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https://github.com/sustainable-processes/summit
Optimising chemical reactions using machine learning
https://github.com/sustainable-processes/summit
bayesian-optimization chemistry drug-discovery machine-learning nelder-mead neural-networks optimization self-optimization snobfit tsemo
Last synced: about 2 months ago
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Optimising chemical reactions using machine learning
- Host: GitHub
- URL: https://github.com/sustainable-processes/summit
- Owner: sustainable-processes
- License: mit
- Created: 2019-07-31T15:13:13.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2023-07-23T00:57:00.000Z (11 months ago)
- Last Synced: 2024-05-05T09:43:01.367Z (about 2 months ago)
- Topics: bayesian-optimization, chemistry, drug-discovery, machine-learning, nelder-mead, neural-networks, optimization, self-optimization, snobfit, tsemo
- Language: Jupyter Notebook
- Homepage: https://gosummit.readthedocs.io/en/latest/
- Size: 47.4 MB
- Stars: 109
- Watchers: 8
- Forks: 21
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists
- awesome-cheminformatics - Summit - A python package for optimizing chemical reactions using machine learning (contains 10 algorithms + several benchmarks). (Libraries / Machine Learning)
- awesome-python-chemistry - Summit - Package for optimizing chemical reactions using machine learning (contains 10 algorithms + several benchmarks). (Machine Learning)
- awesome-self-driving-labs - Summit
- awesome-python-chemistry - Summit - Package for optimizing chemical reactions using machine learning (contains 10 algorithms + several benchmarks). (Machine Learning)
README
# Summit
Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. Go through a tutorial [here](https://colab.research.google.com/drive/1E_KVm-aZMqMm3memvesn3FIzGiXKHwJz#scrollTo=Wxg4TiHLV0Kd&uniqifier=2)!
## What is Summit?
Currently, reaction optimisation in the fine chemicals industry is done by intuition or design of experiments. Both scale poorly with the complexity of the problem.
Summit uses recent advances in machine learning to make the process of reaction optimisation faster. Essentially, it applies algorithms that learn which conditions (e.g., temperature, stoichiometry, etc.) are important to maximising one or more objectives (e.g., yield, enantiomeric excess). This is achieved through an iterative cycle.
Summit has two key features:
- **Strategies**: Optimisation algorithms designed to find the best conditions with the least number of iterations. Summit has eight strategies implemented.
- **Benchmarks**: Simulations of chemical reactions that can be used to test strategies. We have both mechanistic and data-driven benchmarks.
To get started, see the Quick Start below or follow our [tutorial](https://gosummit.readthedocs.io/en/latest/tutorial.html).
## Installation
To install summit, use the following command:
```pip install summit```
## News
* Denali (0.8) is out! Read more about the release [here](https://github.com/sustainable-processes/summit/releases/tag/0.8.0).
* Kobi ([@marcosfelt](https://github.com/marcosfelt)) gave a tutorial on Summit at the online Autonomous Discovery Symposium on Wednesday 21 April 2021. The tutorial can be found [here](https://colab.research.google.com/drive/1E_KVm-aZMqMm3memvesn3FIzGiXKHwJz#scrollTo=Wxg4TiHLV0Kd&uniqifier=2).
## Quick Start
Below, we show how to use the Nelder-Mead strategy to optimise a benchmark representing a nucleophlic aromatic substitution (SnAr) reaction.
```python
# Import summit
from summit.benchmarks import SnarBenchmark
from summit.strategies import SOBO, MultitoSingleObjective
from summit.run import Runner
# Instantiate the benchmark
exp = SnarBenchmark()
# Since the Snar benchmark has two objectives and Nelder-Mead is single objective, we need a multi-to-single objective transform
transform = MultitoSingleObjective(
exp.domain, expression="-sty/1e4+e_factor/100", maximize=False
)
# Set up the strategy, passing in the optimisation domain and transform
nm = SOBO(exp.domain, transform=transform)
# Use the runner to run closed loop experiments
r = Runner(
strategy=nm, experiment=exp,max_iterations=50
)
r.run()
# Make a pareto plot comparing both objectives
r.experiment.pareto_plot()
```
## Documentation
The documentation for summit can be found [here](https://gosummit.readthedocs.io/en/latest/index.html).
## Issues?
Submit an [issue](https://github.com/sustainable-processes/summit/issues) or send an email to [email protected].
## Citing
If you find this project useful, we encourage you to
* Star this repository :star:
* Cite our [paper](https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cmtd.202000051).
```
@article{Felton2021,
author = "Kobi Felton and Jan Rittig and Alexei Lapkin",
title = "{Summit: Benchmarking Machine Learning Methods for Reaction Optimisation}",
year = "2021",
month = "2",
url = "https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/cmtd.202000051",
journal = "Chemistry Methods"
}
```