drugADR

The project involves implementation of a hierarchical anatomical schema for aggregation of side effects towards prediction of side effects (multi-class, multi-label setting) using existing data (SIDER4) by leveraging machine learning and statistical data analysis. During the course of this project the following tasks were performed:

• Extraction of relevant data of drug side effects, chemical properties etc.
• Hierarchical classification of side effects based on organ/system involved. A running visualization of the same, prepared as a part of this project, can be seen at http://cosylab.iiitd.edu.in/ADRhac.html.
• Data preprocessing.
• Implementation of machine learning algorithms for prediction of side effects.

Authors: Somin Wadhwa, Aishwarya Gupta, Shubham Dokania, Rakesh Kanji, Ganesh Bagler*

* Corresponding Author ([email protected])

This work was done in the Complex Systems Laboratory, Center for Computational Biology, IIIT-Delhi.

Pre-requisites

The following are a couple of instructions that must be gone through in order to execute different (or all) sections of this project.

1. Clone the project, replacing drugADR with the name of the directory you are creating:

    $ git clone https://github.com/sominwadhwa/drugADR.git drugADR
    $ cd drugADR
   

2. Make sure you have python 3.4.x running on your local system. If you do, skip this step. In case you don't, head head here.

3. virtualenv is a tool used for creating isolated 'virtual' python environments. It is advisable to create one here as well (to avoid installing the pre-requisites into the system-root). Do the following within the project directory:

    $ [sudo] pip install virtualenv
    $ virtualenv --system-site-packages drugADR
    $ source drugADR/bin/activate
   

To deactivate later, once you're done with the project, just type deactivate.

4. Install the pre-requisites from requirements.txt & run test/init.py to check if all the required packages were correctly installed:

    $ pip install -r requirements.txt
    $ python test/init.py
   

You should see an output - Imports successful. Good to go!

Directory Structure

Top-Level Structure:

.
.
├── data                     # Data used and/or generated
│   ├── 2d_prop.xlsx
│   ├── 3d_prop.xlsx
│   ├── all_se_clf_data.sav
│   ├── AssociatedDrugsVsSideEffects.png
│   ├── id_df.sav
│   ├── list_res_organ.sav
│   ├── list_res_S.sav
│   ├── list_res_Sub_Sys.sav
│   ├── meddra_all_se.tsv
│   ├── misc.xlsx
│   ├── o_v2.xlsx
│   ├── os_v2.xlsx
│   ├── SideEffectsVsAssociatedDrugs.png
│   ├── sub_sys.xlsx
│   ├── unique_SE.csv
├── src                    # Source Files
│   ├── base_o.py
│   ├── base_osub.py
│   ├── base_osys.py
│   ├── match.py
│   ├── preprocess_sider.py
│   ├── prop_pca.py
│   ├── voting_o.py
│   ├── voting_osub.py
│   ├── voting_osys.py
├── test                    # Testing modules (including those for random-control experiments)
│   ├── init.py
│   ├── rand_o.py
│   ├── random_osub.py
│   ├── random_osys.py                  
├── LICENSE
└── README.md
.
.

Files' Description:

• /data/meddra_all_se.tsv: File obtained from SIDER containing drug-ADR associations.
• /src/preprocess_sider.py: Loads the original SIDER data & fetches all the relevant identification tags (InChi, SMILES etc) required for extraction of the drug chemical properties through pubchempy. Output of the script is a dataframe (table) dump in /data/id_df.sav containing various identification tags of all 1430 drugs present in SIDER4.
• /data/2d_prop.xlsx & /data/3d_prop.xlsx: Chemical Properties for 1430 drugs generated using DiscoveryStudio4. They form the basis of our feature set.
• /src/prop_pca.py: Code for Principal Component Analysis on 2D & 3D molecular properties of drugs. Outputs cumulative preserved variance of first one hundred principal components (>99%).
• /src/base_se.py: Code for predicting ADR at the SE level using OneVsRest multi-class multi-label classification. Results generated are saved in a (table/dataframe) pickle dump /data/all_se_clf_data.sav.
• /data/o_v2.xlsx: Base data file used for organ level classification.
• /data/sub_sys.xlsx: Base data file used for sub-systems level classification.
• /data/os_v2.xlsx: Base data file used for organ-systems level classification.
• /src/base_o.py: Prediction of ADR with first level of classification based on anatomical schema -- organ level, 61 classes against 1430 drugs. Generated an output (workbook) in /data/o_v2_results.xlsx containing the results.
• /src/base_osub.py: Prediction of ADR with second level of classification based on anatomical schema -- sub-systems level, 30 classes against 1430 drugs. Generated an output (workbook) in /data/osub_results.xlsx containing the results.
• /src/base_osys.py: Prediction of ADR with final level of classification based on anatomical schema -- sub-systems level, 11 classes against 1430 drugs. Generated an output (workbook) in /data/osys_results.xlsx containing the results.
• /src/voting_o.py: Voting ensemble model at organ level. Generates & stores output /data/o_votingModel_results.xlsx.
• /src/voting_osub.py: Voting ensemble model at sub-systems level. Generates & stores output /data/osub_votingModel_results.xlsx.
• /src/voting_osys.py: Voting ensemble model at organ-system level. Generates & stores output /data/osys_votingModel_results.xlsx.
• /test/rand_o.py: Script to run random-control experiments on organ-level. Generates an output with a compilation of results. /data/list_res_organ.sav.
• /test/rand_osub.py: Script to run random-control experiments on sub-systems level. Generates an output with a compilation of results./data/list_res_Sub_Sys.sav.
• /test/rand_osys.py: Script to run random-control experiments on organ-systems level. Generates an output with a compilation of results./data/list_res_S.sav.

Description/Information about files other than those mentioned up can be directly inferred from the article/paper.

Running the tests

To run something simple, simply execute the standalone .py script via command line:

    $ python3 test/rand_o.py
    $ python3 src/base_o.py
    $ python3 src/prop_pca.py

Advisory: All these experiments were carried out on IIIT-Delhi's HPC server-node with these specifications due to the volume & time of compute required. It is advised to run any tests in a similar environment.

Acknowledgements

G.B. thanks the Indraprastha Institute of Information Technology (IIIT-Delhi) for providing computational facilities and support. S.W., A.G. and S.D. were Summer Research Interns in Dr. Bagler's lab at the Center for Computational Biology, and are thankful to IIIT-Delhi for the support and fellowship. R.K. thanks the Ministry of Human Resource Development, Government of India and Indian Institute of Technology Jodhpur for the senior research fellowship.