A step towards next-generation cancer nanomedicine

Coordination metallacages (MCgs) enable the design of various host structures capable of binding therapeutic guest molecules, such as cisplatin. In this study, well-tempered metadynamics (WT-MetaD) simulations were performed in explicit water and DMSO to investigate the host–guest interactions between two homoleptic [Pd2L4]4+ cages (endo-C and endo-N), differing in the hydrophilic character of their cavities, and cisplatin. The free-energy landscape revealed that the most stable state of the MCg⊃cisplatin systems corresponds to two cisplatin molecules associated with the external MCg surface (state I), via van der Waals or H-bonding interactions, regardless of the endo-functionalization. Encapsulation of a single cisplatin molecule with a second one bound at the cage periphery (state II) is accessible in water but remains less favorable for both cages and is strongly disfavored in DMSO, consistent with experimental observations. Using alternative collective variables to probe solvent coordination, the two cages exhibit distinct hydration behavior: endo-N accommodates cisplatin together with 3–4 water molecules, whereas endo-C hosts cisplatin alone due to its more hydrophobic cavity. Across simulations, the dynamic flexibility of the cages does not substantially alter the binding states. The analysis of observed MCg⊃cisplatin adducts suggests a transient association of the drug with the cage scaffold rather than tight encapsulation within the cavity, which could explain previously reported minor NMR chemical shifts in the pyridyl protons of the cage. Overall, these results highlight the highly dynamic nature of cisplatin nanoconfinement and underscore how cavity hydrophilicity governs coencapsulation of water, with implications for designing MCgs tailored for drug-delivery applications.

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This chapter focuses on the fundamental design principles that guide computational studies in asymmetric organocatalysis, emphasizing key concepts such as transition state theory, the Curtin–Hammett ....

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We present here an integrated computational workflow to study the behavior of selected self-assembled [Pd2L4]4+ metallacages in explicit solvents (water and DMSO) and benchmark them for future in sili...

Metallacages (MCgs) are three-dimensional (3D)-supramolecular coordination complexes (SCCs) obtained by the self-assembly of metal ions with donor lig…

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The prediction of molecular properties is essential in chemoinformatics and has many applications in drug discovery and materials design. Molecular representations play a key role in the prediction models to achieve high prediction accuracy. Nevertheless, appropriate molecular descriptors, including the utilization of conformational information, have been unclear due to a lack of systematic analysis of property prediction models and control. This study investigates the influence of using multiple conformers in machine learning-based property prediction, comparing two- and three-dimensional descriptors using three independent data sets: a large-scale quantum mechanical property, a medium-scale melting point, and small-scale enantioselective chemical reaction data sets. One unique aspect of this study is creating these carefully controlled data sets for models’ performance evaluation in conformational diversity and the target property’s dependence on conformation. Our findings show that using all available conformers as simple data augmentation consistently achieves high prediction accuracy among aggregation approaches, followed by mean aggregation. Furthermore, Uni-Mol, an end-to-end prediction model utilizing atomic coordinates and elements, combined with the ground-truth conformation, significantly outperformed traditional 2D and 3D descriptors and predicted conformational-sensitive properties with high accuracy. Although the prediction accuracy of the Uni-Mol model significantly decreased using the wrong conformers, it still outperformed two-dimensional extended connectivity fingerprints, which showed higher prediction accuracy than most of the tested 3D descriptors.