theia-pypi 0.2

Explainable prediction of EC numbers using a multilayer perceptron.

:anchor: Theia

Quickstart

As you need at least Python 3.9 to get started, I suggest you use conda to create an environment with an up-to-date Python versions (3.11 is really, really fast, so I suggest using this as soon as rdkit supports it). For now, let's go with Python 3.10: conda create -n theia python==3.10 && conda activate theia is all you need (ha). Then you can go ahead and install theia using pip (theia was taken, so make sure to install theia-pypi, except if you want to parse log files):

pip install theia-pypi

Thats pretty much it, now you can start theia by simply typing:

theia

and open the url http://127.0.0.1:5000/ in your web browser.

In case you don't want or need an UI, you can also use the cli to simply predict an EC number from an arbitrary reaction:

theia-cli "rheadb.ec123" "S=C=NCC1=CC=CC=C1>>N#CSCC1=CC=CC=C1"

If you want a bit more information than just the predicted EC class, you can also get the top-5 probabilities:

theia-cli "rheadb.ec123" "S=C=NCC1=CC=CC=C1>>N#CSCC1=CC=CC=C1" --probs

Or, if you want human-readable output, you can make it pretty:

theia-cli "rheadb.ec123" "S=C=NCC1=CC=CC=C1>>N#CSCC1=CC=CC=C1" --probs --pretty

and you'll get a neat table...

┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Prediction ┃ Probability [%] ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ 2.7.4      │           14.22 │
│ 2.3.2      │           11.03 │
│ 2.3.1      │            7.15 │
│ 2.7.8      │            4.62 │
│ 2.6.1      │            4.05 │
└────────────┴─────────────────┘

Of course, there are more models than rhea.ec123, which we used in the previous examples. Here's a complete list of all the included models:

Model	Trained on	Name
Rhea ECX	Rhea	rheadb.ec1
Rhea ECXY	Rhea	rheadb.ec12
Rhea ECXYZ	Rhea	rheadb.ec123
ECREACT ECX	ECREACT 1.0	ecreact.ec1
ECREACT ECXY	ECREACT 1.0	ecreact.ec12
ECREACT ECXYZ	ECREACT 1.0	ecreact.ec123

Reproduction / Custom Models

To get started, install the reproduction requirements with:

pip install -r reproduction_requirements.txt

The training, validation, and test sets used in the manuscript can be recreated using the following two commands (of course, you can plug in your own data sets here to get a custom model):

mkdir experiments/data
python scripts/encode_split_data.py data/rheadb.csv.gz experiments/data/rheadb
python scripts/encode_split_data.py data/ecreact-nofilter-1.0.csv.gz experiments/data/ecreact

The training of the models can be started with:

chmod +x train_all.sh
./train_all.sh

If you want to train the 6 additional models for cross-validation, you can run the following:

chmod +x train_all_cross.sh
./train_all_cross.sh

Finally, to reproduce the figures, you first have to run some additional data crunching scripts:

python scripts/class_counts.py data/ecreact-nofilter-1.0.csv.gz experiments/data/ecreact_counts.csv
python scripts/class_counts.py data/rheadb.csv.gz experiments/data/rheadb_counts.csv

Then it's time to draw:

cd figures
chmod +x generate_figures.sh
./generate_figures.sh

fin.