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Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network (NIPS 2017)

License: MIT License

Python 100.00%

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nips17-rexgen's Issues

TensorFlow 2.X or PyTorch version?

Hi,

I am wondering if you plan on releasing a TensorFlow 2 version (or PyTorch) anytime soon? I guess chances are slim since this repo has not been updated for a couple of years, but asking can never hurt.

Any chance to have a code asap ?

This is not an issue but having an apply model demo code would be great ;-)

Issue in running nntrain.py

I get the following error when I tried to run nntrain.py from core-wln-global.
To Reproduce
Run the following command:
python nntrain.py --train ../data/train.txt --hidden 320 --depth 8 --save_dir .
Error Description

Traceback (most recent call last):
  File "nntrain.py", line 65, in <module>
    atom_hiddens, _ = rcnn_wl_last(graph_inputs, batch_size=batch_size, hidden_size=hidden_size, depth=depth)
  File "/home/sachin/Desktop/nips17-rexgen/USPTO/core-wln-global/models.py", line 32, in rcnn_wl_last
    atom_features = tf.nn.relu(linearND(input_atom, hidden_size, "atom_embedding", init_bias=None))
  File "/home/sachin/Desktop/nips17-rexgen/USPTO/core-wln-global/utils/nn.py", line 81, in linearND
    target_shape = tf.concat(0, [X_shape, [output_size]])
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1121, in concat
    dtype=dtypes.int32).get_shape().assert_is_compatible_with(
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1048, in convert_to_tensor
    as_ref=False)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1144, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 971, in _autopacking_conversion_function
    return _autopacking_helper(v, dtype, name or "packed")
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 923, in _autopacking_helper
    return gen_array_ops.pack(elems_as_tensors, name=scope)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 4689, in pack
    "Pack", values=values, axis=axis, name=name)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
    return func(*args, **kwargs)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3272, in create_op
    op_def=op_def)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1790, in __init__
    control_input_ops)
  File "/home/sachin/anaconda3/envs/reaction_pred/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1629, in _create_c_op
    raise ValueError(str(e))
ValueError: Dimension 0 in both shapes must be equal, but are 2 and 1. Shapes are [2] and [1].
	From merging shape 0 with other shapes. for 'encoder/concat/concat_dim' (op: 'Pack') with input shapes: [2], [1].

During data processing, how to remove wrong reactions whose atom mapping are incorrect ?

Hi jin,
Thanks for your work. I am a little bit confused about the data processing of this dataset. I've read about the old issue, but I still don't know how to find those wrong reactions whose atom mapping are incorrect.
Can you tell me the details of removing wrong atom-mapping reactions? And what's name of this atom-mapping tool? Thank you, sincerely.

Reproducing Reaction Center Prediction

Hi,

I was trying to reproduce the reaction center prediction result shown in the paper with core-wln-global/nntrain.py, but the top 10 accuracy on the test dataset is about 5% lower than what you report in the report.

To reproduce the result, I used hidden=300 and depth=3, which gives me 756k parameters. Everything else is set to the nntrain.py default. Did I miss anything?

Thanks,
Wes

Data Processing

Hi,

Thanks for your work. And recently I am trying to reproduce the result of your paper.

I am a little bit confused about the dataset. There are 1,808,938 records in Lowe’s grants set, but I noticed you selected 480K records from them.

Could you please publish the data processing code or describe the algorithm?

Thanks in advance.

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