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Tartarus

★ 84 Open GitHub ↗

A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

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# Tartarus: Practical and Realistic Benchmarks for Inverse Molecular Design

This repository contains the code and results for the paper [Tartarus, an open-source collection of benchmarks for evaluation of a generative model](https://arxiv.org/abs/2209.12487).

## Benchmarking with Tartarus

### Benchmarking with Docker

To run the Tartarus benchmark we recommend using the provided Docker container. Optionally, we also provide instructions to building and running the benchmark locally. The following directions will walk you through setup and evaluation. You will need to have Docker installed on your machine. Once you have Docker installed, you can follow these steps:

1. Write the SMILES to be evaluated to a CSV file with a column header `smiles`.

2. Pull the latest Tartarus Docker image:

``` bash
    docker pull johnwilles/tartarus:latest
```

3. Run the Docker container with the directory of your data mounted, the benchmark mode and the CSV input filename:

```bash
    docker run --rm -it -v ${LOCAL_PATH_TO_DATA}:/data johnwilles/tartarus:latest --mode ${BENCHMARK_MODE} --input_filename ${INPUT_FILENAME}
```

4. The output file will be written to the same directory by default with the filename `output.csv`.

### Installing from Source

To install Tartarus locally, we recommend using the provided Conda environment definition.

1. Clone the Tartarus repository.

```bash
    git clone git@github.com:aspuru-guzik-group/Tartarus.git
```

2. Create a Conda environment.

```bash
    conda env create -f environment.yml
```

3. Activate the tartarus Conda environment.

```bash
    conda activate tartarus
```

4. Ensure that docking task executables have the correct permissions.

```
    chmod 777 tartarus/data/qvina
    chmod 777 tartarus/data/smina
```

> **Note:** These executables are only compatible with Linux.

## Documentation

Detailed documentation can be found here: [Tartarus Docs](https://tartarus.readthedocs.io/en/latest/)

## Getting started

Below are some examples of how to load the datasets and use the fitness functions. For more details, you can also look at `example.py`.

### Datasets

All datasets are found in the [datasets](datasets/) directory. The arrows indicate the goal (&#8593; = maximization, &#8595; = minimization).

|Task | Dataset name | # of smiles | Columns in file ||||||
|---|--------------------|------------------|----|----|----|---|----|----|
| Designing OPV | `hce.csv` | 24,953 | PCEPCBM -SAS (&#8593;) | PCEPCDTBT -SAS (&#8593;) | 
| Designing emitters | `gdb13.csv` | 403,947 | Singlet-triplet gap (&#8595;) | Oscillator strength (&#8593;) | Multi-objective (&#8593;) | | ||
| Designing drugs | `docking.csv` | 152,296 | 1SYH (&#8595;) | 6Y2F (&#8595;) | 4LDE (&#8595;) | | | |
| Designing chemical reaction substrates | `reactivity.csv` | 60,828 | 	Activation energy &Delta;E&#8225; (&#8595;) | 	Reaction energy &Delta;Er (&#8595;) | &Delta;E&#8225; + &Delta;Er (&#8595;) | - &Delta;E&#8225; + &Delta;Er (&#8595;) | | |

### Designing organic photovoltaics

To use the evaluation function, load either the full xtb calculation from the `pce` module, or use the surrogate model, with pretrained weights.

```python
import pandas as pd
data = pd.read_csv('./datasets/hce.csv')   # or ./dataset/unbiased_hce.csv
smiles = data['smiles'].tolist()
smi = smiles[0]

## use full xtb calculation in hce module
from tartarus import pce
dipm, gap, lumo, combined, pce_pcbm_sas, pce_pcdtbt_sas = pce.get_properties(smi)

## use pretrained surrogate model
dipm, gap, lumo, combined = pce.get_surrogate_properties(smi)
```

### Designing Organic Emitters
Load the objective functions from the `tadf` module. All 3 fitness functions are returned for each smiles.

```python
import pandas as pd
data = pd.read_csv('./datasets/gdb13.csv')  
smiles = data['smiles'].tolist()
smi = smiles[0]

## use full xtb calculation in hce module
from tartarus import tadf
st, osc, combined = tadf.get_properties(smi)
```

### Design of drug molecule
Load the `docking` module. There are separate functions for each of the proteins, as shown below.

```python
import pandas as pd
data = pd.read_csv('./datasets/docking.csv')  
smiles = data['smiles'].tolist()
smi = smiles[0]

## Design of Protein Ligands 
from tartarus import docking
score_1syh = docking.get_1syh_score(smi)
score_6y2f = docking.get_6y2f_score(smi)
score_4lde = docking.get_4lde_score(smi)
```

### Design of Chemical Reaction Substrates
Load the `reactivity` module. All 4 fitness functions are returned for each smiles.

```python
import pandas as pd
data = pd.read_csv('./datasets/reactivity.csv')  
smiles = data['smiles'].tolist()
smi = smiles[0]

## calculating binding affinity for each protein
from tartarus import reactivity
Ea, Er, sum_Ea_Er, diff_Ea_Er = reactivity.get_properties(smi)
```

### Results
Our results for running the corresponding benchmarks can be found here: 
- Design of Protein Ligands: https://drive.google.com/file/d/1d_4mg1Eb7HrUJ2L7A8kFtld-TmPmOKlJ/view?usp=sharing
- Design of Chemical Reaction Substrates: https://drive.google.com/file/d/1fCnFxSUITg4qSlOuwFolvQPUQA31Qaii/view?usp=sharing
- Designing organic photovoltaics (photovoltaic conversion efficiency): https://drive.google.com/file/d/1w6oOBGjDC4Enh492jLQ7A3Xc1XbHXiIt/view?usp=sharing
- Designing Organic Emitters: https://drive.google.com/file/d/1l8weYg835HDGvOoRbOcHUnvLjiyQi_Ms/view?usp=sharing
- Designing organic photovoltaics (Explore): https://drive.google.com/file/d/1-J99iXfBx0_aG1BqEEXPh7q0kovBFD0L/view?usp=sharing
- Designing organic photovoltaics (Surrogate, exploit): https://drive.google.com/file/d/1EV7ST9_F4DBnQpxhd6VaaJWP5r9ygr0c/view?usp=sharing
- Designing organic photovoltaics (Exploit): https://drive.google.com/file/d/1Yh_8E3jRf6X230CvlRlPtk2qPQIkC5hB/view?usp=sharing

## Questions, problems?
Make a github issue 😄. Please be as clear and descriptive as possible. Please feel free to reach
out in person: (akshat98[AT]stanford[DOT]edu, robert[DOT]pollice[AT]gmail[DOT]com)

## License

[Apache License 2.0](https://choosealicense.com/licenses/apache-2.0/)

Tartarus

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