AutoRevDock: An open‐source toolkit for scalable reverse docking

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 de...

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Published in:	Protein science Vol. 34; no. 11; pp. e70358 - n/a
Main Authors:	Luo, Qing, Mu, Yuguang, Zheng, Liangzhen, Guo, Jingjing
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.11.2025 Wiley Subscription Services, Inc
Subjects:	Drug Repositioning drug repurposing drug‐target interaction Humans Molecular Docking Simulation - methods Proteins - chemistry Proteomes reverse docking Software target fishing target prediction Toolkits drug‐target interaction target fishing target prediction drug repurposing reverse docking
ISSN:	0961-8368, 1469-896X, 1469-896X
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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.
AbstractList	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. 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.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.
Author	Guo, Jingjing Zheng, Liangzhen Mu, Yuguang Luo, Qing
Author_xml	– sequence: 1 givenname: Qing surname: Luo fullname: Luo, Qing organization: Macao Polytechnic University, Macao – sequence: 2 givenname: Yuguang surname: Mu fullname: Mu, Yuguang organization: School of Biological Sciences, Nanyang Technological University – sequence: 3 givenname: Liangzhen surname: Zheng fullname: Zheng, Liangzhen email: zhenglz@zelixir.com organization: Shenzhen Zelixir Biotech Company Ltd., Shenzhen – sequence: 4 givenname: Jingjing orcidid: 0000-0002-4632-4364 surname: Guo fullname: Guo, Jingjing email: jguo@mpu.edu.mo organization: Macao Polytechnic University, Macao
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SubjectTerms	Drug Repositioning drug repurposing drug‐target interaction Humans Molecular Docking Simulation - methods Proteins - chemistry Proteomes reverse docking Software target fishing target prediction Toolkits
Title	AutoRevDock: An open‐source toolkit for scalable reverse docking
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