# Reinvent 4: Modern AI–driven generative molecule design

*PRI Rank #15 · 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-15*

| Field | Value |
| --- | --- |
| Rank | #15 |
| 18m citations | 99 |
| Journal | Journal of Cheminformatics |
| Year | 2024 |
| DOI | 10.1186/s13321-024-00812-5 |
| Corresponding authors | Hannes H. Loeffler |
| Institution | AstraZeneca, Sweden |

**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:** [10.1186/s13321-024-00812-5](https://doi.org/10.1186/s13321-024-00812-5)

## Topics

REINVENT 4 · generative AI · Computational drug design · recurrent neural networks · Transformer · transfer learning · reinforcement learning · curriculum learning · De novo molecular design · R-group replacement · Library design · Linker design · Scaffold hopping · Molecular optimization · TOML configuration · JSON configuration · Drug discovery · Open-source framework · Apache 2.0 license

## 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
```