cimm-kzn / 3d-mil-qsar Goto Github PK

The files under /datasets are actually CSV files (ie they would not be parsed as SMILES files).

Thank you for your great repo.

I used
ins_net =AttentionNetRegressor(ndim=ndim, init_cuda=init_cuda)
ins_net.fit(x_train, y_train, n_epoch=n_epoch, batch_size=batch_size, weight_decay=weight_decay, lr=lr)

predictions = ins_net.predict(x_test)

print('3D/SI/Bag-AttentionNet: r2_score test = {:.2f}'.format(r2_score(y_test, predictions)))

then got:

TypeError Traceback (most recent call last)
/tmp/ipykernel_18462/3242914669.py in
1 #Bag-AttentionNet
----> 2 ins_net =AttentionNetRegressor(ndim=ndim, init_cuda=init_cuda)
3 ins_net.fit(x_train, y_train, n_epoch=n_epoch, batch_size=batch_size, weight_decay=weight_decay, lr=lr)
4
5 predictions = ins_net.predict(x_test)

/media/pharma1/e09e0694-327d-420b-b724-c197e5847ab6/3D-MIL-QSAR/miqsar/estimators/attention_nets.py in init(self, ndim, det_ndim, init_cuda)
69 class AttentionNetRegressor(AttentionNet, BaseRegressor):
70 def init(self, ndim=None, det_ndim=None, init_cuda=False):
---> 71 super().init(ndim=ndim, det_ndim=det_ndim, init_cuda=init_cuda)

/media/pharma1/e09e0694-327d-420b-b724-c197e5847ab6/3D-MIL-QSAR/miqsar/estimators/attention_nets.py in init(self, ndim, det_ndim, init_cuda)
35 input_dim = ndim[-1]
36 attention = []
---> 37 for dim in det_ndim:
38 attention.append(Linear(input_dim, dim))
39 attention.append(Sigmoid())

TypeError: 'NoneType' object is not iterable

what is det_ndim?

See https://citation-file-format.github.io/ and https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files

in miqsar/descriptor_calculation/pmapper/pharmacophore.py

Graph.node is deprecated in NetworkX, should be using Graph.nodes instead
https://networkx.org/documentation/stable/release/release_2.4.html#deprecations

Here are the diffs

95c95
<                 dist = self.__dist(self.__g.node[i]['xyz'], self.__g.node[j]['xyz'], bin_step)
---
>                 dist = self.__dist(self.__g.nodes[i]['xyz'], self.__g.nodes[j]['xyz'], bin_step)
144c144
<         feature_labels = dict(zip(ids, (self.__g.node[i]['label'] for i in ids)))
---
>         feature_labels = dict(zip(ids, (self.__g.nodes[i]['label'] for i in ids)))

Thank you for your repo.

When I ran the example.ipynb:

import os
from miqsar.utils import calc_3d_pmapper

#Choose dataset to be modeled and create a folder where the descriptors will be stored

nconfs = 5 #number of conformers to create; calculation is time consuming, so here we set 5, for real tasks set 25..100
ncpu = 7 # set number of CPU cores you have

dataset_file = 'datasets/CHEMBL1075104.smi'
descriptors_folder = os.path.join('descriptors')
os.mkdir(descriptors_folder)

bags, labels, molid = calc_3d_pmapper(dataset_file, nconfs=nconfs, stereo=False, path=descriptors_folder, ncpu=ncpu)

print(f'There are {len(bags)} molecules encoded with {bags[0].shape[1]} pmapper descriptors')

then got:

TypeError Traceback (most recent call last)
/tmp/ipykernel_99958/2926432156.py in
11 os.mkdir(descriptors_folder)
12
---> 13 bags, labels, molid = calc_3d_pmapper(dataset_file, nconfs=nconfs, stereo=False, path=descriptors_folder, ncpu=ncpu)
14
15 print(f'There are {len(bags)} molecules encoded with {bags[0].shape[1]} pmapper descriptors')

~/caocheng/3D-MIL-QSAR-main/miqsar/utils.py in calc_3d_pmapper(dataset_file, nconfs, stereo, path, ncpu)
24
25 for conf in conf_files:
---> 26 dsc_file = calc_pmapper_descriptors(conf, path=path, ncpu=ncpu, col_clean=None, del_undef=True)
27
28 with open(dsc_file, 'rb') as inp:

~/caocheng/3D-MIL-QSAR-main/miqsar/descriptor_calculation/pmapper_3d.py in calc_pmapper_descriptors(*args, **kwargs)
229
230 def calc_pmapper_descriptors(*args, **kwargs):
--> 231 return main(*args, **kwargs)

TypeError: main() got an unexpected keyword argument 'path'

I didn't change anything in example. Why is it wrong?

Please consider making a release and archive with the GitHub/Zenodo integration and then cite that DOI in the preprint.

I suggest removing the .git at the end of the URL use in the preprint and just list https://github.com/dzankov/3D-MIL-QSAR.

