How Many Crystal Structures Do You Need to Trust Your Docking Results?

Abstract Structure-based drug discovery relies on the prediction of protein-bound poses of new molecule designs, the accuracy of which can impact downstream prioritization. While it is expected that crystal structures of similar molecules would provide the best template for predicting the poses of new designs, the time and cost required motivates identifying a point of diminishing returns for collecting new structures. Using 403 crystal structures of SARS-CoV-2 main protease from the open science COVID Moonshot project, we explore the tradeoff between the cost and utility of obtaining crystal structures for accurately predicting poses of designed molecules. We observe that similar reference ligands enable superior pose prediction and show that success plateaus after approximately five crystal structures per generic Bemis-Murcko scaffold, exceeding 95% for the campaign’s lead series. This work provides practical recommendations for resource allocation in structure-enabled drug discovery campaigns.

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Publication Details

Journal
Journal of Medicinal Chemistry
Published
2026-10-07
DOI
https://doi.org/10.1021/acs.jmedchem.5c03559
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

How Many Crystal Structures Do You Need to Trust Your Docking Results?

Benjamin Kaminow, Hugo MacDermott-Opeskin, Sukrit Singh, Jenke Scheen et al.
Journal of Medicinal Chemistry
Computational Drug Discovery Methods
article

How Many Crystal Structures Do You Need to Trust Your Docking Results?

Benjamin Kaminow, Hugo MacDermott-Opeskin, Sukrit Singh, Jenke Scheen, Alexander Matthew Payne, D. Fearon, John D. Chodera, Iván Pulido, Maria A. Castellanos
article en

Abstract

Abstract Structure-based drug discovery relies on the prediction of protein-bound poses of new molecule designs, the accuracy of which can impact downstream prioritization. While it is expected that crystal structures of similar molecules would provide the best template for predicting the poses of new designs, the time and cost required motivates identifying a point of diminishing returns for collecting new structures. Using 403 crystal structures of SARS-CoV-2 main protease from the open science COVID Moonshot project, we explore the tradeoff between the cost and utility of obtaining crystal structures for accurately predicting poses of designed molecules. We observe that similar reference ligands enable superior pose prediction and show that success plateaus after approximately five crystal structures per generic Bemis-Murcko scaffold, exceeding 95% for the campaign’s lead series. This work provides practical recommendations for resource allocation in structure-enabled drug discovery campaigns.

Journal of Medicinal Chemistry
Memorial Sloan Kettering Cancer Center (US), Cornell University (US), Diamond Light Source (GB), Tri-Institutional PhD Program in Chemical Biology (US)
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
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How Many Crystal Structures Do You Need to Trust Your Docking Results? — Benjamin Kaminow, Hugo MacDermott-Opeskin, et al. · Journal of Medicinal Chemistry (2026) | TGRS Research Map | TGRS