AutoRevDock: An open‐source toolkit for scalable reverse docking.
Gespeichert in:
| Titel: | AutoRevDock: An open‐source toolkit for scalable reverse docking. |
|---|---|
| Autoren: | Luo, Qing, Mu, Yuguang, Zheng, Liangzhen, Guo, Jingjing |
| Quelle: | Protein Science: A Publication of the Protein Society; Nov2025, Vol. 34 Issue 11, p1-8, 8p |
| Abstract: | Reverse docking is a pivotal computational strategy for drug repurposing and polypharmacology studies, yet existing tools often suffer from limitations in throughput, accuracy, and reliance on centralized servers. To overcome these challenges, we present AutoRevDock, an open‐source Python toolkit designed to streamline and enhance the reverse docking workflow. Key features include: (1) support for two established docking engines (AutoDock Vina and idock) with a hybrid scoring scheme (Vina_SFCT, combining the Vina score with a scoring function correction term (SFCT)); (2) pre‐processed target libraries covering the human proteome and DrugBank pharmacologically active targets; (3) support for custom target libraries and fully automated local execution. Benchmark evaluations demonstrate that idock operates over 40 times faster than AutoDock Vina. For multiple‐target drugs, Vina_SFCT outperforms the default scoring function in identifying biologically relevant targets. Furthermore, incorporating protein family information leads to increased hit rates, suggesting enhanced predictive power for real‐world applications. By combining robust methodology with user‐centric design, AutoRevDock offers a scalable solution for high‐throughput target fishing in drug discovery. The toolkit is freely available at https://github.com/AI4Bio-GuoLAB/AutoRevDock.git. [ABSTRACT FROM AUTHOR] |
| Copyright of Protein Science: A Publication of the Protein Society is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Datenbank: | Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Schreiben Sie den ersten Kommentar!