Machine learning‐based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel <scp>IGRs</scp>
These models promote the efficient design, screening, and prioritization of new or untested AST analogs. The study clarifies how a machine learning-based strategy facilitates the development of AST analogs as novel IGR lead candidates, offering a useful reference for pest management. © 2024 Society of Chemical Industry.
Authors
- Chandni Iqbal
- Meizi Wang (ORCID: https://orcid.org/0000-0001-9591-5753)
- Shixiang Pan (ORCID: https://orcid.org/0000-0002-1320-5852)
- Dexing Ye
- Jialin Cui (ORCID: https://orcid.org/0000-0001-5919-8849)
- Xinling Yang (ORCID: https://orcid.org/0000-0002-0118-402X)
- Zhen‐peng Kai (ORCID: https://orcid.org/0000-0003-4861-942X)
- Yan Liu (ORCID: https://orcid.org/0000-0002-3170-9771)
- Li Zhang (ORCID: https://orcid.org/0000-0001-6319-1092)
- Yimeng Zhang (ORCID: https://orcid.org/0000-0003-2248-8951)
- Wei Sun (ORCID: https://orcid.org/0000-0002-7958-6117)
- Qi He (ORCID: https://orcid.org/0000-0001-8578-7460)
- Xing‐Xing Lu
- Xinyuan Zhang (ORCID: https://orcid.org/0000-0003-0084-5911)
Institutions
- State Forestry and Grassland Administration (CN)
- China Agricultural University (CN)
- Shanghai Institute of Technology (CN)
Publication Details
- Journal
- Pest Management Science
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1002/ps.8518
- Citations
- 1
- Primary Topic
- Glycosylation and Glycoproteins Research
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China