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Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery:https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available athttps://phenaid.ardigen.com/jumpcpexplorer/.

This chapter reviews recent developments in the use of three‐dimensional (3D) printing or additive manufacturing (AM) to chemical research, process, and analytical chemistry. 3D printing has the potential to revolutionize manufacturing by allowing designs and fast prototyping that would not be feasible with the use of other techniques [1]. Furthermore, it enables engineers and scientists to rapidly close the loop on design, manufacture, and application. These aspects are of particular relevance to process chemistry, where flexibility in reactor design and integration of real‐time analytics are often limiting factors. The application of 3D printing to chemical processes opens a new research space in the interface between chemistry and engineering, reported for the first time by Cronin and co‐workers [2]. An overview of the state of the art and scope for the future of this AM technique will be detailed here.

A configurable platform for synthetic chemistry incorporating an in-line benchtop NMR that is capable of monitoring and controlling organic reactions in real-time is presented. The platform is controlled via a modular LabView software control system for the hardware, NMR, data analysis and feedback optimization. Using this platform we report the real-time advanced structural characterization of reaction mixtures, including 19F, 13C, DEPT, 2D NMR spectroscopy (COSY, HSQC and 19F-COSY) for the first time. Finally, the potential of this technique is demonstrated through the optimization of a catalytic organic reaction in real-time, showing its applicability to self-optimizing systems using criteria such as stereoselectivity, multi-nuclear measurements or 2D correlations.

The discovery of chemical reactions is an inherently unpredictable and time-consuming process1. An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy2. Reaction prediction based on high-level quantum chemical methods is complex3, even for simple molecules. Although machine learning is powerful for data analysis4,5, its applications in chemistry are still being developed6. Inspired by strategies based on chemists’ intuition7, we propose that a reaction system controlled by a machine learning algorithm may be able to explore the space of chemical reactions quickly, especially if trained by an expert8. Here we present an organic synthesis robot that can perform chemical reactions and analysis faster than they can be performed manually, as well as predict the reactivity of possible reagent combinations after conducting a small number of experiments, thus effectively navigating chemical reaction space. By using machine learning for decision making, enabled by binary encoding of the chemical inputs, the reactions can be assessed in real time using nuclear magnetic resonance and infrared spectroscopy. The machine learning system was able to predict the reactivity of about 1,000 reaction combinations with accuracy greater than 80 per cent after considering the outcomes of slightly over 10 per cent of the dataset. This approach was also used to calculate the reactivity of published datasets. Further, by using real-time data from our robot, these predictions were followed up manually by a chemist, leading to the discovery of four reactions.

Change history: Owing to the misidentification of compound 22 in the original Letter, changes have been made to Fig. 5, Extended Data Fig. 2 and the main text; see accompanying Amendment.

We use two 3D-printing platforms as solid- and liquid-handling fabricators, producing sealed reactionware for chemical synthesis with the reagents, catalysts and purification apparatus integrated into monolithic devices. Using this reactionware, a multi-step reaction sequence was performed by simply rotating the device so that the reaction mixture flowed through successive environments under gravity, without the need for any pumps or liquid-handling prior to product retrieval from the reactionware in a pure form.

p { margin-bottom: 0.21cm; } Hundreds of tonnes of hazardous waste are released to the air, water, and land by industry every hour of every day. The chemical industry is the biggest source of such waste and the number of agent considered toxic is continuosly increasing due also to a series of more restrictive laws and regulamentations (REACH).The term Green Chemistry was coined in the 1990s, to bring focus to an increasing interest in developing more environmentally friendly chemical processes and products. In this term the Green Chemistry represents the most concrete answer of the scientific community to the pressing enviromental needs and sustainability. There is a pool of clean technologies that are becoming widely studied or used from wich catalysis is a well established one, well proven at the largest volume end of the chemicals industry. Green Chemistry begin by design and design derive from inspiration. Nature is the biggest chemical laboratory in the world and produces , every days tonnes of chemicals in absolutely eco-friendly and sustainable way. The secret of natural chemistry are the enzymes, "Why don\'t take ispiration from the enzyme to setup new green chemical processes. An example of oxidative reactions inspired by the enzyme Gluatione Peroxidase will be discussed to highlight the reduction of produced waste, the reduction of energy requirement and the mild and ecofriendly confitions used.

Selenium-based reagents bear a high potential for the improvement of known reactions not only from an environmental and pharmaceutical point of view, but also as interesting reagents for the development of, completely new synthetic transformations and as potential ligands in catalytic reactions. A variety of organoselenium compounds have been proven to be useful for organic synthesis over several decades. Organoselenium species can be introduced as either nucleophiles or electrophiles to other organic molecules, producing useful intermediates for organic synthesis. Optically active organoselenium derivatives and their application to highly selective asymmetric synthesis are also of current interest. Probably the most interesting aspect, which emerged in recent years, concerns the possibility of effecting some functional group conversions using catalytic amounts of the selenium reagent or using selenium containing compounds as chiral ligands in metal catalyzed reactions. The developments of all these catalytic processes represent the most important results which have been reported recently in this field and their conceptual and synthetic relevance considerably increases the use of some organoselenium derivatives as Green Catalysts. [1] In consideration that in Nature, the main biological function of selenium is associated with its incorporation in the form of selenocysteine (Sec) into certain proteins having redox motifs different selenium containing compounds can be investigated as bio-inspired catalysts in the carbon-carbon multiple bond oxidation mediated by H2O2 in water. These catalysts resulted to be also interesting good GPx-mimetics. Acknowledgement: Financial support from M.I.U.R.-PRIN2007, Consorzio CINMPIS, Bari, University of Perugia, the grant "British-Italian Partnership" from the British Council/CRUI. References [1] a) D. M. Freudendahl, S. Santoro, S. A. Shahzad, C. Santi, T. Wirth Angew. