What is liGAN?

liGAN is a research codebase for training and evaluating deep generative models for de novo drug design based on 3D atomic density grids. It is based on libmolgrid and the gnina fork of caffe.

VAE paper - 2 minute talk

CVAE paper - 15 minute talk

Dependencies

numpy
pandas
scikit-image
openbabel
rdkit
molgrid
torch
protobuf
gnina version of caffe

Usage

You can use the scripts download_data.sh and download_weights.sh to download the test data and weights that were evaluated in the above papers.

The script generate.py is used to generate atomic density grids and molecular structures from a trained generative model.

Its basic usage can be seen in the scripts generate_vae.sh:

LIG_FILE=$1 # e.g. data/molport/0/102906000_8.sdf

python3 generate.py \
  --data_model_file models/data_48_0.5_molport.model \
  --gen_model_file models/vae.model \
  --gen_weights_file weights/gen_e_0.1_1_disc_x_10_0.molportFULL_rand_.0.0_gen_iter_100000.caffemodel \
  --rec_file data/molport/10gs_rec.pdb \
  --lig_file $LIG_FILE \
  --out_prefix VAE \
  --n_samples 10 \
  --fit_atoms \
  --dkoes_make_mol \
  --output_sdf \
  --output_dx \
  --gpu

And generate_cvae.sh:

REC_FILE=$1 # e.g. data/crossdock2020/PARP1_HUMAN_775_1012_0/2rd6_A_rec.pdb
LIG_FILE=$2 # e.g. data/crossdock2020/PARP1_HUMAN_775_1012_0/2rd6_A_rec_2rd6_78p_lig_tt_min.sdf

python3 generate.py \
  --data_model_file models/data_48_0.5_crossdock.model \
  --gen_model_file models/cvae.model \
  --gen_weights_file weights/lessskip_crossdocked_increased_1.lowrmsd.0_gen_iter_1500000.caffemodel \
  --rec_file $REC_FILE \
  --lig_file $LIG_FILE \
  --out_prefix CVAE \
  --n_samples 10 \
  --fit_atoms \
  --dkoes_make_mol \
  --output_sdf \
  --output_dx \
  --gpu

Both scripts can be run from the root directory of the repository.

Hi, @mattragoza I installed LiGAN successfully. But when I run pytest tests, there is a lot of errors occurred:

.............
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <torch.nn.utils.spectral_norm.SpectralNorm object at 0x7f2c7c6d6070>
module = Conv3d(18, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
do_power_iteration = True

    def compute_weight(self, module: Module, do_power_iteration: bool) -> torch.Tensor:
        # NB: If `do_power_iteration` is set, the `u` and `v` vectors are
        #     updated in power iteration **in-place**. This is very important
        #     because in `DataParallel` forward, the vectors (being buffers) are
        #     broadcast from the parallelized module to each module replica,
        #     which is a new module object created on the fly. And each replica
        #     runs its own spectral norm power iteration. So simply assigning
        #     the updated vectors to the module this function runs on will cause
        #     the update to be lost forever. And the next time the parallelized
        #     module is replicated, the same randomly initialized vectors are
        #     broadcast and used!
        #
        #     Therefore, to make the change propagate back, we rely on two
        #     important behaviors (also enforced via tests):
        #       1. `DataParallel` doesn't clone storage if the broadcast tensor
        #          is already on correct device; and it makes sure that the
        #          parallelized module is already on `device[0]`.
        #       2. If the out tensor in `out=` kwarg has correct shape, it will
        #          just fill in the values.
        #     Therefore, since the same power iteration is performed on all
        #     devices, simply updating the tensors in-place will make sure that
        #     the module replica on `device[0]` will update the _u vector on the
        #     parallized module (by shared storage).
        #
        #    However, after we update `u` and `v` in-place, we need to **clone**
        #    them before using them to normalize the weight. This is to support
        #    backproping through two forward passes, e.g., the common pattern in
        #    GAN training: loss = D(real) - D(fake). Otherwise, engine will
        #    complain that variables needed to do backward for the first forward
        #    (i.e., the `u` and `v` vectors) are changed in the second forward.
        weight = getattr(module, self.name + '_orig')
        u = getattr(module, self.name + '_u')
        v = getattr(module, self.name + '_v')
        weight_mat = self.reshape_weight_to_matrix(weight)
    
