Code for the paper "JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design"

Related tags

Deep LearningJANUS
Overview

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design

This repository contains code for the paper: JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design. By: AkshatKumar Nigam, Robert Pollice, Alán Aspuru-Guzik

Package Requirements:

Using The Code:

The code can be run using:

python ./JANUS.py

Within params_init.py, a user has the option to provide:

  1. A function for calculting property values (see function calc_prop).
  2. Input parameters that are to be used by JANUS (see function generate_params). Initial parameters are provided. These are picked based on prior experience by the authors of the paper.

Output Generation:

All results from running JANUS will be stored here. The following files will be created:

  1. fitness_explore.txt: Fitness values for all molecules from the exploration component of JANUS.
  2. fitness_local_search.txt: Fitness values for all molecules from the exploitation component of JANUS.
  3. generation_all_best.txt: Smiles and fitness value for the best molecule encountered in every generation (iteration).
  4. init_mols.txt: List of molecules used to initialte JANUS.
  5. population_explore.txt: SMILES for all molecules from the exploration component of JANUS.
  6. population_local_search.txt: SMILES for all molecules from the exploitation component of JANUS.

Paper Results/Reproducibility:

Our code and results for each experiment in the paper can be found here:

Questions, problems?

Make a github issue 😄 . Please be as clear and descriptive as possible. Please feel free to reach out in person: (akshat[DOT]nigam[AT]mail[DOT]utoronto[DOT]ca, rob[DOT]pollice[AT]utoronto[DOT]ca)

License

Apache License 2.0

Comments
  • freeze_support runtime error with provided example

    freeze_support runtime error with provided example

    Hello, I'm trying out JANUS and if I just run the example inside the tests as provided I get

      File "/Users/nsofroniew/opt/anaconda3/envs/chem/lib/python3.9/multiprocessing/spawn.py", line 134, in _check_not_importing_main
    raise RuntimeError('''
RuntimeError:
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.

    This can be fixed by wrapping the creation and running of the JANUS agent and inside

    if __name__ == '__main__':
    torch.multiprocessing.freeze_support()

    see for a similar fix https://github.com/pytorch/pytorch/issues/5858#issuecomment-373950687

    I'm happy to submit a PR to fix if you like - it will be very simple

    opened by sofroniewn 4
  • Enquiring on multi-objective fitness function

    Enquiring on multi-objective fitness function

    Hi, I would like to ask if there are multiple objectives which I want the fitness function to fulfil, how would I write out the function because the example on the README.md is only for one objective (logP)?

    Thank you! :D

    opened by yipy0005 3
  • error in numpy/ list multiplication inside mutate_smi_list

    error in numpy/ list multiplication inside mutate_smi_list

    Hello, I am trying to run the provided example, but get the following error

    $ python ./example.py
    Unique and valid fragments generated: 107308
    Unique and valid fragments generated: 107308
On generation 0/200
/Users/nsofroniew/opt/anaconda3/envs/chem/lib/python3.9/site-packages/janus/janus.py:457: RuntimeWarning: overflow encountered in power
  prob_ = 1.0 / (3.0 ** ((F_50_val - fitness) / (F_50_val - F_25_val)) + 1)
    (Explr) Top Fitness: 15.811199999999957
    (Explr) Top Smile: CCCCCCCCCCCCCCCCOP(=O)(O)Oc1ccc(C=Cc2ccc(OP(=O)(O)OCCCCCCCCCCCCCCCC)cc2)cc1
Traceback (most recent call last):
  File "/Users/nsofroniew/Documents/code/chem/janus/tests/./example.py", line 81, in <module>
  File "/Users/nsofroniew/opt/anaconda3/envs/chem/lib/python3.9/site-packages/janus/janus.py", line 317, in run
    mut_smi_loc = self.mutate_smi_list(smiles_local_search, "local")
  File "/Users/nsofroniew/opt/anaconda3/envs/chem/lib/python3.9/site-packages/janus/janus.py", line 137, in mutate_smi_list
    smi_list = smi_list * num_random_samples
numpy.core._exceptions._UFuncNoLoopError: ufunc 'multiply' did not contain a loop with signature matching types (dtype('<U98'), dtype('int64')) -> None

    It looks like smi_list has become a numpy array, which is unintended and it should be a list. This can be fixed by making this line https://github.com/aspuru-guzik-group/JANUS/blob/a52e5994a108063e3fc1d692752cea0ff0240120/src/janus/janus.py#L137 be

        smi_list = list(smi_list) * num_random_samples

    There could be other, more desirable fixes that avoid this problem altogether. I am happy to submit a PR if this fix is desired though.

    opened by sofroniewn 2
  • Unhandled exceptions and KeyError

    Unhandled exceptions and KeyError

    I tried running JANUS.py but got a number of exceptions. In addition, the script terminated with a KeyError.

