Affinity2Vec: Drug-Target Binding Affinity Prediction Method Developed using Representation Learning, Graph Mining, and Machine Learning

This repositery provides an implementation of Affinity2Vec tool which is described in a research paper:

Affinity2Vec+ is a regression-based and network-based method

Received: 20 April 2021
Accepted:
Published:

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This code is implemented using Python 3.8.

For any qutions please contact the first author:

Maha A. Thafar

Email: [email protected]

Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST) - Faculty of Computers and Information Technology, Taif University (TU)

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Getting Started

Prerequisites:

There are several required Python packages to run the code:

• gensim
• numpy
• Scikit-learn
• keras
• deepchem
• protVec
• xgboost
• pandas

These packages can be installed using pip or conda as the follwoing example

pip install -r requirements.txt

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Files Description:

There are 5 folders:

1.Input folder: that includes two folder for 2 datasets include:

• Davis dataset,
• KIBA dataset,
• where each one of these folder has all required data of drug-target binding affinity (in Adjacency matrix format), drug-drug and target-target similarities in (square matrix format), the drugs' SMILES in dictionary format with drugs' IDs, and the proteins' amino-acid sequences in dictionary format with proteins' IDs

2.Embedding folder: that has two folders coressponding for 2 datasets, each folder contains the generated seq2seq embeddings for drugs, and generated ProtVec embeddings for proteins.

3.aupr folder: to convert the data first to binary and then calculate aupr evaluation metric

4.Code_to_generate_Embeddings folder: we add seq2seq model code and ProtVec model code that are necessory to generate the embeddings

5. Predictions Figures: These two. figures represent the binding affinities predicted by Affinity2Vec best model vs. actual binding affinity values for Davis and KIBA datasets

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There are 5 files:

(two main functions, one main for each dataset, and the other functions are same for all datasets which are imported in each main function)

• training_functions.py --> for several training and processing functions such as Cosine_similarity, normalization, etc.

• pathScores.py --> to calculate and return all meta-path scores for 6 path structures

• evaluation.py --> define all evalution metrics used in our experments.

• 2 main functions, one for each dataset:

• Affinity2Vec_Davis.py
• Affinity2Vec_KIBA.py

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Installing:

To get the development environment runining, the code get 2 parameteres from the user which is the dataset name and the model version (the defual dataset is nr) run:

python Affinity2Vec_Davis.py

python Affinity2Vec_KIBA.py

More details:

• about the source code that we utilized to generate the drugs' SMILES embedding, please refere to the main source code:

https://github.com/XericZephyr/seq2seq-fingerprint

• about proteins' amio-acid sequences embeddings please refere to the main source:

https://github.com/ehsanasgari/Deep-Proteomics

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To cite this work:

TBD