
Sirtuin6 Small Molecules
Donated on 10/28/2022
The dataset includes 100 molecules with 6 most relevant descriptors to determine the candidate inhibitors of a target protein, Sirtuin6. The molecules are grouped based on their low- and high-BFEs.
Dataset Characteristics
Tabular
Subject Area
Life Sciences
Associated Tasks
Classification
Attribute Type
-
# Instances
100
# Attributes
-
Information
What do the instances in this dataset represent?
Small molecules
Was there any data preprocessing performed?
The original data consists a complete set of 1875 molecular descriptors generated by PaDEL-Descriptor software and needs feature selection before classification since some of the features are redundant. We reduced the descriptor set by Unsupervised Forward Selection and used the hyperbox classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification.
Citation Requests/Acknowledgements
Tardu, M., Rahim, F., Kavakli, I. H., & Turkay, M. (2016). Milp-hyperbox classification for structure-based drug design in the discovery of small molecule inhibitors of Sirtuin6. RAIRO-Operations Research, 50(2), 387-400. https://doi.org/10.1051/ro/2015042
Features
Attribute Name | Role | Type | Description | Units | Missing Values |
---|---|---|---|---|---|
SC-5 | Feature | Numerical - Continuous | false | ||
SP-6 | Feature | Numerical - Continuous | false | ||
SHBd | Feature | Numerical - Continuous | false | ||
minHaaCH | Feature | Numerical - Continuous | false | ||
maxwHBa | Feature | Numerical - Continuous | false | ||
FMF | Feature | Numerical - Continuous | false | ||
Class | Target | Categorical | false |
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Introduction Paper
-
Tardu,Mehmet & RAHIM,FATIH. (2022). Sirtuin6 Small Molecules. UCI Machine Learning Repository.
@misc{misc_sirtuin6_small_molecules_748, author = {Tardu,Mehmet & RAHIM,FATIH}, title = {{Sirtuin6 Small Molecules}}, year = {2022}, howpublished = {UCI Machine Learning Repository} }
Keywords
Creators
Mehmet Tardu
mtardu@ku.edu.tr
KoƧ University
FATIH RAHIM
frahim@ku.edu.tr
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.