Cavitation energies can outperform dispersion interactions

The accurate dissection of binding energies into their microscopic components is challenging, especially in solution. Here we study the binding of noble gases (He-Xe) with the macrocyclic receptor cucurbit[5]uril in water by displacement of methane and ethane as 1H NMR probes. We dissect the hydration free energies of the noble gases into an attractive dispersive component and a repulsive one for formation of a cavity in water. This allows us to identify the contributions to host-guest binding and to conclude that the binding process is driven by differential cavitation energies rather than dispersion interactions. The free energy required to create a cavity to accept the noble gas inside the cucurbit[5]uril is much lower than that to create a similarly sized cavity in bulk water. The recovery of the latter cavitation energy drives the overall process, which has implications for the refinement of gas-storage materials and the understanding of biological receptors

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

[Image: see text] Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon–oxygen bonds. Epoxides are metabolites often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). Identifying a molecule’s SOE can aid in interpreting adverse events related to reactive metabolites and direct modification to prevent epoxidation for safer drugs. This study utilized a database of 702 epoxidation reactions to build a model that accurately predicted sites of epoxidation. The foundation for this model was an algorithm originally designed to model sites of cytochromes P450 metabolism (called XenoSite) that was recently applied to model the intrinsic reactivity of diverse molecules with glutathione. This modeling algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions with a deep convolution network. This network makes predictions at both an atom and molecule level. The final epoxidation model constructed with this approach identified SOEs with 94.9% area under the curve (AUC) performance and separated epoxidized and non-epoxidized molecules with 79.3% AUC. Moreover, within epoxidized molecules, the model separated aromatic or double bond SOEs from all other aromatic or double bonds with AUCs of 92.5% and 95.1%, respectively. Finally, the model separated SOEs from sites of sp(2) hydroxylation with 83.2% AUC. Our model is the first of its kind and may be useful for the development of safer drugs. The epoxidation model is available at http://swami.wustl.edu/xenosite