655 -22.500 1.00 25.84 O
ATOM 440 CB THR D 116 26.653 -36.531 -20.995 1.00 23.31 C
ATOM 441 OG1 THR D 116 26.565 -35.101 -20.939 1.00 24.07 O
ATOM 442 CG2 THR D 116 25.481 -37.172 -20.200 1.00 23.15 C
ATOM 443 N ALA D 117 30.168 -35.931 -20.664 1.00 21.43 N
ATOM 444 CA ALA D 117 31.390 -35.481 -21.337 1.00 24.06 C
ATOM 445 C ALA D 117 32.448 -35.358 -20.249 1.00 26.97 C
ATOM 446 O ALA D 117 32.117 -35.391 -19.071 1.00 26.44 O
ATOM 447 CB ALA D 117 31.187 -34.134 -22.008 1.00 26.37 C
ATOM 448 N LYS D 118 33.698 -35.151 -20.645 1.00 25.83 N
ATOM 449 CA LYS D 118 34.728 -34.775 -19.670 1.00 25.51 C
ATOM 450 C LYS D 118 34.487 -33.370 -19.098 1.00 24.98 C
ATOM 451 O LYS D 118 34.290 -32.397 -19.842 1.00 25.11 O
ATOM 452 CB LYS D 118 36.104 -34.846 -20.351 1.00 23.48 C
ATOM 453 CG LYS D 118 37.291 -34.462 -19.519 1.00 28.28 C
ATOM 454 CD LYS D 118 38.573 -34.666 -20.371 1.00 31.65 C
ATOM 455 CE LYS D 118 39.801 -34.266 -19.571 1.00 38.13 C
ATOM 456 NZ LYS D 118 41.081 -34.299 -20.356 1.00 48.56 N1+
ATOM 457 N THR D 119 34.510 -33.248 -17.769 1.00 25.47 N
ATOM 458 CA THR D 119 34.285 -31.957 -17.101 1.00 25.51 C
ATOM 459 C THR D 119 35.419 -31.714 -16.102 1.00 25.80 C
ATOM 460 O THR D 119 35.180 -31.635 -14.881 1.00 24.44 O
ATOM 461 CB THR D 119 32.914 -31.904 -16.394 1.00 23.51 C
ATOM 462 OG1 THR D 119 32.858 -32.940 -15.417 1.00 24.53 O
ATOM 463 CG2 THR D 119 31.800 -32.123 -17.420 1.00 25.18 C
ATOM 464 N GLU D 120 36.628 -31.544 -16.635 1.00 27.77 N
ATOM 465 CA GLU D 120 37.828 -31.508 -15.783 1.00 27.56 C
ATOM 466 C GLU D 120 37.882 -30.311 -14.857 1.00 27.55 C
ATOM 467 O GLU D 120 38.555 -30.403 -13.823 1.00 26.35 O
ATOM 468 CB GLU D 120 39.084 -31.530 -16.643 1.00 30.66 C
ATOM 469 CG GLU D 120 39.410 -30.217 -17.347 1.00 30.87 C
ATOM 470 CD GLU D 120 38.528 -29.970 -18.621 1.00 37.11 C
ATOM 471 OE1 GLU D 120 37.664 -30.833 -19.035 1.00 34.51 O
ATOM 472 OE2 GLU D 120 38.706 -28.872 -19.225 1.00 47.55 O1-
ATOM 473 N TRP D 121 37.216 -29.194 -15.183 1.00 26.61 N
ATOM 474 CA TRP D 121 37.251 -28.059 -14.270 1.00 26.51 C
ATOM 475 C TRP D 121 36.530 -28.342 -12.952 1.00 27.57 C
ATOM 476 O TRP D 121 36.670 -27.553 -12.012 1.00 26.63 O
ATOM 477 CB TRP D 121 36.652 -26.784 -14.888 1.00 25.76 C
ATOM 478 CG TRP D 121 35.202 -26.912 -15.224 1.00 27.10 C
ATOM 479 CD1 TRP D 121 34.094 -26.532 -14.450 1.00 27.46 C
ATOM 480 CD2 TRP D 121 34.677 -27.541 -16.385 1.00 25.48 C
ATOM 481 NE1 TRP D 121 32.940 -26.864 -15.126 1.00 28.62 N
ATOM 482 CE2 TRP D 121 33.268 -27.487 -16.304 1.00 26.98 C
ATOM 483 CE3 TRP D 121 35.270 -28.116 -17.524 1.00 27.46 C
