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Category Archives: Organic Chemistry
How A-level chemistry solved the 200-year-old problem with the haloform reaction – Chemistry World
The 200-year-old haloform reaction has been given a modern makeover. Using rigorous mechanistic studies to inform their strategy, researchers at the University of Bristol drove this reaction to accept secondary alcohols in stochiometric quantities, something never attempted in the reactions history.
First discovered in 1822, the haloform reaction converts methyl ketones into carboxylic acids or esters, forming an insoluble haloform as a byproduct. The methyl protons are acidified by virtue of being next to the carbonyl which essentially means that the methyl group CH3 can be converted into CX3, the trihalomethyl, which is a leaving group, explains Liam Ball, a physical organic chemist at the University of Nottingham who wasnt involved with the new work. This CX3 group can then be substituted by either water or alcohol at the carbonyl to make a new COH or COR bond. Both mild and reliable, the reaction became an industrial staple for the synthesis of carboxylic acids and methyl esters, but the requirement for solvent quantities of alcohol limited its application in the synthesis of more complex products.
We were very surprised to discover that no one had actually used more complex alcohols in this reaction, says study author Alastair Lennox. So we set out to discover why that was and whether we could use that knowledge to expand the scope of this reaction. The original reaction uses a combination of aqueous bases to generate the trihalogenated leaving group, but subsequent competition of hydroxide ions from this mixture with the intended nucleophile means a vast excess of alcohol is required to favour formation of the ester over the carboxylic acid.
Seeking to eliminate this competing reaction, Lennoxs team began exploring alternative non-aqueous reagents for the initial iodination step, finally settling on the organic base DBU. With these dry conditions, the team could reduce the amount of alcohol to just one equivalent, facilitating the reaction with a range of non-solvent primary alcohols for the first time.
However, the corresponding reactions with secondary alcohols failed to reach completion under these conditions so the team commenced detailed mechanistic studies to identify the problem step. The headline from those studies is that the iodination steps are reversible. Previously that had not been documented, says Lennox.
Kinetic experiments revealed the formation of the trihalo leaving group occurs in three reversible steps. The equilibrium favours the product in the first two stages but the final step, which forms the trihalogenated compound from the dihalo intermediate, is not favoured, with a competing side product dominating the equilibrium. The reason that is important is because with the primary alcohol the substitution is very rapid so you dont observe this reversibility. Whereas, with the secondary alcohols, because the substitution is significantly slower, we see that equilibrium at play, explains Lennox.
This deep mechanistic insight was the crucial missing piece which enabled the team to incorporate a diverse panel of secondary alcohols into the reaction. In the end, the solution was very simple. We just added more DBU and iodine and that pushed the equilibrium via Le Chateliers principle towards the triiodo compound, Lennox explains. At a higher concentration, the secondary alcohol could now react with it in more meaningful rates.
The teams thorough approach particularly impressed Ball. This could be really beneficial to complex molecule synthesis, both in academia and industry, by enabling the use of more valuable alcohols in this reaction, he says. I think the next step has to be extending this to tertiary alcohols too.
Lennox is eager to exploit the possibilities suggested by these mechanistic insights and the team intend to investigate different nucleophiles as a method to generate other complex molecules from methyl ketones. I think this could really open up new avenues in the formation of different complex products, he says.
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How A-level chemistry solved the 200-year-old problem with the haloform reaction - Chemistry World
Porous organic ‘cage of cages’ crystalline structure predicted by computational modelling – Chemistry World
Organic cages have been used as precursors to synthesise higher-order porous structures, adding to their functionality while the ability to solution process them is retained.
The team from the UK and China used ether-bridged cage molecules as a building block its chlorine atoms are essential for forming ether bridges with fluorine-enriched tetrafluorohydroquinone (TFHQ) as the linear bridge. The fluorine atoms offer structural integrity by limiting bond rotation and can improve the solubility of the resulting cagecage molecules.
