odachi 0.2.6

Advanced deep learning-based organic retrosynthesis engine

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.