    Initial population obtained!
Forming Fragments!
    Fragment creation: 0/10000
    Fragment creation: 1000/10000
    Fragment creation: 2000/10000
    Fragment creation: 3000/10000
    Fragment creation: 4000/10000
    Fragment creation: 5000/10000
    Fragment creation: 6000/10000
    Fragment creation: 7000/10000
    Fragment creation: 8000/10000
    Fragment creation: 9000/10000
Process Process-5:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch3_1]'
	SELFIES: [S][C][=N][N][=C][Branch1][Ring1][C][C][C][Branch1][#Branch1][O][C][Branch1][C][C][=O][C][Branch1][C][C][Branch1][C][C][C][N][N][N][Branch3_1][C][C][=C][=C][Branch1][C][N][C][=C][Ring1][#Branch1][C][#Branch1][N]
Process Process-7:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 124, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Expl=Ring3]'
	SELFIES: [C][Expl=Ring3][=C][Branch2][Ring1][=Branch1][C][=C][C][=C][Branch1][#C][C][=C][Ring1][=Branch1][C][=C][C][=C][C][=C][Ring1][=Branch1][Ring1][=C][F][C][Branch1][C][C][C][C][C][N][Branch1][=Branch2][S][Branch1][C][C][=Branch1][C][=O][=O][C][C][Ring1][#Branch2][C][N][Branch1][Branch1][C][C][C][C][C][C][C][C]
Process Process-4:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch1_3]'
	SELFIES: [C][C][=C][Branch1][=Branch1][C][C][=Ring1][=Branch1][Branch1_3][C][C][=C][C][=C][Branch1][C][C][=C][C][=C][Branch1][C][N][C][=N][Ring1][#Branch1][#Branch2][C][N][C][O][C][C][Ring1][=Branch1][C][Branch1][C][O][=C][C][=C][Ring1][=C][O][Ring1][P]
Process Process-6:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch3_2]'
	SELFIES: [C][C][=Branch1][#C][=C][C][=C][C][=Ring1][=Branch1][C][Branch1][C][F][Branch1][C][F][F][C][C][Branch1][C][C][=N][N][C][=Branch1][C][C][C][N][C][C][C][Branch1][C][N][C][C][Ring1][#Branch1][C][O][=C][C][Branch1][O][C][Branch3_2][C][N][C][C][O][C][C][Ring1][=Branch1][=O]
Process Process-8:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch2_1]'
	SELFIES: [C][=Branch1][C][=O][Branch1][C][O][C][N][C][=Branch1][C][=O][C][Branch2][Ring1][=Branch1][N][C][=Branch1][C][=O][C][Branch1][N][C][=C][C][=C][C][Branch2_1][Ring2][=N][Ring1][=Branch1][N][=N][O][C][Ring2][Ring1][C][S][C][C][=Ring2][Ring1][=Branch1][S][C][S][N][=C][Branch1][Ring2][N][=Ring1][Branch1][S][C][C][N]
Process Process-11:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch2_3]'
	SELFIES: [C][C][=C][C][Branch1][=C][C][N][Branch1][C][C][N][C][=Branch1][C][=O][C][S][C][C][C][Ring1][#Branch1][=C][C][=Ring1][O][C][=C][C][=C][C][=C][Ring1][=Branch1][O][C][=C][Branch2_3][Branch1]
Process Process-10:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch3_3]'
