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gauche

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A Library for Gaussian Processes in Chemistry

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**GAUCHE** is a collaborative, open-source software library that aims to make state-of-the-art
probabilistic modelling and black-box optimisation techniques more easily accessible to scientific
experts in chemistry, materials science and beyond. We provide 30+ bespoke kernels for molecules, chemical reactions and proteins and illustrate how they can be used for Gaussian processes and Bayesian optimisation in 10+ easy-to-adapt tutorial notebooks.

[Overview](#overview) | [Getting Started](#getting-started) | [Documentation](https://leojklarner.github.io/gauche/) | [Paper (NeurIPS 2023)](https://arxiv.org/abs/2212.04450)

## What's New?

* GAUCHE has been accepted to the [NeurIPS 2023 Main Track](https://neurips.cc/virtual/2023/poster/70081)! More details forthcoming!
* Check out our new [Molecular Preference Learning](https://github.com/leojklarner/gauche/blob/main/notebooks/Molecular%20Preference%20Learning.ipynb) and [Preferential Bayesian Optimisation](https://github.com/leojklarner/gauche/blob/main/notebooks/Preferential%20Bayesian%20Optimisation.ipynb) notebooks that show how you can use GAUCHE to learn the latent utility function of a human medicinal chemist from pairwise preference feedback!
* Check out our new [Sparse GP Regression for Big Molecular Data](https://github.com/leojklarner/gauche/blob/main/notebooks/Sparse%20GP%20Regression%20for%20Big%20Molecular%20Data.ipynb) notebook that shows how you can scale molecular GPs to thousands of data points with sparse inducing point kernels!

## Overview

General-purpose Gaussian process (GP) and Bayesian optimisation (BO) libraries do not cater for molecular representations. Likewise, general-purpose molecular machine learning libraries do not consider GPs and BO. To bridge this gap, GAUCHE provides a modular, robust and easy-to-use framework of 30+ parallelisable and batch-GP-compatible implementations of string, fingerprint and graph kernels that operate on a range of widely-used molecular representations.

### Kernels

Standard GP packages typically assume continuous input spaces of low and fixed dimensionality. This makes it difficult to apply them to common molecular representations: molecular graphs are discrete objects, SMILES strings vary in length and topological fingerprints tend to be high-dimensional and sparse. To bridge this gap, GAUCHE provides:

* **Fingerprint Kernels** that measure the similarity between bit/count vectors of descriptor by examining the degree to which their elements overlap.
* **String Kernels** that measure the similarity between strings by examining the degree to which their sub-strings overlap.
* **Graph Kernels** that measure between graphs by examining the degree to which certain substructural motifs overlap.

### Representations

GAUCHE supports any representation that is based on bit/count vectors, strings or graphs. For rapid prototyping and benchmarking, we also provide a range of standard featurisation techniques for molecules, chemical reactions and proteins:
 
<table>
<thead>
  <tr>
    <th>Domain</th>
    <th>Representation</th>
  </tr>
</thead>
<tbody>
  <tr>
    <td rowspan="1">Molecules</td>
    <td>ECFP Fingerprints [1], rdkit Fragments, Fragprints, Molecular Graphs [2], SMILES [3], SELFIES [4] </td>
  </tr>
  <tr>
    <td rowspan="1">Chemical Reactions</td>
    <td>One-Hot Encoding, Data-Driven Reaction Fingerprints [5], Differential Reaction Fingerprints [6], Reaction SMARTS</td>
  </tr>
  <tr>
    <td rowspan="21">Proteins</td>
    <td>Sequences, Graphs [7]</td>
  </tr>
</tbody>
</table>

### Extensions

If there are any specific kernels or representations that you would like to see included in GAUCHE, feel free to submit an issue or pull request.

