# ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries

*PRI Rank #10 · 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-10*

| Field | Value |
| --- | --- |
| Rank | #10 |
| 18m citations | 146 |
| Journal | Bioinformatics |
| Year | 2024 |
| DOI | 10.1093/bioinformatics/btae416 |
| Corresponding authors | Kyle Swanson, Rabindra V. Shivnaraine, James Zou |
| Institution | Stanford University, United States |

**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/bioinformatics/btae416](https://doi.org/10.1093/bioinformatics/btae416)

## Topics

ADMET · ADMET · machine learning · Compound libraries · high-throughput docking · generative AI · Drug-likeness · absorption · distribution · metabolism · excretion · Toxicity prediction · TDC ADMET Leaderboard · combinatorial chemical spaces · batch prediction · Python package · Web server · Small molecule 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
```