        if do_power_iteration:
            with torch.no_grad():
                for _ in range(self.n_power_iterations):
                    # Spectral norm of weight equals to `u^T W v`, where `u` and `v`
                    # are the first left and right singular vectors.
                    # This power iteration produces approximations of `u` and `v`.
>                   v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=self.eps, out=v)
E                   RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemv(handle, op, m, n, &alpha, a, lda, x, incx, &beta, y, incy)`

../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py:84: RuntimeError
----------------------------- Captured stdout setup ------------------------------
Loading data
Initializing generative model and optimizer
Initializing prior model and optimizer
Initializing atom fitter and bond adder
------------------------------ Captured stdout call ------------------------------
Saving generative model state to tests/output/TEST_VAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_VAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_VAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_VAE2_iter_0.prior_solver_state
Writing training metrics to tests/output/TEST_VAE2.train_metrics
Saving generative model state to tests/output/TEST_VAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_VAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_VAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_VAE2_iter_0.prior_solver_state
------------------------------ Captured stderr call ------------------------------
==============================
*** Open Babel Warning  in PerceiveBondOrders
  Failed to kekulize aromatic bonds in OBMol::PerceiveBondOrders (title is data/crossdock2020/1A02_HUMAN_25_199_pep_0/1eez_A_rec.pdb)

_____________ TestGenerativeSolver.test_solver_train_and_test[CVAE2] _____________

self = <test_training.TestGenerativeSolver object at 0x7f2d6bd5b970>
solver = CVAE2Solver(
  (gen_model): CVAE2(
    (input_encoder): GridEncoder(
      (level0): Conv3DBlock(
        (0): Conv3DR...tures=96, out_features=128, bias=True)
      (3): LeakyReLU(negative_slope=0.1)
    )
  )
  (loss_fn): LossFunction()
)
train_params = {'fit_interval': 0, 'max_iter': 10, 'n_test_batches': 1, 'norm_interval': 10, ...}

    def test_solver_train_and_test(self, solver, train_params):
    
        max_iter = train_params['max_iter']
        test_interval = train_params['test_interval']
        n_test_batches = train_params['n_test_batches']
    
        t0 = time.time()
>       solver.train_and_test(**train_params)

tests/test_training.py:534: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
ligan/training.py:37: in wrapper
    return method(self, *args, **kwargs)
ligan/training.py:1163: in train_and_test
    self.test_models(n_batches=n_test_batches, fit_atoms=fit_atoms)
ligan/training.py:1056: in test_models
    self.test_model(
ligan/training.py:1023: in test_model
    loss, metrics = self.gen_forward(
ligan/training.py:625: in gen_forward
    self.gen_model(
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
ligan/models.py:1130: in forward
    (means, log_stds), _ = self.input_encoder(inputs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
ligan/models.py:682: in forward
    outputs = f(inputs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
ligan/models.py:364: in forward
    outputs = f(inputs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1130: in _call_impl
    return forward_call(*input, **kwargs)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/container.py:139: in forward
    input = module(input)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/modules/module.py:1137: in _call_impl
    result = hook(self, input)
../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py:105: in __call__
    setattr(module, self.name, self.compute_weight(module, do_power_iteration=module.training))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <torch.nn.utils.spectral_norm.SpectralNorm object at 0x7f2c7c958760>
module = Conv3d(36, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
do_power_iteration = True

