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Category: chemistry – Page 178

With a big assist from artificial intelligence and a heavy dose of human touch, Tim Cernak’s lab at the University of Michigan has made a discovery that dramatically speeds up the time-consuming chemical process of building molecules that will be tomorrow’s medicines, agrichemicals or materials.

The discovery, published in the Feb. 3 issue of Science, is the culmination of years of chemical synthesis and data science research by the Cernak Lab in the College of Pharmacy and Department of Chemistry.

The goal of the research was to identify key reactions in the synthesis of a molecule, ultimately reducing the process to as few steps as possible. In the end, Cernak and his team achieved the synthesis of a complex alkaloid found in nature in just three steps. Previous syntheses had taken between seven and 26 steps.

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Claudiu Stan of Rutgers University—Newark, New Jersey, and his colleagues were watching moving drops of supercooled water spontaneously freeze when they noticed something unexpected: drops kept suddenly disappearing. Initially they thought that the lost drops had shattered as they froze. But, on closer inspection, they found that the icy drops were still there, they had just moved out of view. The team has now developed a quantitative model for this behavior, attributing it to a rocket-like propulsion mechanism induced by the freezing process [1]. Stan says that the finding could inspire scientists to design self-propelled systems powered by such phase transitions.

The team’s results add to a growing body of work on self-propelled drops. The mechanisms behind such motion vary wildly, but Stan notes that they all involve symmetry breaking. For the freezing drops, this symmetry breaking arises when the ice nucleation starts off-center. When ice nucleates, the change in structure releases latent heat, causing the local evaporation rate to suddenly increase, and if the nucleation is off-center, this enhanced evaporation occurs unevenly over the drop’s surface. Like a rocket ejecting a propellant heated by a chemical reaction, this asymmetrical evaporation increases the drop’s momentum, with the team’s model predicting peak velocities of nearly 1 m/s.

Stan says that this propulsion mechanism has a unique feature that could make it attractive for applications: unlike most self-propelled particles, it requires no surfaces and no surrounding fluid (the experiments were done under vacuum). But, for him, the findings have another bonus: “I am a fan of space exploration, so it was exciting to realize that [we could] draw an analogy between these tiny droplets and rockets,” he says.

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In recent years, organic chemicals containing boron (B) and silicon (Si) have found applications in various fields, including optoelectronics and pharmaceuticals. Moreover, they can also serve as building blocks for complex organic chemicals. As a result, scientists are actively looking for new ways to leverage these versatile chemical tools as well as produce more kinds of organosilicon and organoboron compounds.

One limitation of the synthesis methods currently available for these chemicals is that we cannot introduce multiple B-and Si-containing groups in aromatic heterocycles, i.e., carbon rings in which one of the is replaced by a nitrogen atom. If we could produce and freely transform such molecules, it would unlock the synthesis of several compounds relevant in medicinal chemistry.

Fortunately, a research team including Assistant Professor Yuki Nagashima from Tokyo Institute of Technology (Tokyo Tech), Japan has found a straightforward way around this limitation. As explained in their most recent study published in Nature Communications, the team has developed a method that allows them to modify quinolines, small organic molecules with an aromatic nitrogen heterocycle, with B-, Si-, and carbon-containing groups simultaneously.

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Architectures based on artificial neural networks (ANNs) have proved to be very helpful in research settings, as they can quickly analyze vast amounts of data and make accurate predictions. In 2020, Google’s British AI subsidiary DeepMind used a new ANN architecture dubbed the Fermionic neural network (FermiNet) to solve the Schrodinger equation for electrons in molecules, a central problem in the field of chemistry.

The Schroedinger is a partial differential equation based on well-established theory of energy conservation, which can be used to derive information about the behavior of electrons and solve problems related to the properties of matter. Using FermiNet, which is a conceptually simple method, DeepMind could solve this equation in the context of chemistry, attaining very accurate results that were comparable to those obtained using highly sophisticated quantum chemistry techniques.

Researchers at Imperial College London, DeepMind, Lancaster University, and University of Oxford recently adapted the FermiNet architecture to tackle a quantum physics problem. In their paper, published in Physical Review Letters, they specifically used FermiNet to calculate the ground states of periodic Hamiltonians and study the homogenous electron gas (HEG), a simplified quantum mechanical model of electrons interacting in solids.

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Pharmaceutical synthesis is often quite complex; simplifications are needed to speed up the initial phase of drug development and lower the cost of generic production. Now, in a study recently published in Science, researchers from Osaka University have discovered a chemical reaction that could transform drug production because of its simplicity and utility.

Pharmaceuticals generally contain a few tens of atoms and a similar number of chemical bonds between the atoms. Thus, designing complex architectures from simple precursors using the techniques of usually requires careful planning and tedious, incremental steps.

The gold standard in drug synthesis is to create, in one step, as many chemical bonds as possible. In principle, adding one atom—by forming four bonds in one step—to a drug precursor can be a means of doing so. Unfortunately, atomic carbon is generally too unstable for use in common chemical reaction conditions. This is the problem that the researchers sought to address.

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Evolution’s rapid pace after the Cambrian explosion

Though the work of Schopf and other paleobiologists continues to fill in the Precambrian fossil record, questions remain about the pace of the Cambrian explosion. What triggered life to evolve so fast?

The question has intrigued scientists of many disciplines for decades. Interdisciplinary collaboration has wrought a wealth of evidence from diverse perspectives — geochemical, paleoenvironmental, geological, anatomical, and taxonomic — that describes how biological organisms evolved in concert with changing environmental conditions.

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