Synthetic Activators of Cell Migration Designed by Constructive Machine Learning

Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell-migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top-scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.     

Understanding Refractive Index Changes in Homologous Series of Unbranched Organic Compounds Based on Beer's Law

Changes of the refractive index for homologous series of hydrocarbons are usually plotted versus the density. While there is a clear linear dependence for alkanes and alkenes, the linearity deteriorates for homologous series with functional groups involving heteroatoms. The slope can even become negative, e. g., for carboxylic acids. For gaining a deeper understanding and to establish a more general correlation, we reinvestigate the corresponding theories starting with the Newton-Laplace, Gladstone-Dale and the Lorentz-Lorenz rules. We revisit the concept of molar refractivity pioneered by Landolt and Brühl and show that it is closely connected with a twin of Beer's law. We conclude that the refractive index of homologues series should better be plotted versus the molar concentration of the main UV-chromophore, the C−H bond, which actually causes the refractive index changes. This new approach is not limited to alkanes and alkenes but holds for homologous series with functional groups including heteroatoms.     

Manufacturing Man‐Made Magnetosomes: High‐Throughput In Situ Synthesis of Biomimetic Magnetite Loaded Nanovesicles

A new synthetic method for the production of artificial magnetosomes, i.e., lipid‐coated vesicles containing magnetic nanoparticles, is demonstrated. Magnetosomes have considerable potential in biomedical and other nanotechnological applications but current production methods rely upon magnetotactic bacteria which limits the range of sizes and shapes that can be generated as well as the obtainable yield. Here, electrohydrodynamic atomization is utilized to form nanoscale liposomes of tunable size followed by electroporation to transport iron into the nanoliposome core resulting in magnetite crystallization. Using a combination of electron and fluorescence microscopy, dynamic light scattering, Raman spectroscopy, and magnetic susceptibility measurements, it is shown that single crystals of single‐phase magnetite can be precipitated within each liposome, forming a near‐monodisperse population of magnetic nanoparticles. For the specific conditions used in this study the mean particle size is 58 nm (±8 nm) but the system offers a high degree of flexibility in terms of both the size and composition of the final product.

     

Similarities and Differences between Crystal and Enzyme Environmental Effects on the Electron Density of Drug Molecules

The crystal interaction density is generally assumed to be a suitable measure of the polarization of a low-molecular weight ligand inside an enzyme, but this approximation has seldomly been tested and has never been quantified before. In this study, we compare the crystal interaction density and the interaction electrostatic potential for a model compound of loxistatin acid (E64c) with those inside cathepsin B, in solution, and in vacuum. We apply QM/MM calculations and experimental quantum crystallography to show that the crystal interaction density is indeed very similar to the enzyme interaction density. Less than 0.1 e are shifted between these two environments in total. However, this difference has non-negligible consequences for derived properties.     

Forming of Polymer Nanofibers by a Pressurised Gyration Process

A new route consisting of simultaneous centrifugal spinning and solution blowing to form polymer nanofibers is reported. The fiber diameter (60–1000 nm) is shown to be a function of polymer concentration, rotational speed, and working pressure of the processing system. The fiber length is dependent on the rotational speed. The process can deliver 6 kg of fiber per hour and therefore offers mass production capabilities compared with other established polymer nanofiber generation methods such as electrospinning, centrifugal spinning, and blowing.