Interface

Train

unimol_tools.train.py trains a Uni-Mol model.

class unimol_tools.train.MolTrain(task='classification', data_type='molecule', epochs=10, learning_rate=0.0001, batch_size=16, early_stopping=5, metrics='none', split='random', split_group_col='scaffold', kfold=5, save_path='./exp', remove_hs=False, smiles_col='SMILES', target_cols=None, target_col_prefix='TARGET', target_anomaly_check='filter', smiles_check='filter', target_normalize='auto', max_norm=5.0, use_cuda=True, use_amp=True, freeze_layers=None, freeze_layers_reversed=False, load_model_dir=None, model_name='unimolv1', model_size='84m', **params)

A MolTrain class is responsible for interface of training process of molecular data.

__init__(task='classification', data_type='molecule', epochs=10, learning_rate=0.0001, batch_size=16, early_stopping=5, metrics='none', split='random', split_group_col='scaffold', kfold=5, save_path='./exp', remove_hs=False, smiles_col='SMILES', target_cols=None, target_col_prefix='TARGET', target_anomaly_check='filter', smiles_check='filter', target_normalize='auto', max_norm=5.0, use_cuda=True, use_amp=True, freeze_layers=None, freeze_layers_reversed=False, load_model_dir=None, model_name='unimolv1', model_size='84m', **params)

Initialize a MolTrain class.

Parameters:

• task – str, default=’classification’, currently support [`]classification`, regression, multiclass, multilabel_classification, multilabel_regression.

• data_type – str, default=’molecule’, currently support molecule, oled.

• epochs – int, default=10, number of epochs to train.

• learning_rate – float, default=1e-4, learning rate of optimizer.

• batch_size – int, default=16, batch size of training.

• early_stopping – int, default=5, early stopping patience.

• metrics –

  str, default=’none’, metrics to evaluate model performance.

  currently support:

  • classification: auc, auprc, log_loss, acc, f1_score, mcc, precision, recall, cohen_kappa.

  • regression: mse, pearsonr, spearmanr, mse, r2.

  • multiclass: log_loss, acc.

  • multilabel_classification: auc, auprc, log_loss, acc, mcc.

  • multilabel_regression: mae, mse, r2.

• split –

  str, default=’random’, split method of training dataset. currently support: random, scaffold, group, stratified, select.

  • random: random split.

  • scaffold: split by scaffold.

  • group: split by group. split_group_col should be specified.

  • stratified: stratified split. split_group_col should be specified.

  • select: use split_group_col to manually select the split group. Column values of split_group_col should be range from 0 to kfold-1 to indicate the split group.

• split_group_col – str, default=’scaffold’, column name of group split.

• kfold –

  int, default=5, number of folds for k-fold cross validation.

  • 1: no split. all data will be used for training.

• save_path – str, default=’./exp’, path to save training results.

• remove_hs – bool, default=False, whether to remove hydrogens from molecules.

• smiles_col – str, default=’SMILES’, column name of SMILES.

• target_cols – list or str, default=None, column names of target values.

• target_col_prefix – str, default=’TARGET’, prefix of target column name.

• target_anomaly_check – str, default=’filter’, how to deal with anomaly target values. currently support: filter, none.

• smiles_check – str, default=’filter’, how to deal with invalid SMILES. currently support: filter, none.

• target_normalize – str, default=’auto’, how to normalize target values. ‘auto’ means we will choose the normalize strategy by automatic. currently support: auto, minmax, standard, robust, log1p, none.

• max_norm – float, default=5.0, max norm of gradient clipping.

• use_cuda – bool, default=True, whether to use GPU.

• use_amp – bool, default=True, whether to use automatic mixed precision.

• freeze_layers – str or list, frozen layers by startwith name list. [‘encoder’, ‘gbf’] will freeze all the layers whose name start with ‘encoder’ or ‘gbf’.

• freeze_layers_reversed – bool, default=False, inverse selection of frozen layers

• params – dict, default=None, other parameters.

• load_model_dir – str, default=None, path to load model for transfer learning.

• model_name – str, default=’unimolv1’, currently support unimolv1, unimolv2.

• model_size – str, default=’84m’, model size. work when model_name is unimolv2. avaliable: 84m, 164m, 310m, 570m, 1.1B.

fit(data)

Fit the model according to the given training data with multi datasource support, including SMILES csv file and custom coordinate data.

For example: custom coordinate data.

from unimol_tools import MolTrain
import numpy as np
custom_data ={'target':np.random.randint(2, size=100),
            'atoms':[['C','C','H','H','H','H'] for _ in range(100)],
            'coordinates':[np.random.randn(6,3) for _ in range(100)],
            }

clf = MolTrain()
clf.fit(custom_data)

update_and_save_config()

Update and save config file.

Predict

unimol_tools.predictor.py predict through a Uni-Mol model.

class unimol_tools.predict.MolPredict(load_model=None)

A MolPredict class is responsible for interface of predicting process of molecular data.

__init__(load_model=None)

Initialize a MolPredict class.

Parameters:

load_model – str, default=None, path of model to load.

predict(data, save_path=None, metrics='none')

Predict molecular data.

Parameters:

• data – str or pandas.DataFrame or dict of atoms and coordinates, input data for prediction. - str: path of csv file. - pandas.DataFrame: dataframe of data. - dict: dict of atoms and coordinates, e.g. {‘atoms’: [‘C’, ‘C’, ‘C’], ‘coordinates’: [[0, 0, 0], [0, 0, 1], [0, 0, 2]]}

• save_path – str, default=None, path to save predict result.

• metrics –

  str, default=’none’, metrics to evaluate model performance.

  currently support:

  • classification: auc, auprc, log_loss, acc, f1_score, mcc, precision, recall, cohen_kappa.

  • regression: mae, pearsonr, spearmanr, mse, r2.

  • multiclass: log_loss, acc.

  • multilabel_classification: auc, auprc, log_loss, acc, mcc.

  • multilabel_regression: mae, mse, r2.

Return y_pred:

numpy.ndarray, predict result.

save_predict(data, dir, prefix)

Save predict result to csv file.

Parameters:

• data – pandas.DataFrame, predict result.

• dir – str, directory to save predict result.

• prefix – str, prefix of predict result file name.

Uni-Mol representation

unimol_tools.predictor.py get the Uni-Mol representation.

class unimol_tools.predictor.MolDataset(*args: Any, **kwargs: Any)

A MolDataset class is responsible for interface of molecular dataset.

__init__(data, label=None)

class unimol_tools.predictor.UniMolRepr(data_type='molecule', remove_hs=False, model_name='unimolv1', model_size='84m', use_gpu=True)

A UniMolRepr class is responsible for interface of molecular representation by unimol

__init__(data_type='molecule', remove_hs=False, model_name='unimolv1', model_size='84m', use_gpu=True)

Initialize a UniMolRepr class.

Parameters:

• data_type – str, default=’molecule’, currently support molecule, oled.

• remove_hs – bool, default=False, whether to remove hydrogens in molecular.

• use_gpu – bool, default=True, whether to use gpu.

• model_name – str, default=’unimolv1’, currently support unimolv1, unimolv2.

• model_size – str, default=’84m’, model size of unimolv2.

get_repr(data=None, return_atomic_reprs=False)

Get molecular representation by unimol.

Parameters:

• data –

  str, dict or list, default=None, input data for unimol.

  • str: smiles string or path to a smiles file.

  • dict: custom conformers, should take atoms and coordinates as input.

  • list: list of smiles strings.

• return_atomic_reprs – bool, default=False, whether to return atomic representations.

Returns:

dict of molecular representation.