ATOM 484 CZ2 TRP D 121 32.439 -28.010 -17.308 1.00 27.14 C
ATOM 485 CZ3 TRP D 121 34.442 -28.637 -18.525 1.00 31.36 C
ATOM 486 CH2 TRP D 121 33.051 -28.562 -18.412 1.00 30.65 C
ATOM 487 N LEU D 122 35.767 -29.425 -12.856 1.00 25.33 N
ATOM 488 CA LEU D 122 35.152 -29.862 -11.597 1.00 23.98 C
ATOM 489 C LEU D 122 36.081 -30.719 -10.727 1.00 23.35 C
ATOM 490 O LEU D 122 35.751 -30.988 -9.571 1.00 24.58 O
ATOM 491 CB LEU D 122 33.850 -30.638 -11.877 1.00 23.68 C
ATOM 492 CG LEU D 122 32.828 -29.843 -12.700 1.00 24.73 C
ATOM 493 CD1 LEU D 122 31.584 -30.708 -12.946 1.00 21.80 C
ATOM 494 CD2 LEU D 122 32.465 -28.530 -11.949 1.00 25.03 C
ATOM 495 N LYS D 125 36.820 -30.305 -6.869 1.00 25.57 N
ATOM 496 CA LYS D 125 35.703 -29.580 -6.276 1.00 26.75 C
ATOM 497 C LYS D 125 34.500 -30.451 -5.990 1.00 25.29 C
ATOM 498 O LYS D 125 33.741 -30.144 -5.073 1.00 24.89 O
ATOM 499 CB LYS D 125 35.277 -28.443 -7.211 1.00 28.30 C
ATOM 500 CG LYS D 125 36.318 -27.332 -7.163 1.00 35.00 C
ATOM 501 CD LYS D 125 36.493 -26.626 -8.490 1.00 42.70 C
ATOM 502 CE LYS D 125 37.582 -25.564 -8.326 1.00 52.79 C
ATOM 503 NZ LYS D 125 37.893 -24.945 -9.633 1.00 53.52 N1+
ATOM 504 N HIS D 126 34.303 -31.508 -6.760 1.00 20.67 N
ATOM 505 CA HIS D 126 33.239 -32.472 -6.559 1.00 19.39 C
ATOM 506 C HIS D 126 33.879 -33.839 -6.415 1.00 20.71 C
ATOM 507 O HIS D 126 34.773 -34.187 -7.186 1.00 22.34 O
ATOM 508 CB HIS D 126 32.235 -32.457 -7.778 1.00 19.32 C
ATOM 509 CG HIS D 126 31.564 -31.120 -7.978 1.00 20.73 C
ATOM 510 ND1 HIS D 126 30.298 -30.848 -7.508 1.00 21.33 N
ATOM 511 CD2 HIS D 126 32.017 -29.966 -8.534 1.00 19.48 C
ATOM 512 CE1 HIS D 126 30.008 -29.575 -7.731 1.00 21.59 C
ATOM 513 NE2 HIS D 126 31.039 -29.014 -8.346 1.00 20.27 N
ATOM 514 N SER D 147 18.207 -28.875 -21.602 1.00 22.59 N
ATOM 515 CA SER D 147 18.295 -27.571 -22.262 1.00 24.64 C
ATOM 516 C SER D 147 19.549 -26.875 -21.758 1.00 22.73 C
ATOM 517 O SER D 147 20.210 -27.346 -20.828 1.00 23.89 O
ATOM 518 CB SER D 147 17.053 -26.695 -21.983 1.00 25.24 C
ATOM 519 OG SER D 147 16.969 -26.445 -20.548 1.00 22.52 O
ATOM 520 N ARG D 148 19.829 -25.713 -22.357 1.00 25.96 N1+
ATOM 521 CA ARG D 148 21.000 -24.908 -22.018 1.00 24.35 C
ATOM 522 C ARG D 148 21.118 -24.600 -20.516 1.00 24.87 C
ATOM 523 O ARG D 148 22.214 -24.709 -19.940 1.00 24.30 O
ATOM 524 CB ARG D 148 20.934 -23.608 -22.798 1.00 31.57 C
ATOM 525 CG ARG D 148 22.101 -22.687 -22.535 1.00 27.70 C
ATOM 526 CD ARG D 148 22.135 -21.554 -23.544 1.00 35.79 C
ATOM 527 NE ARG D 148 23.237 -20.645 -23.264 1.00 36.55 N
ATOM 528 CZ ARG D 148 23.143 -19.622 -22.436 1.00 34.71 C
ATOM 529 NH1 ARG D 148 22.023 -19.379 -21.773 1.00 40.79 N
