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
PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

Learning to Reweight Examples for Robust Deep Learning Unofficial PyTorch implementation of Learning to Reweight Examples for Robust Deep Learning. Th

Daniel Stanley Tan 325 Dec 28, 2022
MERLOT: Multimodal Neural Script Knowledge Models

merlot MERLOT: Multimodal Neural Script Knowledge Models MERLOT is a model for learning what we are calling "neural script knowledge" -- representatio

Rowan Zellers 190 Dec 22, 2022
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

Swin Transformer for Object Detection This repo contains the supported code and configuration files to reproduce object detection results of Swin Tran

Swin Transformer 1.4k Dec 30, 2022
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dea

MIC-DKFZ 1.2k Jan 04, 2023
for taichi voxel-challange event

Taichi Voxel Challenge Figure: result of python3 example6.py. Please replace the image above (demo.jpg) with yours, so that other people can immediate

Liming Xu 20 Nov 26, 2022
A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory"

memory_efficient_attention.pytorch A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory" (Rabe&Staats'21). def effic

Ryuichiro Hataya 7 Dec 26, 2022
Rule Based Classification Project

Kural Tabanlı Sınıflandırma ile Potansiyel Müşteri Getirisi Hesaplama İş Problemi: Bir oyun şirketi müşterilerinin bazı özelliklerini kullanaraknseviy

Şafak 1 Jan 12, 2022
Motion and Shape Capture from Sparse Markers

MoSh++ This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface Sh

Nima Ghorbani 135 Dec 23, 2022
A fast model to compute optical flow between two input images.

DCVNet: Dilated Cost Volumes for Fast Optical Flow This repository contains our implementation of the paper: @InProceedings{jiang2021dcvnet, title={

Huaizu Jiang 8 Sep 27, 2021
Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Google Cloud Platform 792 Dec 28, 2022
Modelisation on galaxy evolution using PEGASE-HR

model_galaxy Modelisation on galaxy evolution using PEGASE-HR This is a labwork done in internship at IAP directed by Damien Le Borgne (https://github

Adrien Anthore 1 Jan 14, 2022
Tensorflow implementation for "Improved Transformer for High-Resolution GANs" (NeurIPS 2021).

HiT-GAN Official TensorFlow Implementation HiT-GAN presents a Transformer-based generator that is trained based on Generative Adversarial Networks (GA

Google Research 78 Oct 31, 2022
This is the official source code of "BiCAT: Bi-Chronological Augmentation of Transformer for Sequential Recommendation".

BiCAT This is our TensorFlow implementation for the paper: "BiCAT: Sequential Recommendation with Bidirectional Chronological Augmentation of Transfor

John 15 Dec 06, 2022
Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters

[Unofficial code-base] NeRF--: Neural Radiance Fields Without Known Camera Parameters [ Project | Paper | Official code base ] ⬅️ Thanks the original

Jianfei Guo 239 Dec 22, 2022
CLNTM - Contrastive Learning for Neural Topic Model

Contrastive Learning for Neural Topic Model This repository contains the impleme

Thong Thanh Nguyen 25 Nov 24, 2022
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology Self-Supervised Vision Transformers Learn Visual Concepts in Histopatholog

Richard Chen 95 Dec 24, 2022
wmctrl ported to Python Ctypes

work in progress wmctrl is a command that can be used to interact with an X Window manager that is compatible with the EWMH/NetWM specification. wmctr

Iyad Ahmed 22 Dec 31, 2022
Wordle Env: A Daily Word Environment for Reinforcement Learning

Wordle Env: A Daily Word Environment for Reinforcement Learning Setup Steps: git pull [email&#

2 Mar 28, 2022
Unofficial pytorch-lightning implement of Mip-NeRF

mipnerf_pl Unofficial pytorch-lightning implement of Mip-NeRF, Here are some results generated by this repository (pre-trained models are provided bel

Jianxin Huang 159 Dec 23, 2022
CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation

CSKG: The CommonSense Knowledge Graph CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation: AT

USC ISI I2 85 Dec 12, 2022