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https://github.com/giotto-ai/the-shape-of-chemical-functions


https://github.com/giotto-ai/the-shape-of-chemical-functions

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README 

        
![logo](https://raw.githubusercontent.com/giotto-ai/giotto-tda/master/doc/images/tda_logo.svg)



# The-shape-of-chemical-functions

The application of Machine Learning for biological data is one of the 

most promising and fascinating research direction of AI. In this notebook

we want to give a baseline indication to show how topological data analysis 

tools can be exploited to analyze molecules. More importantly, we show empirically

that shapes matter, in the sense that it is possible to match properties of objects with

their shapes.



The task of this use case is to classify molecules with respect to their 

inhibition property for the HIV virus. In order to achieve it, we propose a method 

to embed molecules into points of an Euclidean Space and so to represent chemical 

compounds with vectors. The method, which is based on TDA concepts, represents the 

most important part of the pipeline by learning meaningful features of molecules. Once 

the vector representation are obtained, they are used as input for a calssificator function

which is parametrized by a simple 2-hidden-layers nerual network. 



Have a lok at out [blog post](https://towardsdatascience.com/the-shape-of-chemical-functions-d1e1568d020)

to learn more.



## Data

The HIV dataset was introduced by the Drug

Therapeutics Program (DTP) AIDS Antiviral Screen, which

tested the ability to inhibit HIV replication for over 40 000

compounds. In the original dataset the chemical compounds were classified

into 3 different classes: confirmed inactive (CI), confirmed active (CA)

and confirmed moderately active (CM). As done in this [paper](https://pubs.rsc.org/en/content/articlehtml/2018/sc/c7sc02664a), 

the two classes CA and CM were grouped into one single class "Active".



## Feature Creation

The innovative part of using TDA in this classification problem consists in finding meaningful structural features for molecules. The idea is to embed molecules into an Euclidean Space where the Euclidean distance reflects the notion of structural dissimilarity.



In 



## Model

We cross-validated a fully connected neural network with 2 hidden layers: the hidden neurons present a ReLu activation function whereas the single output neuron has a sigmoid activation which represent the probability for an input molecule to be a HIV-inhibitor.



## Results

Our results show that the structural features found contain good quality informations on the inhibition property for the HIV viruses providing AUC-ROC scores comparable with the state-of-the-art solutions reported [here](https://pubs.rsc.org/en/content/articlehtml/2018/sc/c7sc02664a).

 

## Notebook overview

In this notebook we want to show how structural features can be inferred by using topological tools. In particolar, we provide atoms and bonds embedding for each molecules and, following the pipeline, we show that the classification procedure goes to very good results. Moreover, we let the user play with the hyperparameters and classificator model to see if other interesting results appear.



## Requirements

In order to run the notebook, the following python packages are required: 



- scikit-learn 0.21.3

- numpy 1.14.0

- networkx 2.4

- giotto-learn-nightly

- rdkit 2018.03.4.0

- deepchem 2.2.1.dev54

- keras 2.3.1

- pandas 0.25.2



To install rdkit and deepchem with conda:



    conda install -c deepchem -c rdkit