Drops on Polymer Brushes: Advances in Thin-Film Modeling of Adaptive Substrates.

We briefly review recent advances in the hydrodynamic modeling of the dynamics of droplets on adaptive substrates, in particular, solids that are covered by polymer brushes. Thereby, the focus is on long-wave and full-curvature variants of mesoscopic hydrodynamic models in gradient dynamics form. After introducing the approach for films/drops of nonvolatile simple liquids on a rigid smooth solid substrate, it is first expanded to an arbitrary number of coupled degrees of freedom before considering the specific case of drops of volatile liquids on brush-covered solids. After presenting the model, its usage is illustrated by briefly considering the natural and forced spreading of drops of nonvolatile liquids on a horizontal brush-covered substrate, stick-slip motion of advancing contact lines as well as drops sliding down a brush-covered incline. Finally, volatile liquids are also considered.

Stick-slip dynamics in the forced wetting of polymer brushes.

We study the static and dynamic wetting of adaptive substrates using a mesoscopic hydrodynamic model for a liquid droplet on a solid substrate covered by a polymer brush. First, we show that on the macroscale Young's law still holds for the equilibrium contact angle and that on the mesoscale a Neumann-type law governs the shape of the wetting ridge. Following an analytic and numeric assessment of the static profiles of droplet and wetting ridge, we examine the dynamics of the wetting ridge for a liquid meniscus that is advanced at constant mean speed. In other words, we consider an inverse Landau-Levich case where a brush-covered plate is introduced into (and not drawn from) a liquid bath. We find a characteristic stick-slip motion that emerges when the dynamic contact angle of the stationary moving meniscus decreases with increasing velocity, and relate the onset of slip to Gibbs' inequality and to a cross-over in relevant time scales.

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

Fragment-Based Discovery of 6-Arylindazole JAK Inhibitors.

Janus kinase (JAK) inhibitors are emerging as novel and efficacious drugs for treating psoriasis and other inflammatory skin disorders, but their full potential is hampered by systemic side effects. To overcome this limitation, we set out to discover soft drug JAK inhibitors for topical use. A fragment screen yielded an indazole hit that was elaborated into a potent JAK inhibitor using structure-based design. Growing the fragment by installing a phenol moiety in the 6-position afforded a greatly improved potency. Fine-tuning the substituents on the phenol and sulfonamide moieties afforded a set of compounds with lead-like properties, but they were found to be phototoxic and unstable in the presence of light.