Practical guide to <scp>SHAP</scp> analysis: Explaining supervised machine learning model predictions in drug development
#2PRI RANKED
Most Cited Paper
in Computational Drug Discovery Methods
18 Months Post-Publication
Class of 2026
“Practical guide to <scp>SHAP</scp> analysis: Explaining supervised machine learning model predictions in drug development”
A research intelligence initiative by Pepkio
pri.pepkio.com/top-papers/computational-drug-discovery-methods/2026/
Topics
SHAP · SHAP · explainable AI · Model interpretability · feature attribution · supervised machine learning · black-box models · drug development applications · regression models · classification models · binary endpoints · time-series models · SHAP · model transparency · clinical decision support · feature importance analysis
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Pepkio Research Index (PRI). Topics and Trends in Most Cited Computational Drug Discovery Methods Papers, Class of 2026. https://pri.pepkio.com/top-papers/computational-drug-discovery-methods/2026. Accessed 2026-07-22. Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459