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.
Authors
- Anastasiia Varava (ORCID: https://orcid.org/0000-0002-0900-1523)
- Alexander Kravberg (ORCID: https://orcid.org/0000-0002-9001-7708)
- Danica Kragić (ORCID: https://orcid.org/0000-0003-2965-2953)
- Lydia E. Kavraki (ORCID: https://orcid.org/0000-0003-0699-8038)
- Didier Devaurs (ORCID: https://orcid.org/0000-0002-3415-9816)
Institutions
- University of Strathclyde (GB)
- Rice University (US)
- KTH Royal Institute of Technology (SE)
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
Funders
- Rice University
- Knut och Alice Wallenbergs Stiftelse
- Vetenskapsrådet
- Medical Research Council