Name
Abstract | ||
|---|---|---|
Many computational methods are devoted to rapidly generating pseudo-natural products to expand the open-ended border of chemical spaces for natural products. However, the accessibility and chemical interpretation were often ignored or underestimated in conventional library/fragment-based or rule-based strategies, thus hampering experimental synthesis. Herein, a bio-inspired strategy (named TeroGen) is developed to mimic the two key biosynthetic stages (cyclization and decoration) of terpenoid natural products, by utilizing physically based simulations and deep learning models, respectively. The precision and efficiency are validated for different categories of terpenoids, and in practice, more than 30 000 sesterterpenoids (10 times as many as the known sesterterpenoids) are predicted to be linked in a reaction network, and their synthetic accessibility and chemical interpretation are estimated by thermodynamics and kinetics. Since it could not only greatly expand the chemical space of terpenoids but also numerate plausible biosynthetic routes, TeroGen is promising for accelerating heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids and derivatives. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/BIB/BBAC197 | Briefings in Bioinformatics |
Keywords | DocType | Volume |
biosynthesis,chemical space,deep learning,molecular generation,terpenoids | Journal | 23 |
Issue | ISSN | Citations |
5 | 1477-4054 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zeng | 1 | 0 | 1.35 |
| Bernard Andes Hess | 2 | 0 | 0.34 |
| Fan Zhang | 3 | 0 | 9.13 |
| Ruibo Wu | 4 | 0 | 0.34 |