    def compute_weight(self, module: Module, do_power_iteration: bool) -> torch.Tensor:
        # NB: If `do_power_iteration` is set, the `u` and `v` vectors are
        #     updated in power iteration **in-place**. This is very important
        #     because in `DataParallel` forward, the vectors (being buffers) are
        #     broadcast from the parallelized module to each module replica,
        #     which is a new module object created on the fly. And each replica
        #     runs its own spectral norm power iteration. So simply assigning
        #     the updated vectors to the module this function runs on will cause
        #     the update to be lost forever. And the next time the parallelized
        #     module is replicated, the same randomly initialized vectors are
        #     broadcast and used!
        #
        #     Therefore, to make the change propagate back, we rely on two
        #     important behaviors (also enforced via tests):
        #       1. `DataParallel` doesn't clone storage if the broadcast tensor
        #          is already on correct device; and it makes sure that the
        #          parallelized module is already on `device[0]`.
        #       2. If the out tensor in `out=` kwarg has correct shape, it will
        #          just fill in the values.
        #     Therefore, since the same power iteration is performed on all
        #     devices, simply updating the tensors in-place will make sure that
        #     the module replica on `device[0]` will update the _u vector on the
        #     parallized module (by shared storage).
        #
        #    However, after we update `u` and `v` in-place, we need to **clone**
        #    them before using them to normalize the weight. This is to support
        #    backproping through two forward passes, e.g., the common pattern in
        #    GAN training: loss = D(real) - D(fake). Otherwise, engine will
        #    complain that variables needed to do backward for the first forward
        #    (i.e., the `u` and `v` vectors) are changed in the second forward.
        weight = getattr(module, self.name + '_orig')
        u = getattr(module, self.name + '_u')
        v = getattr(module, self.name + '_v')
        weight_mat = self.reshape_weight_to_matrix(weight)
    
        if do_power_iteration:
            with torch.no_grad():
                for _ in range(self.n_power_iterations):
                    # Spectral norm of weight equals to `u^T W v`, where `u` and `v`
                    # are the first left and right singular vectors.
                    # This power iteration produces approximations of `u` and `v`.
>                   v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=self.eps, out=v)
E                   RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemv(handle, op, m, n, &alpha, a, lda, x, incx, &beta, y, incy)`

../../../miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/nn/utils/spectral_norm.py:84: RuntimeError
----------------------------- Captured stdout setup ------------------------------
Loading data
Initializing generative model and optimizer
Initializing prior model and optimizer
Initializing atom fitter and bond adder
------------------------------ Captured stdout call ------------------------------
Saving generative model state to tests/output/TEST_CVAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_CVAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_CVAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_CVAE2_iter_0.prior_solver_state
Writing training metrics to tests/output/TEST_CVAE2.train_metrics
Saving generative model state to tests/output/TEST_CVAE2_iter_0.gen_model_state
Saving generative solver state to tests/output/TEST_CVAE2_iter_0.gen_solver_state
Saving prior model state to tests/output/TEST_CVAE2_iter_0.prior_model_state
Saving prior solver state to tests/output/TEST_CVAE2_iter_0.prior_solver_state
------------------------------ Captured stderr call ------------------------------
==============================
*** Open Babel Warning  in PerceiveBondOrders
  Failed to kekulize aromatic bonds in OBMol::PerceiveBondOrders (title is data/crossdock2020/1A02_HUMAN_25_199_pep_0/1eez_A_rec.pdb)

================================ warnings summary ================================
tests/test_atom_fitting.py: 21 warnings
tests/test_training.py: 112 warnings
  /NAS/lh/software/miniconda3/envs/LiGAN/lib/python3.9/site-packages/torch/cuda/memory.py:278: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.
    warnings.warn(

tests/test_atom_grids.py::TestAtomGrid::test_get_coords
  /NAS/lh/software/liGAN/new/LiGAN/./ligan/atom_grids.py:163: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
    idx = idx // dim

tests/test_models.py::test_interpolate
  /NAS/lh/software/liGAN/new/LiGAN/tests/test_models.py:50: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
    start_idx = (interp_step + batch_idxs) // n_samples

