Application of Docking for Lead Optimization

Patra, Jeevan; Singh, D. B.; Jain, Sanchit; Mahindroo, Neeraj

doi:10.1016/b978-0-12-822312-3.00012-6

Cited by 2 publications

(3 citation statements)

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“…It remains one of the most essential techniques in structure-based drug design. − Molecular docking is used in different stages of the drug discovery process. Besides numerous applications in virtual screening, molecular docking can be applied to binding mode prediction for targets that are difficult in terms of structure elucidation (e.g., to advance fragments if no protein–fragment complex structure is available), for target identification, for function prediction for orphan proteins, and in the process of lead optimization …”

Section: Introductionmentioning

confidence: 99%

“…Besides numerous applications in virtual screening, 5 molecular docking can be applied to binding mode prediction for targets that are difficult in terms of structure elucidation (e.g., to advance fragments if no protein−fragment complex structure is available), 6 for target identification, 7 for function prediction for orphan proteins, 8 and in the process of lead optimization. 9 Over the past decades, academic groups, as well as companies, have published a large number of docking tools. A detailed discussion of all tools is beyond the scope of this paper.…”

Section: ■ Introductionmentioning

confidence: 99%

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Redocking the PDB

Flachsenberg,

Ehrt,

Gutermuth

et al. 2023

J. Chem. Inf. Model.

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Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Redocking the PDB

Flachsenberg,

Ehrt,

Gutermuth

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…2, it can be seen that the 430-cavity of NA has a large molecular volume and directly links to the active site, making it an ideal binding site for the design of NA inhibitors. 10 Some research groups have synthesized some series of NA inhibitors by targeting the 430-cavity. For example, Xiao et al 11 synthesized a series of acylhydrazone derivatives as potent neuraminidase inhibitors.…”

Section: Introductionmentioning

confidence: 99%