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Cheminformatic analysis of small molecule type drugs in DrugBank for their ability to form nanoparticles with indocyanine dyes.
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LICENSE
README.md
count_carbonyls.py
count_fluorines.py
count_nitro.py
count_nitroso.py
count_sulfinyls.py
count_sulfonyls.py
count_tertAmines.py
df_drugbank_decision_tree.csv
df_drugbank_sm.csv
df_drugbank_smiles.csv
drugbank_decision_tree.ipynb
get_descriptors_from_drugbank_xml.ipynb
requirements.txt

README.md

DOI

nano-drugbank

Cheminformatic analysis of small molecule type drugs in DrugBank for their ability to form nanoparticles with indocyanine dye IR783.

Related Publication

Yosi Shamay, Janki Shah, Mehtap Işık, Aviram Mizrachi, Josef Leibold, Darjus F. Tschaharganeh, Daniel Roxbury, Januka Budhathoki-Uprety, Karla Nawaly, James L. Sugarman, Emily Baut, Michelle R. Neiman, Megan Dacek, Kripa S. Ganesh, Darren C. Johnson, Ramya Sridharan, Karen L. Chu, Vinagolu K. Rajasekhar, Scott W. Lowe, John D. Chodera, Daniel A. Heller. Quantitative Self-Assembly Prediction Yields Targeted Nanomedicines. Manuscript in revision.

Author

Overview

This repository contains analysis of small molecule drug entries in XML version of DrugBank Database Version 4.5.0 (released on 2016.04.20)[1] to categorize predicted nanoparticle formation abilities according to the decision tree in publication above. SMILES, LogP, strongest acidic pKa and strongest basic pKa values were extracted from DrugBank DataBase. We created a new molecular property descriptor called Number of High Intrinsic State Substructures (NHISS). NHISS is calculated as the total number of functional groups in a molecule with fluorine (-F) and double bonded oxygen (=O) which have high instrinsic state values [2].

NHISS = fluorine + carbonyl + sulfinyl + 2 * sulfonyl + nitroso + 2 * nitro

Substructure search was performed for fluorine, carbonyl, nitro, nitroso, sulfinyl and sulfonyl functional groups on the SMILES strings of each DrugBank entry. Substructure search based on SMARTS was performed programmatically using OpenEYE OEChem Toolkit [3,4].

Steps of the analysis of drugs in DrugBank:

  1. Downloading latest DrugBank Database (Released on Apr. 20th, 2016, XML format) from http://www.drugbank.ca/releases/4-5-0/downloads/all-full-database and named drugbank_20160420.xml

  2. Run IPython Notebook get_descriptors_from_drugbank_xml.ipynb

    Dependencies of this notebook:

    • OpenEye OEChem library, version 2.0.5, OpenEye Scientific Software, Inc., Santa Fe, NM, USA, www.eyesopen.com, 2010.
    • Python scripts: count_carbonyls.py, count_fluorines.py, count_sulfinyls.py, count_sulfonyls.py, count_nitroso.py, count_nitro.py
  3. Run IPython Notebook drugbank_decision_tree.ipynb

    Output: df_drugbank_decision_tree.csv (Group column represents which group of decision tree each small molecule drug is categorized to.)

References

  1. Wishart, D.S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research 34, D668-672 (2006).
  2. Kier, L.B. & Hall, L.H. An electrotopological-state index for atoms in molecules. Pharmaceutical research 7, 801-807 (1990).
  3. OEChem, version 2.0.5, OpenEye Scientific Software, Inc., Santa Fe, NM, USA, www.eyesopen.com, 2010.
  4. Schomburg, Karen T., Lars Wetzer, and Matthias Rarey. “Interactive Design of Generic Chemical Patterns.” Drug Discovery Today 18, no. 13–14 (July 2013): 651–58. doi:10.1016/j.drudis.2013.02.001.

Dependencies

  • OpenEye OEChem, version 2.0.5
    To install required packages: pip install -r requirements.txt
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