Models were constructed using molecular dynamics and density functional theory to predict the reaction products of these blocks. Several topologies and their relative energies were predicted, not considering solvent effects, and the results indicated a strong preference for a [4[2+3]+6] cage product. The team, guided by these simulations, conducted two-step assembly experiments to screen optimum conditions.
The results showed good agreement between the predicted structure for the [4[2+3]+6] cage molecule and the observed crystal structure four trigonal cages assembled into a larger tetrahedral cage. The product demonstrated both good sorption capacity and hydrolytic stability important properties for gas separation and water remediation technologies.
This new cage of cages structure could be used as a building block for even more complex structures. This study highlights the use of computational methods to assess the most likely reaction products as well as non-intuitive new materials in supramolecular synthesis.
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Staley speaks to Gamecocks’ ‘organic chemistry’ that led to title win – Southeastern Conference
Staley speaks to Gamecocks' 'organic chemistry' that led to title win Southeastern Conference
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Staley speaks to Gamecocks' 'organic chemistry' that led to title win - Southeastern Conference
Complex Organic Chemistry In Sulfuric Acid And Life On Venus – Hackaday
Finding extraterrestrial life in any form would be truly one of the largest discoveries in humankinds history, yet after decades of scouring the surface of Mars and investigating other bodies like asteroids, we still have found no evidence. While we generally assume that were looking for carbon-based lifeforms in a water-rich environment like Jupiters moon Europa, what if complex organic chemistry would be just as happy with sulfuric acid (H2SO4) as solvent rather than dihydrogen monoxide (H2O)? This is the premise behind a range of recent studies, with a newly published research article in Astrobiology by [Maxwell D. Seager] and colleagues lending credence to this idea.
Previous studies have shown that organic chemistry in concentrated sulfuric acid is possible, and that nucleic acid bases including adenosine, cytosine, guanine, thymine and uracil which form DNA are also stable in this environment, which is similar to that of the Venusian clouds at an altitude where air pressure is roughly one atmosphere. In this new article, twenty amino acids were exposed to the concentrations of sulfuric acid usually found on Venus, at 98% and 81%, with the rest being water. Of these, 11 were unchanged after 4 weeks, 9 were reactive on their side chains, much like they would have been in pure water. Only tryptophan ended up being unstable, but as the researchers note, not all amino acids are stable in water either.
The limitations of this research is of course that it was performed in a laboratory environment, with uncontaminated concentrated sulfuric acid, rather than the Venusian clouds with their trace elements of other gases such as CO2 and the constant bombardment with meteors that have been shown to often be laced with such amino acids. Future research will take these variables into account, even as scientists cannot wait to get data from upcoming Venus missions, with better sensors that may just catch a glimpse of such organic chemistry in action.
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Complex Organic Chemistry In Sulfuric Acid And Life On Venus - Hackaday
Advancing Chemistry with AI: New Model for Simulating Diverse Organic Reactions – Lab Manager Magazine
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Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of organic materials and conditions.
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"It's a tool that can be used to investigate more reactions in this field," said Shuhao Zhang, a graduate student in Carnegie Mellon University'sDepartment of Chemistry. "We can offer a full simulation of the reaction mechanisms."
Zhang is the first author on the paper that explains the creation and results of this new machine learning model, "Exploring the Frontiers of Chemistry with a General Reactive Machine Learning Potential," which was published in Nature Chemistry on March 7.
Though researchers have simulated reactions before, previous methods had multiple problems. Reactive force field models are relatively common, but they usually require training for specific reaction types. Traditional models that use quantum mechanics, where chemical reactions are simulated based on underlying physics, can be applied to any materials and molecules, but these models require supercomputers to be used.
This new general machine learning interatomic potential (ANI-1xnr) can perform simulations for arbitrary materials containing the elements carbon, hydrogen, nitrogen, and oxygen and requires significantly less computing power and time than traditional quantum mechanics models. According to Olexandr Isayev, associate professor of chemistry at Carnegie Mellon and head of the lab where the model was developed, this breakthrough is due to developments in machine learning.
