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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.08938 |
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| _version_ | 1866916867574071296 |
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| author | Chen, Benson Danel, Tomasz Dreiman, Gabriel H. S. McEnaney, Patrick J. Jain, Nikhil Novikov, Kirill Akki, Spurti Umesh Turnbull, Joshua L. Pandya, Virja Atul Belotserkovskii, Boris P. Weaver, Jared Bryce Biswas, Ankita Nguyen, Dat Gorday, Kent Sultan, Mohammad Stanley, Nathaniel Whalen, Daniel M Kanichar, Divya Klein, Christoph Fox, Emily Watts, R. Edward |
| author_facet | Chen, Benson Danel, Tomasz Dreiman, Gabriel H. S. McEnaney, Patrick J. Jain, Nikhil Novikov, Kirill Akki, Spurti Umesh Turnbull, Joshua L. Pandya, Virja Atul Belotserkovskii, Boris P. Weaver, Jared Bryce Biswas, Ankita Nguyen, Dat Gorday, Kent Sultan, Mohammad Stanley, Nathaniel Whalen, Daniel M Kanichar, Divya Klein, Christoph Fox, Emily Watts, R. Edward |
| contents | DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents a bottleneck for the advancement of machine learning methodologies in this domain. To address this gap, we introduce KinDEL, one of the largest publicly accessible DEL datasets and the first one that includes binding poses from molecular docking experiments. Focused on two kinases, Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1), KinDEL includes 81 million compounds, offering a rich resource for computational exploration. Additionally, we provide comprehensive biophysical assay validation data, encompassing both on-DNA and off-DNA measurements, which we use to evaluate a suite of machine learning techniques, including novel structure-based probabilistic models. We hope that our benchmark, encompassing both 2D and 3D structures, will help advance the development of machine learning models for data-driven hit identification using DELs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08938 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors Chen, Benson Danel, Tomasz Dreiman, Gabriel H. S. McEnaney, Patrick J. Jain, Nikhil Novikov, Kirill Akki, Spurti Umesh Turnbull, Joshua L. Pandya, Virja Atul Belotserkovskii, Boris P. Weaver, Jared Bryce Biswas, Ankita Nguyen, Dat Gorday, Kent Sultan, Mohammad Stanley, Nathaniel Whalen, Daniel M Kanichar, Divya Klein, Christoph Fox, Emily Watts, R. Edward Quantitative Methods Machine Learning DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential, the scarcity of publicly available DEL datasets presents a bottleneck for the advancement of machine learning methodologies in this domain. To address this gap, we introduce KinDEL, one of the largest publicly accessible DEL datasets and the first one that includes binding poses from molecular docking experiments. Focused on two kinases, Mitogen-Activated Protein Kinase 14 (MAPK14) and Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1), KinDEL includes 81 million compounds, offering a rich resource for computational exploration. Additionally, we provide comprehensive biophysical assay validation data, encompassing both on-DNA and off-DNA measurements, which we use to evaluate a suite of machine learning techniques, including novel structure-based probabilistic models. We hope that our benchmark, encompassing both 2D and 3D structures, will help advance the development of machine learning models for data-driven hit identification using DELs. |
| title | KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2410.08938 |