# Practical guide to <scp>SHAP</scp> analysis: Explaining supervised machine learning model predictions in drug development

*PRI Rank #2 · Topics and Trends in Most Cited Computational Drug Discovery Methods Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/computational-drug-discovery-methods/2026/rank-2*

| Field | Value |
| --- | --- |
| Rank | #2 |
| 18m citations | 508 |
| Journal | Clinical and Translational Science |
| Year | 2024 |
| DOI | 10.1111/cts.70056 |
| Corresponding authors | Sven Stodtmann |
| Institution | AbbVie Deutschland GmbH & Co. KG, Germany |

**Ranking page:** [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)

**Paper link:** [10.1111/cts.70056](https://doi.org/10.1111/cts.70056)

## 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

## Cite this ranking

```
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
```