drugex.training.explorers package

Submodules

drugex.training.explorers.frag_graph_explorer module

class drugex.training.explorers.frag_graph_explorer.FragGraphExplorer(agent, env, mutate=None, crover=None, batch_size=128, epsilon=0.1, beta=0.0, n_samples=-1, optim=None, device=device(type='cuda'), use_gpus=(0,), no_multifrag_smiles=True)

Bases: FragExplorer

Explorer to optimize a graph-based fragment-using agent with the given Environment.

forward(src)

Forward pass of the agent.

Parameters:

src (torch.Tensor) – Input tensor of shape (batch_size, 80, 5).

Returns:

Output tensor of shape (batch_size, 80, 5).

Return type:

torch.Tensor

getBatchOutputs(net, src)

Outputs (frags, smiles) and loss of the agent for a batch of fragments-molecule pairs.

Parameters:

• net (torch.nn.Module) – Agent

• src (torch.Tensor) – Fragments-molecule pairs

Returns:

• frags (list) – List of fragments

• smiles (list) – List of SMILES

• loss (torch.Tensor) – Loss of the agent

sampleEncodedPairsToLoader(net, loader)

Sample new fragments-molecule pairs from a data loader.

Parameters:

• net (torch.nn.Module) – Agent

• loader (torch.utils.data.DataLoader) – Data loader for original fragments-molecule pairs

Returns:

Data loader for sampled fragments-molecule pairs

Return type:

torch.utils.data.DataLoader

sample_input(loader, is_test=False)

Sample a batch of fragments-molecule pairs from the dataset.

Parameters:

• loader (torch.utils.data.DataLoader) – Data loader for original fragments-molecule pairs

• is_test (bool) – Whether to sample from the validation set or not

Returns:

Data loader for sampled fragments-molecule pairs

Return type:

torch.utils.data.DataLoader

drugex.training.explorers.frag_sequence_explorer module

class drugex.training.explorers.frag_sequence_explorer.FragSequenceExplorer(agent, env=None, crover=None, mutate=None, batch_size=128, epsilon=0.1, beta=0.0, n_samples=-1, optim=None, device=device(type='cuda'), use_gpus=(0,), no_multifrag_smiles=True)

Bases: FragExplorer

Explorer to optimize a sequence-based fragment-using agent with the given Environment.

forward(src)

Forward pass of the agent.

Parameters:

src (torch.Tensor) – TODO: check the shape of the input tensor.

Returns:

TODO: check the shape of the input tensor.

Return type:

torch.Tensor

getBatchOutputs(net, src)

Outputs (frags, smiles) and loss of the agent for a batch of fragments-molecule pairs.

Parameters:

• net (torch.nn.Module) – Agent

• src (torch.Tensor) – Fragments-molecule pairs

Returns:

• frags (list) – List of fragments

• smiles (list) – List of SMILES

• loss (torch.Tensor) – Loss of the agent

sampleEncodedPairsToLoader(net, loader)

Sample new fragments-molecule pairs from a data loader.

Parameters:

• net (torch.nn.Module) – Agent

• loader (torch.utils.data.DataLoader) – Data loader for original fragments-molecule pairs

Returns:

Data loader for sampled fragments-molecule pairs

Return type:

torch.utils.data.DataLoader

sample_input(loader, is_test=False)

Sample a batch of fragments-molecule pairs from the dataset.

Parameters:

• loader (torch.utils.data.DataLoader) – Data loader for original fragments-molecule pairs

• is_test (bool) – Whether to sample from the validation set or not

Returns:

Data loader for sampled fragments-molecule pairs

Return type:

torch.utils.data.DataLoader

drugex.training.explorers.interfaces module

interfaces

Created by: Martin Sicho On: 01.06.22, 11:29

class drugex.training.explorers.interfaces.Explorer(agent, env, mutate=None, crover=None, no_multifrag_smiles=True, batch_size=128, epsilon=0.1, beta=0.0, n_samples=-1, device=device(type='cuda'), use_gpus=(0,))

Bases: Model, ABC

Implements the DrugEx exploration strategy for DrugEx models under the reinforcement learning framework.

attachToGPUs(gpus)

Attach the model to GPUs

Parameters:

gpus (tuple) – The GPUs to use for training.

abstract fit(train_loader, valid_loader=None, epochs=1000, monitor=None)

Train and validate the model with a given training and validation loader (see DataSet and its implementations docs to learn how to generate them).

Parameters:

• train_loader (torch.utils.data.DataLoader) – The training data loader.

• valid_loader (torch.utils.data.DataLoader) – The validation data loader.

• epochs (int, optional) – The number of epochs to train the model for.

• monitor (TrainingMonitor, optional) – A TrainingMonitor instance to monitor the training process.