ATOM 530 NH2 ARG D 148 24.191 -18.821 -22.275 1.00 35.93 N1+
ATOM 531 N ASN D 149 20.023 -24.169 -19.864 1.00 27.33 N
ATOM 532 CA ASN D 149 20.090 -23.878 -18.429 1.00 22.56 C
ATOM 533 C ASN D 149 19.663 -25.052 -17.569 1.00 25.18 C
ATOM 534 O ASN D 149 19.642 -24.936 -16.335 1.00 25.69 O
ATOM 535 CB ASN D 149 19.285 -22.615 -18.046 1.00 28.00 C
ATOM 536 CG ASN D 149 17.771 -22.857 -17.927 1.00 30.52 C
ATOM 537 OD1 ASN D 149 17.256 -23.895 -18.326 1.00 30.50 O
ATOM 538 ND2 ASN D 149 17.053 -21.853 -17.415 1.00 29.28 N
ATOM 539 N GLY D 150 19.389 -26.194 -18.156 1.00 22.16 N
ATOM 540 CA GLY D 150 19.146 -27.366 -17.378 1.00 21.26 C
ATOM 541 C GLY D 150 17.687 -27.694 -17.163 1.00 25.23 C
ATOM 542 O GLY D 150 17.392 -28.829 -16.792 1.00 22.37 O
TER 543 GLY D 150
END
my 9AX6_ligand.sdf is like this:
9AX6 - prepared
3D
Schrodinger Suite 2023-1.
58 65 0 0 1 0 999 V2000
28.3844 -19.9822 -16.9516 C 0 0 0 0 0 0
25.4875 -23.1282 -10.3767 C 0 0 0 0 0 0
26.6231 -25.1373 -9.5539 C 0 0 0 0 0 0
27.0774 -26.3440 -8.7962 C 0 0 0 0 0 0
25.9002 -27.2877 -8.5200 C 0 0 1 0 0 0
24.0159 -27.3295 -6.9669 C 0 0 0 0 0 0
23.6260 -27.3350 -5.5384 C 0 0 1 0 0 0
22.2136 -26.9332 -5.2349 C 0 0 0 0 0 0
22.6105 -28.3560 -5.0875 C 0 0 2 0 0 0
22.7302 -28.9715 -3.7061 C 0 0 0 0 0 0
26.4011 -28.7121 -8.6592 C 0 0 0 0 0 0
27.1938 -20.8727 -17.1865 C 0 0 0 0 0 0
26.9841 -30.5855 -10.2154 C 0 0 0 0 0 0
28.1612 -30.3901 -11.1775 C 0 0 0 0 0 0
27.6242 -29.6822 -12.4192 C 0 0 0 0 0 0
26.9452 -28.3531 -12.0392 C 0 0 1 0 0 0
26.2446 -27.7417 -13.2238 C 0 0 0 0 0 0
26.5159 -26.8475 -15.3904 C 0 0 0 0 0 0
27.6814 -26.7622 -16.3833 C 0 0 0 0 0 0
27.1205 -26.5878 -17.8111 C 0 0 0 0 0 0
28.4957 -28.0626 -16.3198 C 0 0 0 0 0 0
28.6716 -25.6155 -16.0578 C 0 0 0 0 0 0
27.1061 -22.3303 -15.1609 C 0 0 0 0 0 0
28.0818 -24.2308 -15.9768 C 0 0 0 0 0 0
28.0024 -23.2906 -16.9926 C 0 0 0 0 0 0
28.5591 -23.4314 -18.3454 C 0 0 0 0 0 0
27.7605 -23.4466 -19.5145 C 0 0 0 0 0 0
29.6483 -23.8015 -20.8236 C 0 0 0 0 0 0
30.4866 -23.8279 -19.7158 C 0 0 0 0 0 0
29.9292 -23.6180 -18.4608 C 0 0 0 0 0 0
32.7261 -24.1703 -18.6646 C 0 0 0 0 0 0
33.9399 -25.0305 -18.9399 C 0 0 0 0 0 0
35.9557 -25.1857 -20.2516 C 0 0 0 0 0 0
27.5019 -23.6353 -14.8090 C 0 0 0 0 0 0
33.8114 -24.6412 -21.2564 C 0 0 0 0 0 0
32.5912 -23.7524 -21.1059 C 0 0 0 0 0 0
26.2552 -23.2967 -19.4666 C 0 0 2 0 0 0
25.5955 -24.3169 -20.3667 C 0 0 0 0 0 0
24.5312 -21.6612 -19.7429 C 0 0 0 0 0 0
27.2985 -24.0817 -13.4994 C 0 0 0 0 0 0
26.6978 -23.2245 -12.5771 C 0 0 0 0 0 0
26.2813 -21.9491 -12.9679 C 0 0 0 0 0 0
26.4929 -21.4701 -14.2480 C 0 0 0 0 0 0
