Applications of machine learning in drug discovery and development. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. Nature Reviews Drug Discovery, 18(6):463–477, June, 2019. Number: 6 Publisher: Nature Publishing Group![Applications of machine learning in drug discovery and development [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.
@article{vamathevan_applications_2019,
title = {Applications of machine learning in drug discovery and development},
volume = {18},
copyright = {2019 Springer Nature Limited},
issn = {1474-1784},
url = {http://www.nature.com/articles/s41573-019-0024-5},
doi = {10.1038/s41573-019-0024-5},
abstract = {Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.},
language = {en},
number = {6},
urldate = {2022-05-05},
journal = {Nature Reviews Drug Discovery},
author = {Vamathevan, Jessica and Clark, Dominic and Czodrowski, Paul and Dunham, Ian and Ferran, Edgardo and Lee, George and Li, Bin and Madabhushi, Anant and Shah, Parantu and Spitzer, Michaela and Zhao, Shanrong},
month = jun,
year = {2019},
note = {Number: 6
Publisher: Nature Publishing Group},
keywords = {Diseases, Drug discovery, Imaging, Machine learning},
pages = {463--477},
}
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