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================ short test summary info =============================
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/benzene.sdf]
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/neopentane.sdf]
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/ATP.sdf]
FAILED tests/test_bond_adding.py::TestBondAdding::test_make_ob_mol[oadc-tests/input/4fic_C_0UL.sdf]
FAILED tests/test_data.py::TestMolDataset::test_benchmark[False] - ValueError: ...
FAILED tests/test_data.py::TestMolDataset::test_benchmark[True] - AssertionErro...
FAILED tests/test_models.py::TestConv3DReLU::test_forward_cuda[Conv3DReLU] - Ru...
FAILED tests/test_models.py::TestConv3DReLU::test_forward_cuda[TConv3DReLU] - R...
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-c-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-c-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-c-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-r-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-r-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-r-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-d-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-d-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[Conv3DBlock-d-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-c-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-c-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-c-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-r-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-r-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-r-4]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-d-1]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-d-2]
FAILED tests/test_models.py::TestConv3DBlock::test_forward_cuda[TConv3DBlock-d-4]
FAILED tests/test_models.py::TestGridEncoder::test_enc1_forward - RuntimeError:...
FAILED tests/test_models.py::TestGridEncoder::test_enc1_backward0 - RuntimeErro...
FAILED tests/test_models.py::TestGridEncoder::test_enc1_backward1 - RuntimeErro...
FAILED tests/test_models.py::TestGridEncoder::test_enc2_forward - RuntimeError:...
FAILED tests/test_models.py::TestGridEncoder::test_enc2_backward0 - RuntimeErro...
FAILED tests/test_models.py::TestGridEncoder::test_enc2_backward1 - RuntimeErro...
FAILED tests/test_models.py::TestGridDecoder::test_forward - RuntimeError: CUDA...
FAILED tests/test_models.py::TestGridDecoder::test_backward0 - RuntimeError: CU...
FAILED tests/test_models.py::TestGridDecoder::test_backward1 - RuntimeError: CU...
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_poster[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_forward_prior[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster0[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_poster1[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior0[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[AE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[AE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_backward_prior1[CVAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[AE-0-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[AE-1-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CE-0-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CE-1-c-0] - ...
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[GAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[GAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CGAN-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CGAN-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[VAE2-1-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE2-0-c-0]
FAILED tests/test_models.py::TestGridGenerator::test_gen_benchmark[CVAE2-1-c-0]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster2[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_poster2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior2[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_forward_prior2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_real[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_forward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster2[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_poster2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_backward_prior2[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_real[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_backward_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_poster[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_gen_step_prior[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_real[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_poster[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_poster[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_disc_step_prior[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[AE] - Ru...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CE] - Ru...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[VAE] - R...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CVAE] - ...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[GAN] - R...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CGAN] - ...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[VAE2] - ...
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_state[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_disc[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_gen_fit[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_test_models[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_gen[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_disc[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_models_noup[CVAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[AE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[VAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CVAE]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[GAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[VAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CVAEGAN]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[VAE2]
FAILED tests/test_training.py::TestGenerativeSolver::test_solver_train_and_test[CVAE2]
ERROR tests/test_data.py::TestAtomGridData::test_data_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_find_real_mol[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_find_real_mol[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_split[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_split[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_no_transform - ValueError...
ERROR tests/test_data.py::TestAtomGridData::test_data_rand_rotate - ValueError:...
ERROR tests/test_data.py::TestAtomGridData::test_data_rand_translate - ValueErr...
ERROR tests/test_data.py::TestAtomGridData::test_data_diff_cond_transform - Val...
ERROR tests/test_data.py::TestAtomGridData::test_data_consecutive - ValueError:...
ERROR tests/test_data.py::TestAtomGridData::test_data_benchmark[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_data.py::TestAtomGridData::test_data_benchmark[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generator_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generator_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward2[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_gen_forward2[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generate[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types]
ERROR tests/test_generating.py::TestGenerator::test_generate[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types]
====== 316 failed, 599 passed, 135 warnings, 23 errors in 210.03s (0:03:30) ======

I don't know if it's a cuda issue. Thanks!

model cannot be trained

When I attempted to train the code with train.py and the provided simple example it2_tt_0_lowrmsd_valid_mols_test0_1000.types and crossdock folder, the model froze in the dataloader next_batch part.