	SELFIES: [F][C][=C][C][=C][Branch2][Ring2][O][C][N][C][Branch2][Ring2][Ring2][C][Branch1][#C][C][C][=C][Branch1][=Branch2][C][=C][C][=C][C][=Branch1][C][=O][N][Branch1][C][C][Branch3_3][C][Branch1][C][F][C][=Ring1][#Branch1][F][=N][C][C][Ring1][C][N][C][C][N][C][C][Ring1][=Branch1][=C][=N][C][=Branch1][Branch1][=N][C][=Ring1][=Branch1][N][Branch1][C][C][C][=O][C][=C][Ring2][Ring1][=N]
Process Process-9:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 116, in _derive_mol_from_symbols
    init_state=binit_state, root_atom=prev_atom, rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch3_3]'
	SELFIES: [C][C][Branch1][#Branch2][C][=C][C][=Branch1][Ring2][=C][=Branch1][F][=N][C][=C][Branch2][Ring2][Branch1][N][Branch1][Branch2][C][=N][C][Ring1][=Branch1][=Ring1][=C][N][=C][C][=C][C][=C][Branch1][P][C][=Branch1][Ring2][=C][Ring1][=Branch1][C][Branch3_3][C][N][=C][C][=C][C][=Ring1][=Branch1][O][C][O]
On generation 0/200
    (Explr) Top Fitness: 1.0
    (Explr) Top Smile: C=C1N=CC=C(N=CNc2ccc(N)cc2)N=C(C)[SH]1C
Process Process-27:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
    self.run()
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/multiprocessing/process.py", line 99, in run
    self._target(*self._args, **self._kwargs)
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 205, in calc_parr_prop
    props_collect[property_name][smile] = get_prop_material(smile, alphabet=alphabet, num_random_samples=num_random_samples, num_mutations=num_mutations)  # TODO: TESTING
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in get_prop_material
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/software/JANUS/mutate_parr.py", line 129, in 
    mutated_smiles = [decoder(x) for x in mutated_sf]
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 59, in decoder
    rings=rings
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 147, in _derive_mol_from_symbols
    _raise_decoder_error(selfies, symbol)
  File "/home/ubuntu/anaconda3/envs/reinvent.v3.2/lib/python3.7/site-packages/selfies/decoder.py", line 179, in _raise_decoder_error
    raise DecoderError(err_msg)
selfies.exceptions.DecoderError: invalid symbol '[Branch3_2]'
	SELFIES: [C][O][=C][N][C][N][C][=Branch1][C][=O][N][Branch1][=Branch1][N+1][=Branch1][C][=O][O-1][C][Ring1][=Branch2][N][Ring1][N][O][C][=C][C][C][N][C][N][C][N][C][=C][C][=C][Branch1][C][F][C][Branch1][C][F][=C][Ring1][Branch2][=N][C][Branch1][C][C][Branch3_2][Branch1][=Branch1][C][Branch1][C][C][=O][S][Ring1][=Branch2][=C][C][=Ring1][#Branch1][N][C][N][C][C][=C][C][=C][C][=C][Ring1][=Branch1][C][N][=C][Branch1][=C][SH1][Branch1][C][C][C][=C][C][=C][C][=C][Ring1][=Branch1][=Branch1][C][=C][N][=C][C][=Ring1][#Branch2][C]
Traceback (most recent call last):
  File "./JANUS.py", line 308, in 
    mut_smi_dict_local  = mut_smi_dict_local[population[top_idx]]
KeyError: 'C=C1N=CC=C(N=CNc2ccc(N)cc2)N=C(C)[SH]1C'
    opened by PatWalters 2
  • multiprocessing in windows