26.3970 -23.6762 -11.2180 C 0 0 0 0 0 0
27.4033 -22.1404 -16.5001 N 0 0 0 0 0 0
25.2963 -27.0507 -7.2139 N 0 0 0 0 0 0
26.4230 -29.2568 -9.9167 N 0 0 0 0 0 0
25.8976 -28.5247 -11.0191 N 0 0 0 0 0 0
28.3073 -23.6612 -20.7263 N 0 0 0 0 0 0
31.8745 -23.9780 -19.8437 N 0 0 0 0 0 0
34.6700 -24.4971 -20.0880 N 0 3 0 0 0 0
27.0659 -24.7992 -10.7270 N 0 0 0 0 0 0
23.2086 -27.6271 -7.8415 O 0 0 0 0 0 0
26.8514 -29.3438 -7.7040 O 0 0 0 0 0 0
25.0266 -27.6650 -13.3279 O 0 0 0 0 0 0
27.1221 -27.3203 -14.1412 O 0 0 0 0 0 0
25.9127 -21.9870 -19.9232 O 0 0 0 0 0 0
25.4453 -24.0392 -8.9265 S 0 0 0 0 0 0
1 12 1 0 0 0
2 44 2 0 0 0
2 58 1 0 0 0
3 4 1 0 0 0
3 52 2 0 0 0
3 58 1 0 0 0
4 5 1 0 0 0
5 11 1 0 0 0
5 46 1 0 0 0
6 7 1 0 0 0
6 46 1 0 0 0
6 53 2 0 0 0
7 8 1 0 0 0
7 9 1 0 0 0
8 9 1 0 0 0
9 10 1 0 0 0
11 47 1 0 0 0
11 54 2 0 0 0
12 45 1 0 0 0
13 14 1 0 0 0
13 47 1 0 0 0
14 15 1 0 0 0
15 16 1 0 0 0
16 17 1 0 0 0
16 48 1 0 0 0
17 55 2 0 0 0
17 56 1 0 0 0
18 19 1 0 0 0
18 56 1 0 0 0
19 20 1 0 0 0
19 21 1 0 0 0
19 22 1 0 0 0
22 24 1 0 0 0
23 34 2 0 0 0
23 43 1 0 0 0
23 45 1 0 0 0
24 25 2 0 0 0
24 34 1 0 0 0
25 26 1 0 0 0
25 45 1 0 0 0
26 27 1 0 0 0
26 30 2 0 0 0
27 37 1 0 0 0
27 49 2 0 0 0
28 29 2 0 0 0
28 49 1 0 0 0
29 30 1 0 0 0
29 50 1 0 0 0
31 32 1 0 0 0
31 50 1 0 0 0
32 51 1 0 0 0
33 51 1 0 0 0
34 40 1 0 0 0
35 36 1 0 0 0
35 51 1 0 0 0
36 50 1 0 0 0
37 38 1 0 0 0
37 57 1 0 0 0
39 57 1 0 0 0
40 41 2 0 0 0
41 42 1 0 0 0
41 44 1 0 0 0
42 43 2 0 0 0
44 52 1 0 0 0
47 48 1 0 0 0
M CHG 1 51 1
M END
> <s_m_entry_id>
1
> <s_m_entry_name>
9AX6_ligand.1
> <s_m_Source_Path>
C:\Users\user\Desktop
> <s_m_Source_File>
9AX6_ligand.sdf
> <i_m_Source_File_Index>
1
$$$$
Bugs in sample_linker.py
Hi，author,
1. wrong checkpoint name
when I tried to generate linker for custom pocket and molecule with command:
"
python -u sample_linker.py
--ckpt 500.pt
--pdb_path 3WZE/pocket.pdb
--mol_file 3WZE/3wze-mol.sdf
--mask 0 1 2 3 4 5 6 7 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
--num_atom 4
--num_samples 100
--sampling_type generalized
--batch_size 32
"
but I got a error:
"
Traceback (most recent call last):
File "/home/PMDM/sample_linker.py", line 293, in
data_list, = construct_dataset_pocket(num_samples*1,batch_size,dataset_info,num_points,None,start_linker,None,
File "/home/PMDM/utils/sample.py", line 154, in construct_dataset_pocket
atom_type_linker = torch.cat([start_linker['linker_atom_type'], atom_type])
RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 10 but got size 8 for tensor number 1 in the list._
"
if I rename the checkpoint to "500_pocket.pt", this bug have been fixed.