Looking forward to your help!

opened by yuanqidu 31

how to set the input for simple_fit.py and test_dkoes_simple_fit.py

Hi, I am trying to use simple_fit.py and test_dkoes_simple_fit.py to obtain a molecules from a grid. I tried the following setting for the input path of sdf file: --for simple_fit.py, I used the exact same code and input a sdf file. if name == 'main':
results = []

print('Globbing input files')
files = glob.glob('/home.local/Level_admin/yang/liGAN/1b3g_ligand.sdf')
print (files)

print('Starting to fit molecules')
for (i,fname) in enumerate(files):
    print (i,fname)
    try:
        start = time.time()
        struct, fittime, loss, fixes, rmsd = fitmol(fname,25)
        mol,misses = make_mol(struct)
        mol = pybel.Molecule(mol)
        
        totaltime = time.time()-start
        ligname = os.path.split(fname)[1]    

        mol.write('sdf','output/fit_%s'%ligname,overwrite=True)
        print('{}/{}'.format(i+1, len(files)))        
    except Exception as e:
        print("Failed",fname,e)


results = pd.DataFrame(results,columns=('lig','loss','fixes','fittime','totaltime','misses','rmsd'))
results.to_csv('cntfixes.csv')

sns.boxplot(data=results,x='misses',y='loss')
plt.savefig('loss_by_misses_box.png')

plt.hist(results.loss,bins=np.logspace(-6,1,8))
plt.gca().set_xscale('log')
plt.savefig('loss_hist.png')

print('Low loss but nonzero misses, sorted by misses:')
print(results[(results.loss < 0.1) & (results.misses > 0)].sort_values(by='misses'))

print('Overall average loss:')
print(np.mean(results.loss))

plt.hist(results.fittime)
plt.savefig('fit_time_hist.png')

print('Average fit time and total time')
print(np.mean(results.fittime))
print(np.mean(results.totaltime))

print('Undefined RMSD sorted by loss:')
print(results[np.isinf(results.rmsd)].sort_values(by='loss'))

print('All results sorted by loss:')
print(results.sort_values(by='loss'))

and got the following error: Failed /home.local/Level_admin/yang/liGAN/1b3g_ligand.sdf Invalid input dimensions in forward of Coords2Grid

opened by yangxiufengsia 8

Aborted (core dumped) when generate molecules

Hello, when I use generate.py config/generate.config. It appears a problem as follows, and i couldn't locate the problem. Do you have some advices?

Setting random seed to 0
Loading data
Initializing generative model
Loading generative model state
Initializing atom fitter
Initializing bond adder
Initializing output writer
Empty DataFrame
Columns: []
Index: []
Starting to generate grids
0 0 0 0
Calling generator forward
  prior = 0
  stage2 = False
Getting next batch of data
Aborted (core dumped)

opened by Matheo-Chang 7

what is your final KL loss value in your liGAN paper?

Hi

I obtained over 90% valid percentage of posterior sampling of liGAN, but I got 0% valid percentage from prior sampling of liGAN. My final KL loss is around 3, which i think it is already small. I want to ask what is your final KL loss in you liGAN paper? since this loss is not reported in your results. Thank you!

opened by yangxiufengsia 6
where is the output for generated molecules and matrix?

I just installed ligan, and ran python3 generate.py config/generate.config

everything is fine. I think I finished the run, now I am wondering where is the output files with generated molecules? do we have some post-analysis scripts available? like evaluation matrix and score for the generated molecules? Thanks,

opened by sll513 4

Error when run cmake of libmogrid

Hi, when I run cmake of libmogrid, there is an error as below:

cmake .. \
	-DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX \
	-DOPENBABEL3_INCLUDE_DIR=$CONDA_PREFIX/include/openbabel3 \
	-DOPENBABEL3_LIBRARIES=$CONDA_PREFIX/lib/libopenbabel.so \
	-DZLIB_LIBRARY=$CONDA_PREFIX/lib/libz.so

CMake Error at /NAS/lh/software/miniconda3/envs/LiGAN/share/cmake-3.24/Modules/CMakeDetermineCXXCompiler.cmake:48 (message):
  Could not find compiler set in environment variable CXX:

  /NAS/lh/software/miniconda3/bin/x86_64-conda-linux-gnu-c++.