"Machine learning is emerging as a powerful approach to construct various forms of transferable atomistic potentials utilizing regression algorithms. The overall goal of this project is to develop a machine learning method capable of predicting reaction energetics and rates for chemical processes with high accuracy, but with a very low computational cost," Isayev said. "We have shown that those machine learning models can be trained at high levels of quantum mechanics theory and can successfully predict energies and forces with quantum mechanics accuracy and an increase in speed of as much as 6-7 orders of magnitude. This is a new paradigm in reactive simulations."
Researchers tested ANI-1xnr on different chemical problems, including comparing biofuel additives and tracking methane combustion. They even recreated the Miller experiment, a famous chemical experiment meant to demonstrate how life originated on Earth. Using this experiment, they found that the ANI-1xnr model produced accurate results in condensed-phase systems.
Zhang said that the model could potentially be used for other areas in chemistry with further training.
"We found out it can be potentially used to simulate biochemical processes like enzymatic reactions," Zhang said. "We didn't design it to be used in such a way, but after modification it may be used for that purpose.
In the future, the team plans to refine ANI-1xnr and allow it to work with more elements and in more chemical areas, and they will try to increase the scale of the reactions it can process. This could allow it to be used in multiple fields where designing new chemical reactions could be relevant, such as drug discovery.
- This press release was originally published on the Carnegie Mellon University website
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University of Gttingen: Professor Lutz Ackermann Ranked Among Top Ten Worldwide in Organic Chemistry Research – India Education Diary
From Code to Chemistry: Coscientist, the AI System Mastering Nobel Prize-Winning Reactions – SciTechDaily
Coscientist, an AI developed by Carnegie Mellon University, has autonomously mastered and executed complex Nobel Prize-winning chemical reactions, demonstrating significant potential in enhancing scientific discovery and experimental precision. Its ability to control laboratory robotics marks a major leap in AI-assisted research. Credit: SciTechDaily.com
An AI-based system succeeds in planning and carrying out real-world chemistry experiments, showing the potential to help human scientists make more discoveries, faster.
In less time than it will take you to read this article, an artificial intelligence-driven system was able to autonomously learn about certain Nobel Prize-winning chemical reactions and design a successful laboratory procedure to make them. The AI did all that in just a few minutes and nailed it on the first try.
This is the first time that a non-organic intelligence planned, designed, and executed this complex reaction that was invented by humans, says Carnegie Mellon University chemist and chemical engineer Gabe Gomes, who led the research team that assembled and tested the AI-based system. They dubbed their creation Coscientist.
The most complex reactions Coscientist pulled off are known in organic chemistry as palladium-catalyzed cross couplings, which earned its human inventors the 2010 Nobel Prize for chemistry in recognition of the outsize role those reactions came to play in the pharmaceutical development process and other industries that use finicky, carbon-based molecules.
Published in the journal Nature, the demonstrated abilities of Coscientist show the potential for humans to productively use AI to increase the pace and number of scientific discoveries, as well as improve the replicability and reliability of experimental results. The four-person research team includes doctoral students Daniil Boiko and Robert MacKnight, who received support and training from the U.S. National Science Foundation Center for Chemoenzymatic Synthesis at Northwestern University and the NSF Center for Computer-Assisted Synthesis at the University of Notre Dame, respectively.
An artists conceptual representation of chemistry research conducted by AI. The work was led by Gabe Gomes at Carnegie Mellon University and supported by the U.S. National Science Foundation Centers for Chemical Innovation. Credit: U.S. National Science Foundation
Beyond the chemical synthesis tasks demonstrated by their system, Gomes and his team have successfully synthesized a sort of hyper-efficient lab partner, says NSF Chemistry Division Director David Berkowitz. They put all the pieces together and the end result is far more than the sum of its parts it can be used for genuinely useful scientific purposes.