2. missing QVinaDockingTask in sample_linker.py
QVinaDockingTask.from_generated_data(protein_filename,gmol,protein_root) line 391.
Missing geometry.py in the models folder when sample molecules for given customized pockets
Hi，
when I try to sample molecules for our customized pockets with command:
"python -u sample_for_pdb.py --ckpt ./500.pt --pdb_path 3WZE/pocket.pdb --num_atom 32 --num_samples 100 --sampling_type generalized"
But, I got an error:
"File "/home/PMDM/sample_for_pdb.py", line 13, in
from models.epsnet import get_model
File "/home/PMDM/models/epsnet/init.py", line 1, in
from .MDM_pocket_coor_shared import MDM_full_pocket_coor_shared
File "/home/PMDM/models/epsnet/MDM_pocket_coor_shared.py", line 15, in
from ..encoders.schnet_geo import SchNetEncoder # wufeil
File "/home/PMDM/models/encoders/schnet_geo.py", line 8, in
from ..geometry import get_distance
ModuleNotFoundError: No module named 'models.geometry' "
It looks like the geometry.py file is missing in the models folder
There is no prepare_receptor4.py in the evaluation/ directory
There is no prepare_receptor4.py in the evaluation/ directory. It is required for pdbqt conversion, and required by prepare_qdbqt.py
sample_for_pdb.py error
Dear Dr. Huang, I have encountered following errors when applying the sample_for_pdb.py . Would you please help me with it? Many thanks.
python -u sample_for_pdb.py --ckpt pretrained/500.pt --pdb_path 5fc4_md.pdb --num_atom 32 --num_samples 20 --sampling_type generalized
sh: module: command not found
/home/public/.conda/envs/mol/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/home/public/.conda/envs/mol/lib/python3.9/site-packages/torchvision/image.so: undefined symbol: _ZN5torch3jit17parseSchemaOrNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE'If you don't plan on using image functionality from torchvision.io, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have libjpeg or libpng installed before building torchvision from source?
warn(
Entropy of n_nodes: H[N] -1.3862943649291992
[2024-05-12 13:49:16,210::test::INFO] Namespace(pdb_path='5fc4_md.pdb', sdf_path=None, num_atom=32, build_method='reconstruct', config=None, cuda=True, ckpt='pretrained/500.pt', save_traj=False, num_samples=20, batch_size=10, resume=None, tag='', clip=1000.0, n_steps=1000, global_start_sigma=inf, w_global_pos=1.0, w_local_pos=1.0, w_global_node=1.0, w_local_node=1.0, sampling_type='generalized', eta=1.0)
[2024-05-12 13:49:16,210::test::INFO] {'model': {'type': 'diffusion', 'network': 'MDM_full_pocket_coor_shared', 'hidden_dim': 128, 'protein_hidden_dim': 128, 'num_convs': 3, 'num_convs_local': 3, 'protein_num_convs': 2, 'cutoff': 3.0, 'g_cutoff': 6.0, 'encoder_cutoff': 6.0, 'time_emb': True, 'atom_num_emb': False, 'mlp_act': 'relu', 'beta_schedule': 'sigmoid', 'beta_start': 1e-07, 'beta_end': 0.002, 'num_diffusion_timesteps': 1000, 'edge_order': 3, 'edge_encoder': 'mlp', 'smooth_conv': False, 'num_layer': 9, 'feats_dim': 5, 'soft_edge': True, 'norm_coors': True, 'm_dim': 128, 'context': 'None', 'vae_context': False, 'num_atom': 10, 'protein_feature_dim': 31}, 'train': {'seed': 2021, 'batch_size': 16, 'val_freq': 250, 'max_iters': 500, 'max_grad_norm': 10.0, 'num_workers': 4, 'anneal_power': 2.0, 'optimizer': {'type': 'adam', 'lr': 0.001, 'weight_decay': 0.0, 'beta1': 0.95, 'beta2': 0.999}, 'scheduler': {'type': 'plateau', 'factor': 0.6, 'patience': 10, 'min_lr': 1e-06}, 'transform': {'mask': {'type': 'mixed', 'min_ratio': 0.0, 'max_ratio': 1.2, 'min_num_masked': 1, 'min_num_unmasked': 0, 'p_random': 0.5, 'p_bfs': 0.25, 'p_invbfs': 0.25}, 'contrastive': {'num_real': 50, 'num_fake': 50, 'pos_real_std': 0.05, 'pos_fake_std': 2.0}}}, 'dataset': {'name': 'crossdock', 'type': 'pl', 'path': './data/crossdocked_pocket10', 'split': './data/split_by_name.pt'}}
[2024-05-12 13:49:16,211::test::INFO] Loading crossdock data...