Call Stack (most recent call first):
  CMakeLists.txt:2 (project)


CMake Error: CMAKE_CXX_COMPILER not set, after EnableLanguage
CMake Error: CMAKE_CUDA_COMPILER not set, after EnableLanguage
-- Configuring incomplete, errors occurred!
See also "/NAS/lh/software/liGAN/new/libmolgrid/build/CMakeFiles/CMakeOutput.log".

I don't know how to solve it. Thanks!

opened by lh12565 3

tests ValueError: File does not exist: data/test_pockets/xxx

ERROR tests/test_generating.py::TestGenerator::test_generator_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types] - ValueError: Could not open ... ERROR tests/test_generating.py::TestGenerator::test_generator_init[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types] - ValueError: Could not open f... ERROR tests/test_generating.py::TestGenerator::test_gen_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types] - ValueError: Could not open fil... ERROR tests/test_generating.py::TestGenerator::test_gen_forward[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types] - ValueError: Could not open file... ERROR tests/test_generating.py::TestGenerator::test_gen_forward2[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types] - ValueError: Could not open fi... ERROR tests/test_generating.py::TestGenerator::test_gen_forward2[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types] - ValueError: Could not open fil... ERROR tests/test_generating.py::TestGenerator::test_generate[data/test_pockets/AROK_MYCTU_1_176_0/fixed_input_1zyu_A_rec_mutants.types] - ValueError: Could not open file d... ERROR tests/test_generating.py::TestGenerator::test_generate[data/test_pockets/AROK_MYCTU_1_176_0/fixed_cond_1zyu_A_rec_mutants.types] - ValueError: Could not open file da...

opened by zyx5256 3
How to use PDB of Alphafold2 to drug discovery?

Hi @mattragoza I want to use pdb of alphafold2 to find molecules. Do I only need to change two parameters: data_root and data_file in config/generate.config? Is the 'crossdock2020' the default reference files? for example:

data_root: data/ data_file: data/alphafold.pdb

Thanks!

opened by lh12565 3

Conflict between RDKIT and conda-molgrid

Hi, I try to install molgrid using conda-molgrid. When I finished installing molgrid, there was an error installing RDKit:

conda install -c conda-forge rdkit

Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: /
Found conflicts! Looking for incompatible packages.
This can take several minutes.  Press CTRL-C to abort.                           | failed

I try to install rdkit from source, but there is still an error:

make[1]: *** [Code/GraphMol/CMakeFiles/graphmoltestChirality.dir/all] Error 2
[ 61%] Linking CXX static library libRDKitDescriptors_static.a
[ 61%] Built target Descriptors_static
[ 61%] Linking CXX executable graphmolMolOpsTest
/NAS/lh/software/miniconda3/envs/molgrid/bin/../lib/gcc/x86_64-conda-linux-gnu/9.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: warning: libboost_iostreams-mt.so.1.53.0, needed by /usr/local/lib/libmaeparser.so, not found (try using -rpath or -rpath-link)
/NAS/lh/software/miniconda3/envs/molgrid/bin/../lib/gcc/x86_64-conda-linux-gnu/9.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: warning: libboost_regex-mt.so.1.53.0, needed by /usr/local/lib/libmaeparser.so, not found (try using -rpath or -rpath-link)
/NAS/lh/software/miniconda3/envs/molgrid/bin/../lib/gcc/x86_64-conda-linux-gnu/9.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: ../../lib/libRDKitFileParsers.so.1.2022.09.1pre: undefined reference to `schrodinger::mae::Block::getIndexedBlock(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)'
/NAS/lh/software/miniconda3/envs/molgrid/bin/../lib/gcc/x86_64-conda-linux-gnu/9.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: ../../lib/libRDKitDepictor.so.1.2022.09.1pre: undefined reference to `sketcherMinimizer::setTemplateFileDir(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)'
/NAS/lh/software/miniconda3/envs/molgrid/bin/../lib/gcc/x86_64-conda-linux-gnu/9.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: ../../lib/libRDKitFileParsers.so.1.2022.09.1pre: undefined reference to `schrodinger::mae::Reader::next(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)'
collect2: error: ld returned 1 exit status
make[2]: *** [Code/GraphMol/graphmolMolOpsTest] Error 1
make[1]: *** [Code/GraphMol/CMakeFiles/graphmolMolOpsTest.dir/all] Error 2
make: *** [all] Error 2

