# Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

*PRI Rank #13 · 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-13*

| Field | Value |
| --- | --- |
| Rank | #13 |
| 18m citations | 125 |
| Journal | Nucleic Acids Research |
| Year | 2024 |
| DOI | 10.1093/nar/gkae254 |
| Corresponding authors | Alex G. C. de Sá, David B. Ascher |
| Institution | The University of Queensland, Australia |

**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:** [10.1093/nar/gkae254](https://doi.org/10.1093/nar/gkae254)

## Topics

Deep-PK · Pharmacokinetics · Toxicity prediction · ADMET · Graph neural network · Graph-based features · Molecular optimization · 73 endpoints · Small molecule drug discovery · High-throughput screening · absorption · distribution · metabolism · excretion · Graph-based features · ADMET · Model interpretability · Artificial intelligence in drug discovery

## 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
```