Entropy of n_nodes: H[N] -3.543935775756836
[2024-05-12 13:49:16,212::test::INFO] Loading data...
[2024-05-12 13:49:16,257::test::INFO] Building model...
[2024-05-12 13:49:16,257::test::INFO] MDM_full_pocket_coor_shared
{'type': 'diffusion', 'network': 'MDM_full_pocket_coor_shared', 'hidden_dim': 128, 'protein_hidden_dim': 128, 'num_convs': 3, 'num_convs_local': 3, 'protein_num_convs': 2, 'cutoff': 3.0, 'g_cutoff': 6.0, 'encoder_cutoff': 6.0, 'time_emb': True, 'atom_num_emb': False, 'mlp_act': 'relu', 'beta_schedule': 'sigmoid', 'beta_start': 1e-07, 'beta_end': 0.002, 'num_diffusion_timesteps': 1000, 'edge_order': 3, 'edge_encoder': 'mlp', 'smooth_conv': False, 'num_layer': 9, 'feats_dim': 5, 'soft_edge': True, 'norm_coors': True, 'm_dim': 128, 'context': 'None', 'vae_context': False, 'num_atom': 10, 'protein_feature_dim': 31}
sdf idr: generate_ref
Entropy of n_nodes: H[N] -3.543935775756836
100%|████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 147.77it/s]
0%| | 0/4 [00:00<?, ?it/s]1
/disk1/liyan/PMDM-main/models/common.py:485: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated. Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:605.)
bgraph_adj = torch.sparse.LongTensor(
sample: 0it [00:00, ?it/s]
0%| | 0/4 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/disk1/liyan/PMDM-main/sample_for_pdb.py", line 350, in
pos_gen, pos_gen_traj, atom_type, atom_traj = model.langevin_dynamics_sample(
File "/disk1/liyan/PMDM-main/models/epsnet/MDM_pocket_coor_shared.py", line 790, in langevin_dynamics_sample
net_out = self.net(
File "/disk1/liyan/PMDM-main/models/epsnet/MDM_pocket_coor_shared.py", line 478, in net
node_attr_global, pos_attr_global = self.encoder_global(
File "/home/public/.conda/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/public/.conda/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/disk1/liyan/PMDM-main/models/encoders/egnn.py", line 476, in forward
x = layer(x, edge_index, edge_attr, batch=batch, ligand_batch=ligand_batch, size=bsize, linker_mask=linker_mask)
File "/home/public/.conda/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/public/.conda/envs/mol/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/disk1/liyan/PMDM-main/models/encoders/egnn.py", line 215, in forward
hidden_out, coors_out = self.propagate(edge_index, x=feats, edge_attr=edge_attr_feats,
ValueError: too many values to unpack (expected 2)
ModuleNotFoundError: No module named 'utils.sample'
Hi,
I have installed the code and setup the environment, but it appears that their is an issue with importing the utilities from sample.py into the other scripts:
When I run:
python sample_for_pdb.py -h
Traceback (most recent call last):
File "/home/michaelcarter/DD_tools/PMDM/sample_for_pdb.py", line 20, in <module>
from utils.sample import *
ModuleNotFoundError: No module named 'utils.sample
This error persists for all scripts... do you have a fix for this?
How to generate bond
Hi，Layne-Huang
Thank you very much for your work, and I want to know in your work, only generate the atom and atom coordinate, and use the 'open babel' tools to generate the final drug stucture? I wonder how to generate the bond?
thanks!
Zhongyu
How to generate .lmdb and .pt file before training?
Hi Huang,
Thank you for the great work.
When I reproduce your project with the code below:
"python -u train.py --config ./configs/crossdock_epoch.yml"
It gives me the following error:
“lmdb.Error: ./data/crossdocked_pocket10/crossdocked_pocket10_processed.lmdb: No such file or directory”, and I also noticed that I cannot find _name2id.pt file.
I would be appreivate if you could provide the scripts that how to generate .lmdb and .pt files for both corssdocked dataset and pdbind dataset.
Best
Training loss does not decrease
Hi there, I'm now reproducing the training code with cross-dock dataset but the training loss is maintained at ~1200 after 50 epochs.
