predicting drug likeness and molecular activity

Citation Author(s):
Amgad
Mahmoud
The British University in Egypt
Frederic
Andres
National Institute of Informatics
Andreas
Pester
The British University in Egypt
Shihori
Tanabe
National Institute of Health Sciences
Hesham
Ali
University of Nebraska at Omaha
Nada
Adel
New Giza University
Submitted by:
Andres Frederic
Last updated:
Fri, 10/13/2023 - 07:46
DOI:
10.21227/3zzp-hj56
Data Format:
License:
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Abstract 

During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.

 

References:

[1] Axelrod, S., & Gomez-Bombarelli, R. (2021). GEOM (Version V4) [Computer software]. Harvard Dataverse. https://doi.org/10.7910/DVN/JNGTDF

[2] Greg Landrum, Paolo Tosco, Brian Kelley, sriniker, gedeck, NadineSchneider, Riccardo Vianello, Ric, Andrew Dalke, Brian Cole, AlexanderSavelyev, Matt Swain, Samo Turk, Dan N, Alain Vaucher, Eisuke Kawashima, Maciej Wójcikowski, Daniel Probst, guillaume godin, … DoliathGavid. (2020). rdkit/rdkit: 2020_03_1 (Q1 2020) Release (Release_2020_03_1). Zenodo. https://doi.org/10.5281/zenodo.3732262

Instructions: 

You can start by importing the dataset, and then you can transform SMILES of the molecule into RDKit objects or using mooecular graphs.

Comments

Hope you enjoy playing with the data!

Submitted by Andres Frederic on Fri, 10/13/2023 - 05:41