# MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction

*PRI Rank #17 · Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026/rank-17*

| Field | Value |
| --- | --- |
| Rank | #17 |
| 18m citations | 38 |
| Journal | Briefings in Bioinformatics |
| Year | 2024 |
| DOI | 10.1093/bib/bbae387 |
| Corresponding authors | Jingjing Wang |
| Institution | State Key Laboratory of NBC Protection for Civilian, China |

**Ranking page:** [Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026](https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026)

**Paper link:** [10.1093/bib/bbae387](https://doi.org/10.1093/bib/bbae387)

## Topics

Enzyme kinetics · Turnover number · Michaelis constant (Km) · multitask deep learning · pretrained language models · MPEK model · Enzyme function prediction · DLKcat · UniKP · Kroll_model · enzyme promiscuity · Enzyme function prediction · directed evolution · Enzyme engineering · pH and temperature modeling · organismal information integration · Pearson coefficient evaluation · Bioprocessing

## Cite this ranking

```
Pepkio Research Index (PRI). Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026. https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026. Accessed 2026-07-29.

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
```