MoleQCage: Geometric High-Throughput Screening for Molecular Caging Prediction

Although being able to determine whether a host molecule can enclose a guest molecule and form a caging complex could benefit numerous chemical and medical applications, the experimental discovery of molecular caging complexes has not yet been achieved at scale. Here, we propose MoleQCage, a simple tool for the high-throughput screening of host and guest candidates based on an efficient robotics-inspired geometric algorithm for molecular caging prediction, providing theoretical guarantees and robustness assessment. MoleQCage is distributed as Linux-based software with a graphical user interface and is available online at https://hub.docker.com/r/dantrigne/moleqcage in the form of a Docker container. Documentation and examples are available as Supporting Information and online at https://hub.docker.com/r/dantrigne/moleqcage.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2024-12-12
DOI
https://doi.org/10.1021/acs.jcim.4c01419
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

MoleQCage: Geometric High-Throughput Screening for Molecular Caging Prediction

Anastasiia Varava, Alexander Kravberg, Danica Kragić, Lydia E. Kavraki et al.
Journal of Chemical Information and Modeling
Machine Learning in Materials Science
article

MoleQCage: Geometric High-Throughput Screening for Molecular Caging Prediction

Anastasiia Varava, Alexander Kravberg, Danica Kragić, Lydia E. Kavraki, Didier Devaurs
article en

Abstract

Although being able to determine whether a host molecule can enclose a guest molecule and form a caging complex could benefit numerous chemical and medical applications, the experimental discovery of molecular caging complexes has not yet been achieved at scale. Here, we propose MoleQCage, a simple tool for the high-throughput screening of host and guest candidates based on an efficient robotics-inspired geometric algorithm for molecular caging prediction, providing theoretical guarantees and robustness assessment. MoleQCage is distributed as Linux-based software with a graphical user interface and is available online at https://hub.docker.com/r/dantrigne/moleqcage in the form of a Docker container. Documentation and examples are available as Supporting Information and online at https://hub.docker.com/r/dantrigne/moleqcage.

Journal of Chemical Information and ModelingVol. 64(24)
University of Strathclyde (GB), Rice University (US), KTH Royal Institute of Technology (SE)
Rice University, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Medical Research Council
Openalex Percentile: Top 86%
Machine Learning in Materials Science
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MoleQCage: Geometric High-Throughput Screening for Molecular Caging Prediction — Anastasiia Varava, Alexander Kravberg, et al. · Journal of Chemical Information and Modeling (2024) | TGRS Research Map | TGRS