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ILMAC Basel 2023
Get PDFBasel fusion point of the pharmaceutical and chemical industry: ILMAC covers the entire Swiss market, and the Basel Exhibition Center is the perfect location for the Swiss pharmaceutical and chemical industry. As a user-oriented trade fair, the event is aimed at specialists from over 20 different branches of industry in the pharmaceutical, chemical, biotechnological, food, beverage, cosmetics, and other sectors. Despite the variety, ILMAC is extremely manageable and can be visited in one day
Putting Chemical Knowledge to Work in Machine Learning for Reactivity
Get PDFMachine learning has been used to study chemical reactivity for a long time in fields such as physical organic chemistry, chemometrics and cheminformatics. Recent advances in computer science have resulted in deep neural networks that can learn directly from the molecular structure. Neural networks are a good choice when large amounts of data are available. However, many datasets in chemistry are small, and models utilizing chemical knowledge are required for good performance. Adding chemical knowledge can be achieved either by adding more information about the molecules or by adjusting the model architecture itself. The current method of choice for adding more information is descriptors based on computed quantum-chemical properties. Exciting new research directions show that it is possible to augment deep learning with such descriptors for better performance in the low-data regime. To modify the models, differentiable programming enables seamless merging of neural networks with mathematical models from chemistry and physics. The resulting methods are also more data-efficient and make better predictions for molecules that are different from the initial dataset on which they were trained. Application of these chemistry-informed machine learning methods promise to accelerate research in fields such as drug design, materials design, catalysis and reactivity
Tools for Synthesis Planning, Automation, and Analytical Data Analysis
Get PDFComputer-aided synthesis design, automation, and analytics assisted by machine learning are promising resources in the researcher’s toolkit. Each component may alleviate the chemist from routine tasks, provide valuable insights from data, and enable more informed experimental design. Herein, we highlight selected works in the field and discuss the different approaches and the problems to which they may apply. We emphasize that there are currently few tools with a low barrier of entry for non-experts, which may limit widespread integration into the researcher’s workflow
Education in Flow Chemistry
Get PDFFlow chemistry is a growing and promising technology that can be used in research, development, and production. Nowadays, properly trained staff in flow chemistry is lacking in industry. To efficiently work with this technology, a mix of engineering and chemical skills is required. Although, this dual education is well addressed in the chemistry major given at the Haute Ecole d’Ingénierie et d’Architecture de Fribourg, a school of the University of Applied Sciences and Arts, Western Switzerland, the teaching in flow chemistry should be enhanced and reinforced
Uncovering the Untapped Potential of the Use of Sodium Amides for Regioselective Arene Functionalisation
Get PDFAlkali-metal amides have become key reagents in synthetic chemistry, with special focus in deprotonation reactions. Despite the higher reactivity found in the heavier sodium and potassium amides, their insolubility and low stability has favoured the use of the more soluble lithium analogues, converting them into the most used non-nucleophilic bases. Studying the coordination effects of Lewis donor molecules such as tridentate amine PMDETA (N,N,N’,N’’,N’’-pentamethyldiethylenetriamine) in combination with the sodium amide NaTMP (TMP = 2,2’,6,6’-tetramethylpiperidide), we have been able to unlock the use of these reagents for the functionalisation of arenes, i.e. the deuterium incorporation by hydrogen isotope exchange and the deprotonative borylation of unactivated arenes. These findings show how sodium amides are not just a simple more sustainable replacement of their lithium counterparts, but also that they can display significantly enhanced reactivities allowing for the development of new transformations
Covalency-enabled Drug Discovery is Redefining Druggability: Medicinal Chemistry and Chemical Biology Highlights
No full textFuelling the Digital Chemistry Revolution with Language Models: Sandmeyer Award 2022
No full textThe RXN for Chemistry project, initiated by IBM Research Europe – Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This research adopts an innovative concept by treating chemical reaction data as language records, treating the prediction of a synthetic organic chemistry reaction as a translation task between precursor and product languages. Over the years, the IBM Research team has successfully developed language models for various applications including forward reaction prediction, retrosynthesis, reaction classification, atom-mapping, procedure extraction from text, inference of experimental protocols and its use in programming commercial automation hardware to implement an autonomous chemical laboratory. Furthermore, the project has recently incorporated biochemical data in training models for greener and more sustainable chemical reactions. The remarkable ease of constructing prediction models and continually enhancing them through data augmentation with minimal human intervention has led to the widespread adoption of language model technologies, facilitating the digitalization of chemistry in diverse industrial sectors such as pharmaceuticals and chemical manufacturing. This manuscript provides a concise overview of the scientific components that contributed to the prestigious Sandmeyer Award in 202
