# Equivariant 3D-conditional diffusion model for molecular linker design

*PRI Rank #17 · 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-17*

| Field | Value |
| --- | --- |
| Rank | #17 |
| 18m citations | 91 |
| Journal | Nature Machine Intelligence |
| Year | 2024 |
| DOI | 10.1038/s42256-024-00815-9 |
| Corresponding authors | Bruno E. Correia |
| Institution | École Polytechnique Fédérale de Lausanne, Switzerland |

**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:** [10.1038/s42256-024-00815-9](https://doi.org/10.1038/s42256-024-00815-9)

## Topics

DiffLinker · Equivariant diffusion models · three-dimensional conditional diffusion · Linker design · fragment-based drug discovery · disconnected molecular fragments · arbitrary number of fragments · automatic atom placement · attachment point prediction · Protein pocket conditioning · Synthetic accessibility · Chemical diversity · De novo molecular design · equivariant neural networks · Structure-based drug design

## 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
```