"Aborted (core dumped)" error using tutorial for Generating molecules

I tried to follow the tutorial for generating molecules: python3 generate.py config/generate.config I got the "Aborted (core dumped)" error after the output stopped at "Getting next batch of data" for a long while: any thought? is this due to I donot have enough memory (I have 160GB for now)

opened by sll513 3
Can not access to https://bits.csb.pitt.edu/files/ to download those files

Excuse me, there are some problems happend when I was attempt to use sh download_weights.sh to download the model`s weights. The web(https://bits.csb.pitt.edu) appears to be out of service resulting in the inability to download the required files. Would you please check it out ? Thanks !

opened by Hovi123123 2
Get killed when running generate.py

Hi,

I found that the code will be killed when running genreate.py

I debugged the code and found that the code is killed in line 330 of atom_types.py file.

Could you please help me solve this problem?

Thank you very much.

opened by Layne-Huang 2
Get Stuck when runing generate.py

Hi, Thanks for your amazing work and code! I followed the instructions on the github page and try to run: python3 generate.py config/generate.config However, it stuck at here for a long time How long does it take to finish the generation procedure typically? Thanks!

opened by zaixizhang 2

Errors when run pytest tests

opened by lh12565 12

change configuration file for prior (no lig) sampling

I found that the default generate.config file provided is for posterior sampling (with ligand) https://github.com/mattragoza/LiGAN/blob/master/config/generate.config

What parameter(s) I need to change for prior (no lig) sampling as input file I need to provide has ligand information? "prior"? "post_factor"? anything else? Thanks,

a sample prior generate.config file is appreciated

opened by sll513 1
AttributeError: type object 'object' has no attribute 'dtype'

Excuse me, when I use the command python3 generate.py config/generate.config. It appears a problem as follows: Traceback (most recent call last): File "generate.py", line 48, in main(sys.argv[1:]) File "generate.py", line 41, in main debug=args.debug, File "/da1/home/changhaoyu/liGAN/liGAN/generating.py", line 98, in init **output_kws, File "/da1/home/changhaoyu/liGAN/liGAN/generating.py", line 671, in init self.metrics = pd.DataFrame(columns=columns).set_index(columns) File "/home/changhaoyu/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/core/frame.py", line 392, in init mgr = init_dict(data, index, columns, dtype=dtype) File "/home/changhaoyu/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 196, in init_dict nan_dtype) File "/home/changhaoyu/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/core/dtypes/cast.py", line 1175, in construct_1d_arraylike_from_scalar dtype = dtype.dtype AttributeError: type object 'object' has no attribute 'dtype' How can I solve it?

Originally posted by @Matheo-Chang in https://github.com/mattragoza/LiGAN/issues/36#issuecomment-1178787379

opened by mattragoza 9

Deep generative models of 3D grids for structure-based drug discovery

Related tags

Overview

What is liGAN?

Dependencies

Usage

Comments

Owner

Matt Ragoza

OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

A Python framework for developing parallelized Computational Fluid Dynamics software to solve the hyperbolic 2D Euler equations on distributed, multi-block structured grids.

This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

The code for SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network.

This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

Bayesian Image Reconstruction using Deep Generative Models

Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

Finding an Unsupervised Image Segmenter in each of your Deep Generative Models

DeepCAD: A Deep Generative Network for Computer-Aided Design Models

TAug :: Time Series Data Augmentation using Deep Generative Models

A method that utilized Generative Adversarial Network (GAN) to interpret the black-box deep image classifier models by PyTorch.

MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Generative Models for Graph-Based Protein Design

Generative Autoregressive, Normalized Flows, VAEs, Score-based models (GANVAS)