# Leveraging large language models for predictive chemistry

*PRI Rank #5 · 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-5*

| Field | Value |
| --- | --- |
| Rank | #5 |
| 18m citations | 180 |
| Journal | Nature Machine Intelligence |
| Year | 2024 |
| DOI | 10.1038/s42256-023-00788-1 |
| Corresponding authors | Berend Smit |
| Institution | Friedrich Schiller University Jena, Germany |

**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/s42256-023-00788-1](https://doi.org/10.1038/s42256-023-00788-1)

## Topics

GPT-3 · large language models · Fine-tuning · natural language processing for chemistry · molecular property prediction · materials property prediction · chemical reaction yield prediction · inverse molecular design · Low-data regime · Foundation model · Textual molecular representation · transfer learning in chemistry · question-answering format · small chemical datasets · benchmarking against specialized models · natural language queries for chemistry

## 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
```