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README.md

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+The CHOP_inhibitors_SMILES_1H_NMR_spectrosopy_concise dataset is a part of the study "Predicting Nanoparticle Effects on Small Biomolecule Functionalities Using the Capability of Scikit-learn and PyTorch: A Case Study on Inhibitors of the DNA Damage-Inducible Transcript 3 (CHOP)"
+https://doi.org/10.48550/arXiv.2504.09537
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+Dataset content: The Dataset has 20,309 rows of samples, 13 columns of features. Each feature corresponds to a range of the 1H NMR spectroscopy chemical shifts scale. Natural numbers have defined these ranges. The values of the features are the number of chemical picks in the corresponding range/feature for the relevant compound. The column "target" contains the labels, as follows: 1 for the active CHOP inhibitors and 0 for the inactive inhibitors.
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+Dataset generation: The labels and the corresponding SMILES were taken from PubChem AID 2732 bioassay https://pubchem.ncbi.nlm.nih.gov/bioassay/2732  "HTS for small molecule inhibitors of CHOP to regulate the unfolded protein response to ER stress", which contains 8,241 active and 210,429 inactive compounds. 
+The inactive samples were reduced to 12,091 samples by merging the aforementioned dataset with the dataset of PubChem AID 1996 bioassay https://pubchem.ncbi.nlm.nih.gov/bioassay/1996 "Aqueous Solubility from MLSMR Stock Solutions",  on SMLES, keeping only the common compounds for both bioassays.
+The SMILES notations on the remaining samples were then converted into 1H NMR spectroscopy data using the NMRDB online tool https://www.nmrdb.org/new_predictor/index.shtml?v=v2.171.1, counting the 1H NMR spectroscopy chemical shifts in ranges defined by natural numbers. 
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 license: "cc-by-4.0"