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| Main Authors: | , |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19984726 |
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Table of Contents:
- <p>The dataset was constructed by integrating compound bioactivity data from two primary sources: the dataset reported by <span class="hover:entity-accent entity-underline inline cursor-pointer align-baseline"><span class="whitespace-normal">Aljarf et al.</span></span> and the <span class="hover:entity-accent entity-underline inline cursor-pointer align-baseline"><span class="whitespace-normal">ChEMBL</span></span> database. To ensure relevance to leukemia-specific drug response, the data were filtered to retain only three human leukemia cell lines: K-562, CCRF-CEM, and HL-60(TB).</p> <p>Following data integration, quality control procedures, including the removal of duplicate entries and inconsistent records, were applied to obtain a clean and reliable dataset. The resulting dataset comprises 69,270 compound–activity pairs, distributed across the selected cell lines as follows: 28,602 for K-562, 27,850 for CCRF-CEM, and 12,818 for HL-60(TB).</p> <p>Each compound is represented using a SMILES string and is associated with a transformed bioactivity value (pGI50), obtained by applying a logarithmic transformation to the original GI50 measurements. In addition, a binary activity label is provided for each compound, where compounds are classified as active or inactive based on a predefined pGI50 threshold, enabling both regression and classification tasks.</p> <p>To mitigate class imbalance and ensure suitability for machine learning tasks, each subset corresponding to a given cell line was balanced as per the activity labels.</p>