Ionic Interactions in a Polarizable Dipolar Hard-Sphere Solvent

Potentials of mean forces of ions in infinitely diluted solutions are calculated by means of a general virial expansion for systems with short and long range interactions. The screened potentials are calculated by the MSA-approximation for the chain sum in the mixture model.     

On the electron impact induced hydroxyl loss from o-nitrostyrene

Unimolecular hydroxyl (OD^˙) loss from regio- and stereo-specifically labelled o-nitrostyrenes 1a, 1c and 1d results in the formation of an ion which upon collisional activation gives identical mass spectra. Suggestions are made which aim at explaining: (i) the loss of stereochemical integrity of the diastereotopic methylene hydrogens in the course of hydroxyl elimination; and (ii) to account for the collision induced losses of CO and HNC from the [M—hydroxyl]⁺ ion.     

Accessing New Chemical Entities through Microfluidic Systems

Flow systems have been successfully utilized for a wide variety of applications in chemical research and development, including the miniaturization of (bio)analytical methods and synthetic (bio)organic chemistry. Currently, we are witnessing the growing use of microfluidic technologies for the discovery of new chemical entities. As a consequence, chemical biology and molecular medicine research are being reshaped by this technique. In this Minireview we portray the state‐of‐the‐art, including the most recent advances in the application of microchip reactors as well as the micro‐ and mesoscale coil reactor‐assisted synthesis of bioactive small molecules, and forecast the potential future use of this promising technology.     

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.     

Designing the molecular future

Approximately 25 years ago the first computer applications were conceived for the purpose of automated ‘de novo’ drug design, prominent pioneering tools being ALADDIN, CAVEAT, GENOA, and DYLOMMS. Many of these early concepts were enabled by innovative techniques for ligand-receptor interaction modeling like GRID, MCSS, DOCK, and CoMFA, which still provide the theoretical framework for several more recently developed molecular design algorithms. After a first wave of software tools and groundbreaking applications in the 1990s—expressly GROW, GrowMol, LEGEND, and LUDI representing some of the key players—we are currently witnessing a renewed strong interest in this field. Innovative ideas for both receptor and ligand-based drug design have recently been published. We here provide a personal perspective on the evolution of de novo design, highlighting some of the historic achievements as well as possible future developments of this exciting field of research, which combines multiple scientific disciplines and is, like few other areas in chemistry, subject to continuous enthusiastic discussion and compassionate dispute.