Hello,

When running example.ipynb, the following error occurs:
path=descriptors_folder, ncpu=ncpu)
File "/data/3D-MIL-QSAR-main/3D-MIL-QSAR-main/miqsar/utils.py", line 37, in calc_3d_pmapper
descr_num=descr_num, remove=0.05, keep_temp=False, ncpu=ncpu, verbose=False)
File "/data/3D-MIL-QSAR-main/3D-MIL-QSAR-main/miqsar/descriptor_calculation/pmapper_3d.py", line 344, in calc_pmapper_descriptors
return main(*args, **kwargs)
TypeError: main() got an unexpected keyword argument 'factory'

I followed the installing process and bellow is the conda list:

Name | Version | Build | Channel

_libgcc_mutex | 0.1 | main |  
_openmp_mutex | 5.1 | 1_gnu |  
blas | 1 | mkl | https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
bzip2 | 1.0.8 | h7b6447c_0
ca-certificates | 2023.12.12 | h06a4308_0
cairo | 1.16.0 | hb05425b_5
certifi | 2021.5.30 | py36h06a4308_0
cudatoolkit | 11.3.1 | h2bc3f7f_2
dataclasses | 0.8 | pyh4f3eec9_6
decorator | 4.4.2 | pypi_0 | pypi
ffmpeg | 4.3 | hf484d3e_0 | pytorch
fontconfig | 2.14.1 | hef1e5e3_0
freetype | 2.10.4 | h0708190_1 | conda-forge
glib | 2.69.1 | h4ff587b_1
gmp | 6.2.1 | h295c915_3
gnutls | 3.6.15 | he1e5248_0
icu | 58.2 | hf484d3e_1000 | conda-forge
intel-openmp | 2022.1.0 | h9e868ea_3769
joblib | 1.1.1 | pypi_0 | pypi
jpeg | 9b | 0 | https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
lame | 3.1 | h7b6447c_0
lcms2 | 2.12 | h3be6417_0
ld_impl_linux-64 | 2.38 | h1181459_1
libboost | 1.73.0 | h3ff78a5_11
libffi | 3.3 | he6710b0_2
libgcc-ng | 11.2.0 | h1234567_1
libgomp | 11.2.0 | h1234567_1
libiconv | 1.14 | 0 | https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
libidn2 | 2.3.4 | h5eee18b_0
libpng | 1.6.39 | h5eee18b_0
libstdcxx-ng | 11.2.0 | h1234567_1
libtasn1 | 4.19.0 | h5eee18b_0
libtiff | 4.2.0 | h85742a9_0
libunistring | 0.9.10 | h27cfd23_0
libuv | 1.44.2 | h5eee18b_0
libwebp-base | 1.3.2 | h5eee18b_0
libxcb | 1.15 | h7f8727e_0
libxml2 | 2.9.14 | h74e7548_0
lz4-c | 1.9.4 | h6a678d5_0
mkl | 2020.2 | 256 |  
mkl-service | 2.3.0 | py36he8ac12f_0
mkl_fft | 1.3.0 | py36h54f3939_0
mkl_random | 1.1.1 | py36h0573a6f_0
ncurses | 6.4 | h6a678d5_0
nettle | 3.7.3 | hbbd107a_1
networkx | 2.5.1 | pypi_0 | pypi
numpy | 1.19.2 | py36h54aff64_0
numpy-base | 1.19.2 | py36hfa32c7d_0
olefile | 0.44 | pypi_0 | pypi
openbabel | 3.1.1 | py36he0ca515_2 | conda-forge
openh264 | 2.1.1 | h4ff587b_0
openjpeg | 2.4.0 | h3ad879b_0
openssl | 1.1.1w | h7f8727e_0
pandas | 1.1.5 | py36ha9443f7_0
pcre | 8.45 | h9c3ff4c_0 | conda-forge
pillow | 8.3.1 | py36h2c7a002_0
pip | 21.2.2 | py36h06a4308_0
pixman | 0.40.0 | h7f8727e_1
pmapper | 1.0.4 | pypi_0 | pypi
py-boost | 1.73.0 | py36ha9443f7_11
python | 3.6.13 | h12debd9_1
python-dateutil | 2.8.2 | pyhd3eb1b0_0
python_abi | 3.6 | 2_cp36m | conda-forge
pytorch | 1.10.2 | py3.6_cuda11.3_cudnn8.2.0_0 | pytorch
pytorch-mutex | 1 | cuda | pytorch
pytorch-ranger | 0.1.1 | pypi_0 | pypi
pytz | 2021.3 | pyhd3eb1b0_0
rdkit | 2020.09.1.0 | py36hd50e099_1 | rdkit
readline | 8.2 | h5eee18b_0
scikit-learn | 0.24.2 | pypi_0 | pypi
scipy | 1.5.4 | pypi_0 | pypi
setuptools | 58.0.4 | py36h06a4308_0
six | 1.16.0 | pypi_0 | pypi
sqlite | 3.41.2 | h5eee18b_0
threadpoolctl | 3.1.0 | pypi_0 | pypi
tk | 8.6.12 | h1ccaba5_0
torch-optimizer | 0.3.0 | pypi_0 | pypi
torchaudio | 0.10.2 | py36_cu113 | pytorch
torchvision | 0.11.3 | py36_cu113 | pytorch
typing_extensions | 4.1.1 | pyh06a4308_0
wheel | 0.37.1 | pyhd3eb1b0_0
xz | 5.4.5 | h5eee18b_0
zlib | 1.2.13 | h5eee18b_0
zstd | 1.4.9 | haebb681_0

Thank you.

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