I dived into the code and found that in ./models/epsnet/MDM_pocket_coor_shared.py, the F.mse_loss(pos_eq_global + pos_eq_local, target_pos_global + target_pos_local, reduction='none') means the loss between the output of self.net and the input with noise added, but IMP, it maybe the output of self.net and the original input.
Any comments from developers?
Unexpected Results in Sampling for Custom Pockets
I generate molecules using the following command:
python -u sample_for_pdb.py --ckpt checkpoints/500.pt --pdb_path test_data/3ug2.pdb --num_atom 50 --num_samples 2 --sampling_type generalized --build_method build
Why are the generated molecules all unstable ?
generated smile: [C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C]#[C].[C]C[C][C][O].[O]
generated smile: [C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C].[C]#[C].[N].[N].[O][O]
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:36<00:00, 36.71s/it]
[2024-05-06 16:16:47,651::test::INFO] valid:2
[2024-05-06 16:16:47,651::test::INFO] stable:0
mat1 and mat2 shapes cannot be multiplied (389x10 and 8x128)
Hi,
I was wondering why I am receiving this matrix multiplication error. I have downloaded the code and followed step by step the process. I am trying to train the model from scratch and after processing the crossDocked data, I run this command
python -u train.py --config configs/crossdock_epoch.yml
This is throwing an error on what seems like the very first iteration.
here is the whole stack trace if that is helpful....
Traceback (most recent call last):
File "/home/user/PMDM/train.py", line 416, in
avg_val_loss = validate(it)
File "/home/user/PMDM/train.py", line 186, in validate
loss = model(
File "/home/user/miniconda3/envs/pmdm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/miniconda3/envs/pmdm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/PMDM/models/epsnet/MDM_pocket_coor_shared.py", line 635, in forward
net_out = self.net(
File "/home/user/PMDM/models/epsnet/MDM_pocket_coor_shared.py", line 374, in net
ligand_atom_feature = self.ligand_encoder(
File "/home/user/miniconda3/envs/pmdm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/miniconda3/envs/pmdm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/PMDM/models/encoders/schnet.py", line 95, in forward
h = self.emblin(node_attr)
File "/home/user/miniconda3/envs/pmdm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/miniconda3/envs/pmdm/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/miniconda3/envs/pmdm/lib/python3.9/site-packages/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (389x10 and 8x128)
Training loss does not decrease
Hi there, I'm now reproducing the training code with cross-dock dataset but the training loss is maintained at ~1200 after 50 epochs.
I dived into the code and found that in ./models/epsnet/MDM_pocket_coor_shared.py, the F.mse_loss(pos_eq_global + pos_eq_local, target_pos_global + target_pos_local, reduction='none') means the loss between the output of self.net and the input with noise added, but IMP, it maybe the output of self.net and the original input.
Any comments from developers?
fail to run sample_for_pdb.py
hi, layne,
thank you so much for providing such amazing work!
when I try to run sample_for_pdb.py, the error occured as below:
the command line I use for running
!python -u sample_for_pdb.py \
--ckpt ckpt/500.pt \
--pdb_path protein/4yhj.pdb \
--num_atom 50 \
--num_samples 100 \
--sampling_type generalizedthe error info:
Entropy of n_nodes: H[N] -1.3862943649291992
Entropy of n_nodes: H[N] -3.543935775756836
{'type': 'diffusion', 'network': 'MDM_full_pocket_coor_shared', 'hidden_dim': 128, 'protein_hidden_dim': 128, 'num_convs': 3, 'num_convs_local': 3, 'protein_num_convs': 2, 'cutoff': 3.0, 'g_cutoff': 6.0, 'encoder_cutoff': 6.0, 'time_emb': True, 'atom_num_emb': False, 'mlp_act': 'relu', 'beta_schedule': 'sigmoid', 'beta_start': 1e-07, 'beta_end': 0.002, 'num_diffusion_timesteps': 1000, 'edge_order': 3, 'edge_encoder': 'mlp', 'smooth_conv': False, 'num_layer': 9, 'feats_dim': 5, 'soft_edge': True, 'norm_coors': True, 'm_dim': 128, 'context': 'None', 'vae_context': False, 'num_atom': 10, 'protein_feature_dim': 31}
sdf idr: protein\generate_ref
Entropy of n_nodes: H[N] -3.543935775756836
'module' is not recognized as an internal or external command,
operable program or batch file.