Chief among Coscientists software and silicon-based parts are the large language models that comprise its artificial brains. A large language model is a type of AI that can extract meaning and patterns from massive amounts of data, including written text contained in documents. Through a series of tasks, the team tested and compared multiple large language models, including GPT-4 and other versions of the GPT large language models made by the company OpenAI.
Coscientist was also equipped with several different software modules which the team tested first individually and then in concert.
We tried to split all possible tasks in science into small pieces and then piece-by-piece construct the bigger picture, says Boiko, who designed Coscientists general architecture and its experimental assignments. In the end, we brought everything together.
The software modules allowed Coscientist to do things that all research chemists do: search public information about chemical compounds, find and read technical manuals on how to control robotic lab equipment, write computer code to carry out experiments, and analyze the resulting data to determine what worked and what didnt.
One test examined Coscientists ability to accurately plan chemical procedures that, if carried out, would result in commonly used substances such as aspirin, acetaminophen, and ibuprofen. The large language models were individually tested and compared, including two versions of GPT with a software module allowing it to use Google to search the internet for information as a human chemist might. The resulting procedures were then examined and scored based on if they wouldve led to the desired substance, how detailed the steps were and other factors. Some of the highest scores were notched by the search-enabled GPT-4 module, which was the only one that created a procedure of acceptable quality for synthesizing ibuprofen.
Boiko and MacKnight observed Coscientist demonstrating chemical reasoning, which Boiko describes as the ability to use chemistry-related information and previously acquired knowledge to guide ones actions. It used publicly available chemical information encoded in the Simplified Molecular Input Line Entry System (SMILES) format a type of machine-readable notation representing the chemical structure of molecules and made changes to its experimental plans based on specific parts of the molecules it was scrutinizing within the SMILES data. This is the best version of chemical reasoning possible, says Boiko.
Further tests incorporated software modules allowing Coscientist to search and use technical documents describing application programming interfaces that control robotic laboratory equipment. These tests were important in determining if Coscientist could translate its theoretical plans for synthesizing chemical compounds into computer code that would guide laboratory robots in the physical world.
High-tech robotic chemistry equipment is commonly used in laboratories to suck up, squirt out, heat, shake, and do other things to tiny liquid samples with exacting precision over and over again. Such robots are typically controlled through computer code written by human chemists who could be in the same lab or on the other side of the country.
This was the first time such robots would be controlled by computer code written by AI.
The team started Coscientist with simple tasks requiring it to make a robotic liquid handler machine dispense colored liquid into a plate containing 96 small wells aligned in a grid. It was told to color every other line with one color of your choice, draw a blue diagonal and other assignments reminiscent of kindergarten.
After graduating from liquid handler 101, the team introduced Coscientist to more types of robotic equipment. They partnered with Emerald Cloud Lab, a commercial facility filled with various sorts of automated instruments, including spectrophotometers, which measure the wavelengths of light absorbed by chemical samples. Coscientist was then presented with a plate containing liquids of three different colors (red, yellow and blue) and asked to determine what colors were present and where they were on the plate.
Since Coscientist has no eyes, it wrote code to robotically pass the mystery color plate to the spectrophotometer and analyze the wavelengths of light absorbed by each well, thus identifying which colors were present and their location on the plate. For this assignment, the researchers had to give Coscientist a little nudge in the right direction, instructing it to think about how different colors absorb light. The AI did the rest.
Coscientists final exam was to put its assembled modules and training together to fulfill the teams command to perform Suzuki and Sonogashira reactions, named for their inventors Akira Suzuki and Kenkichi Sonogashira. Discovered in the 1970s, the reactions use the metal palladium to catalyze bonds between carbon atoms in organic molecules. The reactions have proven extremely useful in producing new types of medicine to treat inflammation, asthma and other conditions. Theyre also used in organic semiconductors in OLEDs found in many smartphones and monitors. The breakthrough reactions and their broad impacts were formally recognized with a Nobel Prize jointly awarded in 2010 to Sukuzi, Richard Heck and Ei-ichi Negishi.
