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https://github.com/shihchengli/baseprop

Baseline models for molecular property prediction
https://github.com/shihchengli/baseprop

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Baseline models for molecular property prediction

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# Baseprop

Baseline models for molecular property prediction. Currently, this package includes the GNN from “[Semi-supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)” and traditional MLP as model architectures. 



# Installing Baseprop

```python

conda create -n baseprop python=3.11

conda activate baseprop

git clone https://github.com/shihchengli/baseprop.git

cd baseprop

pip install -e .

```



# Using Baseprop

Baseprop can used either by CLI or as a Python module.



## CLI

Four different types of CLI are supported: `train`, `predict`, `hpopt`, and `nestedCV`. Below are some examples and descriptions of the arguments for each job. More details about other arguments can be found in the modules under the CLI folder.

### `train`: model training

```bash

baseprop train \

--data-path tests/data/freesolv.csv \

--task-type regression \

--output-dir train_example \

--smiles-columns smiles \

--target-columns freesolv \

--save-smiles-splits \

--split-type cv \

--num-folds 5 \

--molecule-featurizers morgan_binary

```

* `--data-path`: Path to an input CSV file containing SMILES and the associated target values.

* `--task-type`: Type of dataset. This determines the default loss function used during training. Defaults to regression.

* `--output-dir`: Directory where training outputs will be saved. Defaults to 'CURRENT_DIRECTORY/chemprop_training/STEM_OF_INPUT/TIME_STAMP'.

* `--smiles-columns`: The column names in the input CSV containing SMILES strings.

* `--target-columns`: Name of the columns containing target values.

* `--save-smiles-splits`: Save smiles for each train/val/test splits for prediction convenience later.

* `--split-type`: Method of splitting the data into train/val/test (case insensitive).

* `--num-folds`: Number of folds when performing cross validation.

* `--molecule-featurizers`: Method(s) of generating molecule features to use as extra descriptors.



### `predict`: model inference

```bash

baseprop predict \

--test-path freesolv.csv \

--preds-path train_example/fold_0/test_preds.csv \

--target-columns freesolv \

--model-path train_example/fold_0/model_0/best.pt \

--molecule-featurizers morgan_binary

```

* `--test-path`: Path to an input CSV file containing SMILES.

* `--preds-path`: Path to which predictions will be saved.

* `--model-path`: Location of checkpoint(s) or model file(s) to use for prediction.



### `hpopt`: hyperparameters optimization

```bash

baseprop cli \

--data-path freesolv.csv \

--task-type regression \

--smiles-columns smiles \

--target-columns freesolv \

--raytune-num-samples 5 \

--raytune-temp-dir $RAY_TEMP_DIR \

--raytune-num-cpus 40 \

--raytune-num-gpus 2 \

--raytune-max-concurrent-trials 2 \

--search-parameter-keywords depth ffn_num_layers hidden_channels ffn_hidden_dim dropout lr batch_size \

--hyperopt-random-state-seed 42 \

--hpopt-save-dir $results_dir

```

* `--raytune-num-samples`: Passed directly to Ray Tune TuneConfig to control number of trials to run.

* `--raytune-temp-dir`: Passed directly to Ray Tune init to control temporary director.

* `--raytune-num-cpus`: Passed directly to Ray Tune init to control number of CPUs to use.

* `--raytune-num-gpus`: Passed directly to Ray Tune init to control number of GPUs to use.

* `--raytune-max-concurrent-trials`: Passed directly to Ray Tune TuneConfig to control maximum concurrent trials.

* `--search-parameter-keywords`: The model parameters over which to search for an optimal hyperparameter configuration.

* `--hyperopt-random-state-seed`: Passed directly to HyperOptSearch to control random state seed.

* `--hpopt-save-dir`: Directory to save the hyperparameter optimization results.



### `nestedCV`: nested cross-validation (CV)

```bash

baseprop nestedCv \

--data-path freesolv.csv \

--task-type regression \

--smiles-columns smiles \

--target-columns freesolv \

--raytune-num-samples 20 \

--raytune-temp-dir $RAY_TEMP_DIR \

--raytune-num-cpus 40 \

--raytune-num-gpus 2 \

--raytune-max-concurrent-trials 2 \

--search-parameter-keywords depth ffn_num_layers hidden_channels ffn_hidden_dim dropout lr batch_size \

--hyperopt-random-state-seed 42 \

--hpopt-save-dir $results_dir \

--split-type cv \

--num-folds 5

```

**Note**: The number of CV folds in the outer and inner loops is the same as `--num-folds`.



# Python Module

Baseprop can also be used as a Python module to run baseline benchmarks or more complicated jobs. For example, there is a [notebook](https://github.com/shihchengli/baseprop/blob/main/examples/active_learning.ipynb) for active learning under the examples folder.



# Relationship to Chemprop

Baseprop is very similar to Chemprop, which uses a directed message passing (D-MPNN) neural network as the GNN model for chemical property prediction. Here, the GNN from “[Semi-supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)” is used as the baseline in the package. Additionally, the traditional MLP method can also be used with `--features-only` and `--molecule-featurizers` to only utilize fingerprints as input for the MLP. I ([@shihchengli](https://github.com/shihchengli)) am also a developer of Chemprop, so I adopted most of the code from Chemprop. This approach ensures a fair comparison between the model performance benchmark with D-MPNN and the other baselines implemented in this package.