[2024-04-08 11:35:18,967::test::INFO] Namespace(pdb_path='protein/4yhj.pdb', sdf_path=None, num_atom=50, build_method='reconstruct', config=None, cuda=True, ckpt='ckpt/500.pt', save_traj=False, num_samples=100, batch_size=10, resume=None, tag='', clip=1000.0, n_steps=1000, global_start_sigma=inf, w_global_pos=1.0, w_local_pos=1.0, w_global_node=1.0, w_local_node=1.0, sampling_type='generalized', eta=1.0)
[2024-04-08 11:35:18,968::test::INFO] {'model': {'type': 'diffusion', 'network': 'MDM_full_pocket_coor_shared', 'hidden_dim': 128, 'protein_hidden_dim': 128, 'num_convs': 3, 'num_convs_local': 3, 'protein_num_convs': 2, 'cutoff': 3.0, 'g_cutoff': 6.0, 'encoder_cutoff': 6.0, 'time_emb': True, 'atom_num_emb': False, 'mlp_act': 'relu', 'beta_schedule': 'sigmoid', 'beta_start': 1e-07, 'beta_end': 0.002, 'num_diffusion_timesteps': 1000, 'edge_order': 3, 'edge_encoder': 'mlp', 'smooth_conv': False, 'num_layer': 9, 'feats_dim': 5, 'soft_edge': True, 'norm_coors': True, 'm_dim': 128, 'context': 'None', 'vae_context': False, 'num_atom': 10, 'protein_feature_dim': 31}, 'train': {'seed': 2021, 'batch_size': 16, 'val_freq': 250, 'max_iters': 500, 'max_grad_norm': 10.0, 'num_workers': 4, 'anneal_power': 2.0, 'optimizer': {'type': 'adam', 'lr': 0.001, 'weight_decay': 0.0, 'beta1': 0.95, 'beta2': 0.999}, 'scheduler': {'type': 'plateau', 'factor': 0.6, 'patience': 10, 'min_lr': 1e-06}, 'transform': {'mask': {'type': 'mixed', 'min_ratio': 0.0, 'max_ratio': 1.2, 'min_num_masked': 1, 'min_num_unmasked': 0, 'p_random': 0.5, 'p_bfs': 0.25, 'p_invbfs': 0.25}, 'contrastive': {'num_real': 50, 'num_fake': 50, 'pos_real_std': 0.05, 'pos_fake_std': 2.0}}}, 'dataset': {'name': 'crossdock', 'type': 'pl', 'path': './data/crossdocked_pocket10', 'split': './data/split_by_name.pt'}}
[2024-04-08 11:35:18,968::test::INFO] Loading crossdock data...
[2024-04-08 11:35:18,969::test::INFO] Loading data...
[2024-04-08 11:35:19,787::test::INFO] Building model...
[2024-04-08 11:35:19,788::test::INFO] MDM_full_pocket_coor_shared
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90%|█████████ | 18/20 [00:00<00:00, 54.99it/s]
100%|██████████| 20/20 [00:00<00:00, 53.82it/s]
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Traceback (most recent call last):
File "e:\Cheminfo_Workshop\4_Fragment_Scaffold_Evolution\PMDM-main\sample_for_pdb.py", line 344, in <module>
batch = Batch.from_data_list(datas, follow_batch=FOLLOW_BATCH).to(device)
File "c:\Users\LSY\.conda\envs\diffhopp\lib\site-packages\torch_geometric\data\batch.py", line 76, in from_data_list
batch, slice_dict, inc_dict = collate(
File "c:\Users\LSY\.conda\envs\diffhopp\lib\site-packages\torch_geometric\data\collate.py", line 85, in collate
value, slices, incs = _collate(attr, values, data_list, stores,
File "c:\Users\LSY\.conda\envs\diffhopp\lib\site-packages\torch_geometric\data\collate.py", line 134, in _collate
incs = get_incs(key, values, data_list, stores)
File "c:\Users\LSY\.conda\envs\diffhopp\lib\site-packages\torch_geometric\data\collate.py", line 267, in get_incs
repeats = [
File "c:\Users\LSY\.conda\envs\diffhopp\lib\site-packages\torch_geometric\data\collate.py", line 268, in <listcomp>
data.__inc__(key, value, store)
File "e:\Cheminfo_Workshop\4_Fragment_Scaffold_Evolution\PMDM-main\utils\data.py", line 35, in __inc__
if 'ligand_element' in self.keys():
TypeError: 'list' object is not callable
my OS windows 10, with python 3.9
Pytorch version = 1.13.1+cu117
Pytorch Geometric version = 2.3.1
CUDA version = 11.7
CUDA available = True
Random Pytorch test tensor = tensor([0.7748])
could you please provide the suggesions how to fix it?
btw: Is the generated molecule saved as a sdf file, and is it possible to define the path location of the stored file?
many thanks,
Best,
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