## Getting Started

The easiest way to install Gauche is via pip.

```bash
pip install gauche
```

As not all users will need the full functionality of the package, we provide a range of installation options:

* `pip install gauche` - installs the core functionality of GAUCHE (kernels, representations, data loaders, etc.) and should cover a wide range of use cases.
* `pip install gauche[rxn]` - additionally installs the [rxnfp](https://github.com/rxn4chemistry/rxnfp) and [drfp](https://github.com/reymond-group/drfp) fingerprints that can be used to represent chemical reactions.
* `pip install gauche[graphs]` - installs all dependencies for graph kernels and representations.

If you aren't sure which installation option is right for you, you can simply install all of them with `pip install gauche[all]`.

---

### Tutorial Notebooks

The best way to get started with GAUCHE is to check out our [tutorial notebooks](https://leojklarner.github.io/gauche/). These notebooks provide a step-by-step introduction to the core functionality of GAUCHE and illustrate how it can be used to solve a range of common problems in molecular property prediction and optimisation. To install gauche in the colab environment run:

`pip install gauche`

|   |   |
|---|---|
| [Loading and Featurising Molecules](https://leojklarner.github.io/gauche/notebooks/loading_and_featurising_molecules.html)  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Loading%20and%20Featurising%20Molecules.ipynb)   |
| [GP Regression on Molecules](https://leojklarner.github.io/gauche/notebooks/gp_regression_on_molecules.html)  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/GP%20Regression%20on%20Molecules.ipynb)   |
| [Bayesian Optimisation Over Molecules](https://leojklarner.github.io/gauche/notebooks/bayesian_optimisation_over_molecules.html)  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Bayesian%20Optimisation%20Over%20Molecules.ipynb)   |
| [Multioutput Gaussian Processes for Multitask Learning](https://leojklarner.github.io/gauche/notebooks/multitask_gp_regression_on_molecules.html)  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Multitask%20GP%20Regression%20on%20Molecules.ipynb)   |
| [Training GPs on Graphs](https://leojklarner.github.io/gauche/notebooks/training_gps_on_graphs.html)  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Training%20GPs%20on%20Graphs.ipynb)   |
| [Sparse GP Regression for Big Molecular Data](https://leojklarner.github.io/gauche/notebooks/sparse_gp_regression_for_big_molecular_data.html)  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Sparse%20GP%20Regression%20for%20Big%20Molecular%20Data.ipynb)   |
|[Molecular Preference Learning](https://github.com/leojklarner/gauche/blob/main/notebooks/Molecular%20Preference%20Learning.ipynb)|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Molecular%20Preference%20Learning.ipynb) |
|[Preferential Bayesian Optimisation](https://github.com/leojklarner/gauche/blob/main/notebooks/Preferential%20Bayesian%20Optimisation.ipynb)|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Preferential%20Bayesian%20Optimisation.ipynb) |
|[Training Bayesian GNNs on Molecules](https://leojklarner.github.io/gauche/notebooks/bayesian_gnn_on_molecules.html)|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Bayesian_GNN_on_Molecules.ipynb) |

---

### Example Usage: Loading and Featurising Molecules

GAUCHE provides a range of helper functions for loading and preprocessing datasets for molecular property and reaction yield prediction and optimisation tasks. For more detail, check out our [Loading and Featurising Molecules Tutorial](https://leojklarner.github.io/gauche/notebooks/loading_and_featurising_molecules.html) and the corresponding section in the [Docs](https://leojklarner.github.io/gauche/notebooks/loading_and_featurising_molecules.html).

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/Loading%20and%20Featurising%20Molecules.ipynb)

```python	
from gauche.dataloader import MolPropLoader

loader = MolPropLoader()

# load one of the included benchmarks
loader.load_benchmark("Photoswitch")

# or a custom dataset
loader.read_csv(path="data.csv", smiles_column="smiles", label_column="y")

# and quickly featurise the provided molecules
loader.featurize('ecfp_fragprints')
X, y = loader.features, loader.labels
```

### Example Usage: GP Regression on Molecules

Fitting a GP model with a kernel from GAUCHE and using it to predict the properties of new molecules is as easy as this. For more detail, check out our [GP Regression on Molecules Tutorial](https://leojklarner.github.io/gauche/notebooks/gp_regression_on_molecules.html) and the corresponding section in the [Docs](https://leojklarner.github.io/gauche/modules/dataloader.html).

