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A Robotics-Inspired Screening Algorithm for Molecular Caging Prediction
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemistry, Organic chemistry.ORCID iD: 0000-0002-9001-7708
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-1114-6040
Univ Grenoble Alpes, CNRS, Grenoble INP, INRIA,LJK,Inst Engn, F-38000 Grenoble, France..ORCID iD: 0000-0002-3415-9816
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2020 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 60, no 3, p. 1302-1316Article in journal (Refereed) Published
Abstract [en]

We define a molecular caging complex as a pair of molecules in which one molecule (the "host" or "cage") possesses a cavity that can encapsulate the other molecule (the "guest") and prevent it from escaping. Molecular caging complexes can be useful in applications such as molecular shape sorting, drug delivery, and molecular immobilization in materials science, to name just a few. However, the design and computational discovery of new caging complexes is a challenging task, as it is hard to predict whether one molecule can encapsulate another because their shapes can be quite complex. In this paper, we propose a computational screening method that predicts whether a given pair of molecules form a caging complex. Our method is based on a caging verification algorithm that was designed by our group for applications in robotic manipulation. We tested our algorithm on three pairs of molecules that were previously described in a pioneering work on molecular caging complexes and found that our results are fully consistent with the previously reported ones. Furthermore, we performed a screening experiment on a data set consisting of 46 hosts and four guests and used our algorithm to predict which pairs are likely to form caging complexes. Our method is computationally efficient and can be integrated into a screening pipeline to complement experimental techniques.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2020. Vol. 60, no 3, p. 1302-1316
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-272906DOI: 10.1021/acs.jcim.9b00945ISI: 000526390800024PubMedID: 32130862Scopus ID: 2-s2.0-85082145919OAI: oai:DiVA.org:kth-272906DiVA, id: diva2:1433946
Note

QC 20200602

Available from: 2020-06-02 Created: 2020-06-02 Last updated: 2024-03-18Bibliographically approved

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Kravchenko, OleksandrVarava, AnastasiiaPokorny, Florian T.Kragic, Danica

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Kravchenko, OleksandrVarava, AnastasiiaPokorny, Florian T.Devaurs, DidierKavraki, Lydia E.Kragic, Danica
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Organic chemistryRobotics, Perception and Learning, RPL
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