# Machine learning-aided generative molecular design

*PRI Rank #16 · 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-16*

| Field | Value |
| --- | --- |
| Rank | #16 |
| 18m citations | 93 |
| Journal | Nature Machine Intelligence |
| Year | 2024 |
| DOI | 10.1038/s42256-024-00843-5 |
| Corresponding authors | Philippe Schwaller, Tom L. Blundell |
| Institution | Cornell 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:** [10.1038/s42256-024-00843-5](https://doi.org/10.1038/s42256-024-00843-5)

## Topics

machine learning · De novo molecular design · De novo molecular design · De novo molecular design · Deep learning models · Chemical space exploration · SMILES · molecular property prediction · reinforcement learning · generative adversarial networks · variational autoencoders · recurrent neural networks · Molecular optimization · Drug-likeness · Synthetic accessibility · transfer learning

## 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
```