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/leojklarner/gauche/blob/main/notebooks/GP%20Regression%20on%20Molecules.ipynb)

```python
import gpytorch
from botorch import fit_gpytorch_model
from gauche.kernels.fingerprint_kernels.tanimoto_kernel import TanimotoKernel

# define GP model with Tanimoto kernel
class TanimotoGP(gpytorch.models.ExactGP):
  def __init__(self, train_x, train_y, likelihood):
    super(TanimotoGP, self).__init__(train_x, train_y, likelihood)
    self.mean_module = gpytorch.means.ConstantMean()
    self.covar_module = gpytorch.kernels.ScaleKernel(TanimotoKernel())
  
  def forward(self, x):
    mean_x = self.mean_module(x)
    covar_x = self.covar_module(x)
    return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)

# initialise GP likelihood, model and 
# marginal log likelihood objective
likelihood = gpytorch.likelihoods.GaussianLikelihood()
model = TanimotoGP(X_train, y_train, likelihood)
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)

# fit GP with BoTorch in order to use
# the LBFGS-B optimiser (recommended)
fit_gpytorch_model(mll)

# use the trained GP to get predictions and 
# uncertainty estimates for new molecules
model.eval()
likelihood.eval()
preds = model(X_test)
pred_means, pred_vars = preds.mean, preds.variance
```

## Citing GAUCHE

If GAUCHE is useful for your work please consider citing the following paper:

```bibtex
@article{griffiths2024gauche,
  title={{GAUCHE}: A library for {Gaussian} processes in chemistry},
  author={Griffiths, Ryan-Rhys and Klarner, Leo and Moss, Henry and Ravuri, Aditya and Truong, Sang and Du, Yuanqi and Stanton, Samuel and Tom, Gary and Rankovic, Bojana and Jamasb, Arian and others},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

```

## References

[1] Rogers, D. and Hahn, M., 2010. [Extended-connectivity fingerprints](https://pubs.acs.org/doi/abs/10.1021/ci100050t). Journal of Chemical Information and Modeling, 50(5), pp.742-754.

[2] Fey, M., & Lenssen, J. E. (2019). [Fast graph representation learning with PyTorch Geometric](https://arxiv.org/abs/1903.02428). arXiv preprint arXiv:1903.02428.

[3] Weininger, D., 1988. [SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules.](https://pubs.acs.org/doi/pdf/10.1021/ci00057a005) Journal of Chemical Information and Computer Sciences, 28(1), pp.31-36.

[4] Krenn, M., Häse, F., Nigam, A., Friederich, P. and Aspuru-Guzik, A., 2020. [Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation](https://iopscience.iop.org/article/10.1088/2632-2153/aba947/meta). Machine Learning: Science and Technology, 1(4), p.045024.

[5] Probst, D., Schwaller, P. and Reymond, J.L., 2022. [Reaction classification and yield prediction using the differential reaction fingerprint DRFP](https://pubs.rsc.org/en/content/articlehtml/2022/dd/d1dd00006c). Digital Discovery, 1(2), pp.91-97.

[6] Schwaller, P., Probst, D., Vaucher, A.C., Nair, V.H., Kreutter, D., Laino, T. and Reymond, J.L., 2021. [Mapping the space of chemical reactions using attention-based neural networks](https://www.nature.com/articles/s42256-020-00284-w). Nature Machine Intelligence, 3(2), pp.144-152.

[7] Jamasb, A., Viñas Torné, R., Ma, E., Du, Y., Harris, C., Huang, K., Hall, D., Lió, P. and Blundell, T., 2022. [Graphein-a Python library for geometric deep learning and network analysis on biomolecular structures and interaction networks](https://proceedings.neurips.cc/paper_files/paper/2022/hash/ade039c1db0391106a3375bd2feb310a-Abstract-Conference.html). Advances in Neural Information Processing Systems, 35, pp.27153-27167.

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