    multiprocessing in windows

    Hi. I was run the code, but I met errors in all codes that used multiprocessing. As I know these errors came from the difference of OS.

    Since I use Windows, I wonder how to correct the code for Windows.

    Thanks.

    opened by gunwook12 1

  • "aux" folder name should be changed to make the repository compatible with Windows.

    It looks like "aux" is a reserved word in Windows and directory of this name cannot be used. This is causing error, when I try to clone the repository.

    opened by sptiwari 1
  • Question: How sensitive is the model to molecule initialization?

    Question: How sensitive is the model to molecule initialization?

    Hello, great work! I'm excited to try this out for a chemical design project I'm working on. One question that I had after reading the pre-print— how sensitive is the model to molecule initialization?

    I read that you can start JANUS with random molecules or provide it with defined molecules. Did you perform any tests on what happens if the best molecule's scaffold was ablated from the initial bank of molecules? And how much, if at all, does rational initialization improve over random molecular initialization? Does this relate back to being trapped in local minima, and would the choice of initialization help explore more chemical space?

    Thank you!

    question 
    opened by jrhorne 1
Releases(v1)
Owner
Aspuru-Guzik group repo
Aspuru-Guzik group repo
GitHub Repository
A state of the art of new lightweight YOLO model implemented by TensorFlow 2.

CSL-YOLO: A New Lightweight Object Detection System for Edge Computing This project provides a SOTA level lightweight YOLO called "Cross-Stage Lightwe

Miles Zhang 54 Dec 21, 2022
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

Xuebin Qin 6.5k Jan 09, 2023
LAMDA: Label Matching Deep Domain Adaptation

LAMDA: Label Matching Deep Domain Adaptation This is the implementation of the paper LAMDA: Label Matching Deep Domain Adaptation which has been accep

Tuan Nguyen 9 Sep 06, 2022
Simulate genealogical trees and genomic sequence data using population genetic models

msprime msprime is a population genetics simulator based on tskit. Msprime can simulate random ancestral histories for a sample of individuals (consis

Tskit developers 150 Dec 14, 2022
Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting Note: You can find here the accompanying seq2seq RNN forecas

Guillaume Chevalier 1k Dec 25, 2022
👨‍💻 run nanosaur in simulation with Gazebo/Ingnition

🦕 👨‍💻 nanosaur_gazebo nanosaur The smallest NVIDIA Jetson dinosaur robot, open-source, fully 3D printable, based on ROS2 & Isaac ROS. Designed & ma

nanosaur 9 Jul 19, 2022
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Contents Local and Global GAN Cross-View Image Translation Semantic Image Synthesis Acknowledgments Related Projects Citation Contributions Collaborat

Hao Tang 131 Dec 07, 2022
EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

MADE (Multi-Adapter Dataset Experts) This repository contains the implementation of MADE (Multi-adapter dataset experts), which is described in the pa

Princeton Natural Language Processing 68 Jul 18, 2022
High performance Cross-platform Inference-engine, you could run Anakin on x86-cpu,arm, nv-gpu, amd-gpu,bitmain and cambricon devices.

Anakin2.0 Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineer

514 Dec 28, 2022
Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation

SSWS-loss_function_based_on_MS-TCN Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation Supervised Sliding Window

3 Aug 03, 2022
Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"

NeuralSymbolicRegressionThatScales Pytorch implementation and pretrained models for the paper "Neural Symbolic Regression That Scales", presented at I

35 Nov 25, 2022
Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

AdversarialTexture Adversarial Texture Optimization from RGB-D Scans (CVPR 2020). Scanning Data Download Please refer to data directory for details. B

Jingwei Huang 153 Nov 28, 2022
AI Flow is an open source framework that bridges big data and artificial intelligence.

Flink AI Flow Introduction Flink AI Flow is an open source framework that bridges big data and artificial intelligence. It manages the entire machine

144 Dec 30, 2022
MMDetection3D is an open source object detection toolbox based on PyTorch

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project developed by MMLab.

OpenMMLab 3.2k Jan 05, 2023
PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

ContextNet ContextNet has CNN-RNN-transducer architecture and features a fully convolutional encoder that incorporates global context information into

Sangchun Ha 24 Nov 24, 2022
Powerful and efficient Computer Vision Annotation Tool (CVAT)

Computer Vision Annotation Tool (CVAT) CVAT is free, online, interactive video and image annotation tool for computer vision. It is being used by our

OpenVINO Toolkit 8.6k Jan 01, 2023
Convert ONNX model graph to Keras model format.

Convert ONNX model graph to Keras model format.

Grigory Malivenko 175 Dec 28, 2022
DyNet: The Dynamic Neural Network Toolkit

The Dynamic Neural Network Toolkit General Installation C++ Python Getting Started Citing Releases and Contributing General DyNet is a neural network

Chris Dyer's lab @ LTI/CMU 3.3k Jan 06, 2023
Eth brownie struct encoding example

eth-brownie struct encoding example Overview This repository contains an example of encoding a struct, so that it can be used in a function call, usin

Ittai Svidler 2 Mar 04, 2022