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. 2024 Jul 8;25(4):bbae321. doi: 10.1093/bib/bbae321

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© The Author(s) 2024. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Comparing DrugMetric Performance with QED across Datasets. (A,B) Shows the scores of DrugMetric and QED on the CD dataset, where DrugMetric (67.15) significantly outperforms QED (51.27). (C) Demonstrates the performance on a broad dataset of 195 anticancer drugs, with DrugMetric scoring an average of 70.01, significantly higher than QED’s 43.04. (D,E) Depicts their performance on MoleculeNet’s molecular property prediction datasets, where DrugMetric exhibits superior accuracy and versatility, while QED demonstrates relatively weaker performance. These results underscore the potential and value of DrugMetric in practical applications.

Inline graphic — Comparing DrugMetric Performance with QED across Datasets. (A,B) Shows the scores of DrugMetric and QED on the CD dataset, where DrugMetric (67.15) significantly outperforms QED (51.27). (C) Demonstrates the performance on a broad dataset of 195 anticancer drugs, with DrugMetric scoring an average of 70.01, significantly higher than QED’s 43.04. (D,E) Depicts their performance on MoleculeNet’s molecular property prediction datasets, where DrugMetric exhibits superior accuracy and versatility, while QED demonstrates relatively weaker performance. These results underscore the potential and value of DrugMetric in practical applications.