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Dockey: a modern integrated tool for large-scale molecular docking and virtual screening.

• Authors : Du, Lianming^1,2 (AUTHOR); Geng, Chaoyue³ (AUTHOR); Zeng, Qianglin¹ (AUTHOR)

• Source: Briefings in Bioinformatics. Mar2023, Vol. 24 Issue 2, p1-7. 7p.

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fastDRH: a webserver to predict and analyze protein–ligand complexes based on molecular docking and MM/PB(GB)SA computation.

• Authors : Wang, Zhe¹ (AUTHOR); Pan, Hong² (AUTHOR); Sun, Huiyong³ (AUTHOR)

• Source: Briefings in Bioinformatics. Sep2022, Vol. 23 Issue 5, p1-10. 10p.

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Machine learning–enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations

• Authors : Si Zheng; Yaowen Gu; Yuzhen Gu

Subjects: 0301 basic medicine; 03 medical and health sciences; Problem Solving Protocol

• Source: Brief Bioinform

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InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening.

• Authors : Mohammad T; Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.; Mathur Y

Subjects: Algorithms* ; Drug Design* ; High-Throughput Screening Assays*

• Source: Briefings in bioinformatics [Brief Bioinform] 2021 Jul 20; Vol. 22 (4).Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN:

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HCovDock: an efficient docking method for modeling covalent protein–ligand interactions.

• Authors : Wu, Qilong¹ (AUTHOR); Huang, Sheng-You¹ (AUTHOR)

• Source: Briefings in Bioinformatics. Jan2023, Vol. 24 Issue 1, p1-12. 12p.

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ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody–antigen computational docking.

• Authors : Zhao, Nan¹ (AUTHOR); Han, Bingqing¹ (AUTHOR); Zhao, Cuicui¹ (AUTHOR)

• Source: Briefings in Bioinformatics. Mar2024, Vol. 25 Issue 2, p1-18. 18p.

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Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term.

• Authors : Zheng, Liangzhen^1,2 (AUTHOR); Meng, Jintao^1,3 (AUTHOR); Jiang, Kai⁴ (AUTHOR)

• Source: Briefings in Bioinformatics. May2022, Vol. 23 Issue 3, p1-15. 15p.

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A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function.

• Authors : Wang, Zechen¹ (AUTHOR); Zheng, Liangzhen^2,3 (AUTHOR) ; Wang, Sheng³ (AUTHOR)

• Source: Briefings in Bioinformatics. Jan2023, Vol. 24 Issue 1, p1-11. 11p.

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RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

• Authors : Tan LH; Interdisciplinary Graduate School, Nanyang Technological University, 61 Nanyang Drive, 637335 Singapore, Singapore.; Kwoh CK

Subjects: Molecular Docking Simulation* ; Machine Learning* ; Nucleic Acids*/Nucleic Acids*/Nucleic Acids*/chemistry

• Source: Briefings in bioinformatics [Brief Bioinform] 2024 Mar 27; Vol. 25 (3).Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN:

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GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

• Authors : Guo L; School of Computer Science and Engineering, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China.; Hunan Provincial Key Lab on Bioinformatics, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China.

Subjects: Proteins*/Proteins*/Proteins*/chemistry ; Proteins*/Proteins*/Proteins*/metabolism ; Molecular Docking Simulation*

• Source: Briefings in bioinformatics [Brief Bioinform] 2024 Mar 27; Vol. 25 (3).Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN:

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