Of course, Coscientist had never attempted these reactions before. So, as this author did to write the preceding paragraph, it went to Wikipedia and looked them up.
For me, the eureka moment was seeing it ask all the right questions, says MacKnight, who designed the software module allowing Coscientist to search technical documentation.
Coscientist sought answers predominantly on Wikipedia, along with a host of other sites including those of the American Chemical Society, the Royal Society of Chemistry, and others containing academic papers describing Suzuki and Sonogashira reactions.
In less than four minutes, Coscientist had designed an accurate procedure for producing the required reactions using chemicals provided by the team. When it sought to carry out its procedure in the physical world with robots, it made a mistake in the code it wrote to control a device that heats and shakes liquid samples. Without prompting from humans, Coscientist spotted the problem, referred back to the technical manual for the device, corrected its code, and tried again.
The results were contained in a few tiny samples of clear liquid. Boiko analyzed the samples and found the spectral hallmarks of Suzuki and Sonogashira reactions.
Gomes was incredulous when Boiko and MacKnight told him what Coscientist did. I thought they were pulling my leg, he recalls. But they were not. They were absolutely not. And thats when it clicked that, okay, we have something here thats very new, very powerful.
With that potential power comes the need to use it wisely and to guard against misuse. Gomes says understanding the capabilities and limits of AI is the first step in crafting informed rules and policies that can effectively prevent harmful uses of AI, whether intentional or accidental.
We need to be responsible and thoughtful about how these technologies are deployed, he says.
Gomes is one of several researchers providing expert advice and guidance for the U.S. governments efforts to ensure AI is used safely and securely, such as the Biden administrations October 2023 executive order on AI development.
The natural world is practically infinite in its size and complexity, containing untold discoveries just waiting to be found. Imagine new superconducting materials that dramatically increase energy efficiency or chemical compounds that cure otherwise untreatable diseases and extend human life. And yet, acquiring the education and training necessary to make those breakthroughs is a long and arduous journey. Becoming a scientist is hard.
Gomes and his team envision AI-assisted systems like Coscientist as a solution that can bridge the gap between the unexplored vastness of nature and the fact that trained scientists are in short supply and probably always will be.
Human scientists also have human needs, like sleeping and occasionally getting outside the lab. Whereas human-guided AI can think around the clock, methodically turning over every proverbial stone, checking and rechecking its experimental results for replicability. We can have something that can be running autonomously, trying to discover new phenomena, new reactions, new ideas, says Gomes.
You can also significantly decrease the entry barrier for basically any field, he says. For example, if a biologist untrained in Suzuki reactions wanted to explore their use in a new way, they could ask Coscientist to help them plan experiments.
You can have this massive democratization of resources and understanding, he explains.
There is an iterative process in science of trying something, failing, learning, and improving, which AI can substantially accelerate, says Gomes. That on its own will be a dramatic change.
For more on this paper, see Carnegie Mellons AI Coscientist Transforms Lab Work.
Reference: Autonomous scientific research capabilities of large language models by Daniil A. Boiko, Robert MacKnight, Ben Kline and Gabe Gomes, 20 December 2023, Nature. DOI: 10.1038/s41586-023-06792-0
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Organic chemistry research transformed: The convergence of automation and AI reshapes scientific exploration – EurekAlert
image:
(A) Appraisal of the research groups diverse inputs in AI applications for organic chemistry. Visualization through (B) research groups and (C) institutes word cloud maps, along with (D) geographical distribution.
Credit: Science China Press
Recently, National Science Openmagazine published online a review article led by Professor Fanyang Mo (School of Materials Science and Engineering, Peking University) and Professor Yuntian Chen (Eastern Institute of Technology, Ningbo). The research team proposed a significant shift towards automation and artificial intelligence (AI) in organic chemistry over the past decade. Furthermore, they introduced an innovative concept: the development of a generative, self-evolving AI chemistry research assistant.
