Forcing polymers to be semiconductors: In mechanochemistry, the application of force to a polymer is used to pry open specific chemical bonds. Chen et al. leveraged this technique to produce semiconducting blocks of polyacetylene in an insulating precursor. Ring-opening metathesis polymerization tethered together a series of fused four-carbon rings, reminiscent of the unusual ladderane membrane lipids of anaerobic ammonium-oxidizing bacteria. Subsequently, sonication unzipped these strained rings into alternating C=C double bonds, thereby extending π-conjugation along the polymer backbone. Science , this issue p. 475

While alkyl halides are valuable intermediates in synthetic organic chemistry, their use as bioactive motifs in drug discovery and medicinal chemistry is rare in comparison. This is likely attributable to the common misconception that these compounds are merely non-specific alkylators in biological systems. A number of chlorinated compounds in the pharmaceutical and food industries, as well as a growing number of halogenated marine natural products showing unique bioactivity, illustrate the role that chiral alkyl halides can play in drug discovery. Through a series of case studies, we demonstrate in this review that these motifs can indeed be stable under physiological conditions, and that halogenation can enhance bioactivity through both steric and electronic effects. Our hope is that, by placing such compounds in the minds of the chemical community, they may gain more traction in drug discovery and inspire more synthetic chemists to develop methods for selective halogenation.

Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures via mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections. Non-adiabatic molecular dynamics require thousands of these computations per trajectory, which limits simulations to ∼1 picosecond for most organic photochemical reactions. Westermayr et al. recently introduced a neural-network-based method to accelerate the predictions of electronic properties and pushed the simulation limit to 1 ns for the model system, methylenimmonium cation (CH₂NH₂ ⁺). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI²MD) software for the cis–trans isomerization of trans-hexafluoro-2-butene and the 4π-electrocyclic ring-closing of a norbornyl hexacyclodiene. We performed a 10 ns simulation for trans-hexafluoro-2-butene in just 2 days. The same simulation would take approximately 58 years with traditional multiconfigurational photodynamics simulations. We generated training data by combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to adaptively sample sparse data regions along reaction coordinates. The final data set of the cis–trans isomerization and the 4π-electrocyclic ring-closing model has 6207 and 6267 data points, respectively. The training errors in energy using feedforward neural networks achieved chemical accuracy (0.023–0.032 eV). The neural network photodynamics simulations of trans-hexafluoro-2-butene agree with the quantum chemical calculations showing the formation of the cis-product and reactive carbene intermediate. The neural network trajectories of the norbornyl cyclohexadiene corroborate the low-yielding syn-product, which was absent in the quantum chemical trajectories, and revealed subsequent thermal reactions in 1 ns.