Commit 8cf0fed4 authored by Martino Bertoni's avatar Martino Bertoni 🌋

better readme intro

parent 33cb3508
# Signaturizer
Generate Chemical Checker signatures from molecules SMILES.
# Install from PyPI
![alt text](http://gitlabsbnb.irbbarcelona.org/packages/signaturizer/raw/master/images/cc_signatures.jpg "Molecule Signaturization")
```
Bioactivity signatures are multi-dimensional vectors that capture biological
traits of the molecule (for example, its target profile) in a numerical vector
format that is akin to the structural descriptors or fingerprints used in the
field of chemoinformatics.
Our **signaturizers** relate to bioactivities of 25 different types (including
target profiles, cellular response and clinical outcomes) and can be used as
drop-in replacements for chemical descriptors in day-to-day chemoinformatics
tasks.
For and overview of the different bioctivity descriptors available please check
the original Chemical Checker
[paper](https://www.nature.com/articles/s41587-020-0502-7) or
[website](https://chemicalchecker.com/)
# Installation
## from PyPI
```bash
pip install signaturizer
```
# Install from Git repository
## from Git repository
```
```bash
pip install git+http://gitlabsbnb.irbbarcelona.org/packages/signaturizer.git
```
# Example
# Usage
## Generating Bioactivity Signatures
```python
from signaturizer import Signaturizer
# load the bioactivity space predictor
# load the predictor for B1 space (representing the Mode of Action)
sign = Signaturizer('/aloy/web_checker/exported_smilespreds/B1')
# prepare a list of SMILES strings
smiles = ['C', 'CCC']
......@@ -34,7 +60,7 @@ results = sign.predict(smiles, 'destination.h5')
```
# Example Multiple Signatures
## Generating Multiple Bioactivity Signatures
```python
from signaturizer import Signaturizer
# load the bioactivity space predictor for all space
......@@ -46,4 +72,4 @@ smiles = ['C', 'CCC']
results = sign.predict(smiles)
print(results.signature.shape)
# (2, 3200)
```
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```
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