# Odachi

Advanced deep learning-based organic retrosynthesis engine.

# Overview

The Odachi Retrosynthesis Engine provides a platform for predicting organic retrosynthetic disconnections using a graph convolutional network. It also exposes two custom Tensorflow layers for performing spectral graph convolutions. The engine powers the retrosynthesis.com website, which provides a clean and intuitive interface to run retrosynthetic predictions.

## Requirements

The Odachi Engine is built in Python 3. It has only three requirements to run:

```
- TensorFlow 2.x
- Scikit-Learn
- Numpy
```

# Reference

## Installation

To download dgaintel, simply use Pypi via pip.

`$ pip install odachi`

Alternatively, you could install from source.

```
$ git clone https://github.com/sudo-rushil/odachi
$ cd odachi
$ python setup.py install
```

Verify your installation by running

```
>>> import odachi
>>> odachi.engine.model.Odachi()
'<odachi.engine.model.Odachi object at 0x7f9ec80b3bd0>''
```

# Examples

### Predict bond disconnection

This is simple way of finding a retrosynthetic disconnection in a molecule. The input to the model is the SMILES string of the molecule (Ex. Aspirin).

```
from odachi.engine.model import Odachi
odachi = Odachi() # instantiates engine and load up TensorFlow model in backend.
results = odachi('O=C(C)Oc1ccccc1C(=O)O') # call prediction function on an input molecule.
print(results)
```

{'bonds': [2], 'smiles': 'O=C(C)Oc1ccccc1C(=O)O', 'svg':...}

# Documentation

The Odachi package exposes four main objects: the GraphConv and ConvEmbed TensorFlow layers for spectral graph convolutions with knockdown, the Conv object for representing molecules as graphs, and the Odachi object for top-level predictions.

## Layers

### GraphConv

```
graph_conv = odachi.engine.layers.GraphConv(n,
num_feat = 41,
num_atoms = 130,
activation = tf.nn.elu,
knockdown = 0.1,
BATCH_SIZE = 1)
```

Layer for performing single-phase spectral graph convolutions. Inherits from `tensorflow.keras.layers.Layer`

and has access to all associated methods.

#### Parameters

- n - Layer index for labeling purpose.
- num_feat - Number of features for each node in graph.
- num_atoms - Maximum number of nodes over all graphs.
- activation - Activation function for layer.
- knockdown - Convolutional knockdown threshold for spectral regularization.
- BATCH_SIZE - Number of batches in input

### Call

`A, X = graph_conv([A, X])`

#### Parameters

- A - Adjacency matrix of graph. Has dimensions (BATCH_SIZE, num_atoms, num_atoms).
- X - Features matrix of graph. Has dimensions (BATCH_SIZE, num_atoms, num_feat).

#### Returns

- A - Adjacency matrix of graph. Unchanged from input.
- X - Convolved features matrix of graph.

### ConvEmbed

```
conv_embed = odachi.engine.layers.ConvEmbed(num_feat = 41,
num_atoms = 130,
depth = 10,
knock = 0.2,
BATCH_SIZE = 1)
```

Model object for performing stacked graph convolutions with the number of features staying constant across layers. Inherits from `tensorflow.keras.Model`

.

#### Parameters

- num_feat - Number of features for each node in graph.
- num_atoms - Maximum number of nodes over all graphs.
- depth - Number of stacked convolutional layers.
- knock - Convolutional knockdown threshold.
- BATCH_SIZE - Number of batches in input.

### Call

`X = conv_embed([A, X])`

#### Parameters

- A - Initial adjacency matrix of graph. Has dimensions (BATCH_SIZE, num_atoms, num_atoms).
- X - Initial features matrix of graph. Has dimensions (BATCH_SIZE, num_atoms, num_feat).

#### Returns

- X - Fully convolved features matrix of graph.

## Molecular Graph Representation

### Conv

`conv = odachi.data.conv.Conv(smiles)`

Convolutional molecule (Conv) object for storing and representing molecules as featurized graphs upon which graph convolutional methods can be applied.

#### Parameters

- smiles - SMILES string representing the molecule to be stored as a featurized graph.

#### Attributes

- smiles - SMILES string of the molecule stored in the object.
- num_atoms - Number of atoms in the stored molecule.
- num_feat - Number of features per each atom (default 41).
- adj_matrix - Adjacency matrix of molecular graph. Padded up to 130 nodes by default.
- atom_features - Features matrix of molecular graph. Padded up to 130 nodes by default.

## Engine

### Odachi

`odachi = odachi.engine.models.Odachi(knock = 0.0)`

Engine implementation that wraps all three phases of the retrosynthetic prediction process to allow for predictions to be made and streamed to the retrosynthesis.com website.

#### Parameters

- knock - Convolutional knockdown threshold for loading saved models.

### Call

```
result_dict = odachi(smiles,
clusters = 2,
version = 9)
```

#### Parameters

- smiles - SMILES string of the query target molecule.
- clusters - number of synthons to cluster the target molecule into.
- version - Version edition of the convolutional embedding to use for prediction. Latest version is 9.

#### Returns

- result_dict - Dictionary containing prediction data.
- smiles - Original smiles string of target molecule.
- bonds - List of bonds which are predicted to be disconnected.
- svg - Raw SVG for rendering the predicted retrosynthetic disconnection.
- time - Total prediction runtime.