• **kwargs – Additional keyword arguments to pass to the training loop.

getModel()

Returns the current state of the agent

Returns:

The current state of the agent

Return type:

torch.nn.Module

getNovelMoleculeMetrics(scores)

Get metrics for novel molecules

Parameters:

scores (pd.DataFrame) – The scores for each molecule.

Returns:

The metrics:

• valid_ratio (float): ratio of valid molecules

• unique_ratio (float): ratio of valid and unique molecules

• desired_ratio (float): ratio of valid, unique and desired molecules

• avg_amean (float): average arithmetic mean score of valid and unique molecules

• avg_gmean (float): average geometric mean score of valid and unique molecules

Return type:

dict

logPerformanceAndCompounds(epoch, metrics, scores)

Log performance of model

Parameters:

• epoch (int) – The current epoch.

• metrics (dict) – The metrics.

• scores (pd.DataFrame) – The scores for each molecule.

abstract policy_gradient(*args, **kwargs)

Policy gradient training.

saveBestState(value, epoch, it)

Save best state based on selection criteria

Parameters:

• value (float) – The value of the selection criteria.

• epoch (int) – The current epoch.

• it (int) –

class drugex.training.explorers.interfaces.FragExplorer(agent, env, mutate=None, crover=None, no_multifrag_smiles=True, batch_size=128, epsilon=0.1, beta=0.0, n_samples=-1, device=device(type='cuda'), use_gpus=(0,))

Bases: Explorer

Implements the DrugEx exploration strategy for DrugEx models under the reinforcement learning framework for fragment-based generators

fit(train_loader, valid_loader=None, epochs=1000, patience=50, criteria='desired_ratio', min_epochs=100, monitor=None)

Fit the graph explorer to the training data.

Parameters:

• train_loader (torch.utils.data.DataLoader) – Data loader for training data

• valid_loader (torch.utils.data.DataLoader) – Data loader for validation data

• epochs (int) – Number of epochs to train for

• patience (int) – Number of epochs to wait for improvement before early stopping

• criteria (str) – Criteria to use for early stopping

• min_epochs (int) – Minimum number of epochs to train for

• monitor (Monitor) – Monitor to use for logging and saving model

abstract getBatchOutputs(src, net)

Outputs (frags, smiles) and loss of the agent for a batch of fragments-molecule pairs.

policy_gradient(loader)

Policy gradient training.

Novel molecules are generated by the agent and are scored by the environment. The policy gradient is calculated using the REINFORCE algorithm and the agent is updated.

Parameters:

loader (torch.utils.data.DataLoader) – Data loader for training data

Returns:

The average loss of the agent

Return type:

float

abstract sampleEncodedPairsToLoader(net, loader)

Sample new fragments-molecule pairs from a data loader.

abstract sample_input(loader, is_test=False)

Sample a batch of fragments-molecule pairs from the training data loader.

drugex.training.explorers.sequence_explorer module

class drugex.training.explorers.sequence_explorer.SequenceExplorer(agent, env, mutate=None, crover=None, no_multifrag_smiles=True, batch_size=128, epsilon=0.1, beta=0.0, n_samples=1000, optim=None, device=device(type='cuda'), use_gpus=(0,))

Bases: Explorer

Explorer to optimize a sequence-based agent (RNN) with the given Environment.

Reference: Liu, X., Ye, K., van Vlijmen, H.W.T. et al. DrugEx v2: De Novo Design of Drug Molecule by

Pareto-based Multi-Objective Reinforcement Learning in Polypharmacology. J Cheminform (2021). https://doi.org/10.1186/s13321-019-0355-6

fit(train_loader=None, valid_loader=None, monitor=None, epochs=1000, patience=50, reload_interval=50, criteria='desired_ratio', min_epochs=100)

Fit the graph explorer to the training data.

Parameters:

• train_loader (torch.utils.data.DataLoader) – ignored, for compatibility with FragExplorer

• valid_loader (torch.utils.data.DataLoader) – ignored, for compatibility with FragExplorer

• epochs (int) – Number of epochs to train for

• patience (int) – Number of epochs to wait for improvement before early stopping

• reload_interval (int) – Every nth epoch reset the agent (and the crover) network to the best state

• criteria (str) – Criteria to use for early stopping: ‘desired_ratio’, ‘avg_amean’ or ‘avg_gmean’

• min_epochs (int) – Minimum number of epochs to train for

• monitor (Monitor) – Monitor to use for logging and saving model

forward()

Generate molecules with the given agent network

Returns:

• smiles (list) – The generated SMILES.

• seqs (torch.Tensor) – The generated encoded sequences.

policy_gradient(smiles=None, seqs=None)

Policy gradient training.

Novel molecules are scored by the environment. The policy gradient is calculated using the REINFORCE algorithm and the agent is updated.

Parameters:

• smiles (list) – The generated SMILES.

• seqs (torch.Tensor) – The generated encoded sequences.

Returns:

loss – The loss of the policy gradient.

Return type:

float

Module contents