The landscape of research in organic chemistry has undergone profound changes. Data, computing power, and sophisticated algorithms constitute the foundational pillars of AI-driven scientific research. In recent years, the rapid advancements in computing technology, coupled with the iterative enhancement of algorithms, have initiated a series of paradigm shifts in the scientific domain. This has led to a complete overhaul of conventional research methodologies. Organic chemistry, inherently predisposed to creating new substances, is uniquely positioned to thrive in this era of intelligent innovation. Scientists globally are now converging in their efforts to explore and harness the capabilities of artificial intelligence in chemistry, thus igniting the 'artificial intelligence chemistry' movement.
The academic realm is currently at the forefront of a research renaissance in this domain. The future holds great promise for the application of knowledge embedding and knowledge discovery techniques in scientific machine learning. This innovative approach is designed to narrow the gap between existing predictive models and automated experimental platforms, thereby facilitating the development of self-evolving AI chemical research assistants. In the field of organic chemistry, the concept of knowledge discovery through scientific machine learning is unlocking new possibilities. At the heart of this discipline is the understanding of reaction mechanisms, which often involve complex networks of intermediates, transition states, and concurrent reactions. Traditional approaches to deciphering these mechanisms have depended on kinetic studies and isotope labeling. However, merging symbolic mathematics with AI is poised to cast new light on these intricate pathways, potentially transforming both the understanding and teaching of organic chemical reactions.
Furthermore, the aspect of knowledge embedding holds significant importance from an organic chemist's perspective. Organic chemistry is replete with heuristic rules, ranging from Markovnikov's rules for electrophilic addition to Baldwin's rules for ring closures. Embedding these established principles into AI models would ensure that their predictions are not solely data-driven but also resonate with the intuitive understanding of chemists. This integration would yield insights that are both deeper and more aligned with the nuanced perspectives of organic chemistry.
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Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence
https://doi.org/10.1360/nso/20230037
National Science Open
2-Nov-2023
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.
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Organic compounds in asteroids formed in cold areas of space – Tech Explorist
Polycyclic aromatic hydrocarbons (PAHs) contain 20% of the carbon in the interstellar medium. They are potentially produced in circumstellar environments by interstellar clouds or by processing of carbon-rich dust grains.
Scientists studied certain organic compounds, known as polycyclic aromatic hydrocarbons (PAHs), extracted from the Ryugu asteroid and Murchison meteorite. Surprisingly, they discovered these PAHs likely formed in the cold areas of space between stars rather than in hot regions near stars, challenging previous beliefs. This finding has opened up new possibilities for understanding life beyond Earth and the chemistry of celestial objects. The researchers from Curtin University in Australia conducted controlled burnings of plants to produce PAHs as part of this study.
ARC Laureate Fellow John Curtin Distinguished Professor Kliti Grice, director of WA-OIGC, said PAHs are organic compounds of carbon and hydrogen common on Earth but also found in celestial bodies like asteroids and meteorites.
We performed controlled burn experiments on Australian plants, which were isotopically compared to PAHs from fragments of the Ryugu asteroid that were returned to Earth by a Japanese spacecraft in 2020 and the Murchison meteorite that landed in Australia in 1969. The bonds between light and heavy carbon isotopes in the PAHs were analyzed to reveal the temperature at which they were formed,Professor Grice said.
Select PAHs from Ryugu and Murchison were found to have different characteristics: the smaller ones likely formed in cold outer space, while bigger ones probably formed in warmer environments, like near a star or inside a celestial body.
Study co-author Dr Alex Holman, also from WA-OIGC, saidunderstanding the isotopic composition of PAHs helps unravel the conditions and environments in which these molecules were created, offering insights into the history and chemistry of celestial bodies like asteroids and meteorites.
This research gives us valuable insights into how organic compounds form beyond Earth and where they come from in space,Dr Holman said.
The use of high-tech methods and creative experiments has shown that select PAHs on asteroids can be formed in cold space.
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Organic compounds in asteroids formed in cold areas of space - Tech Explorist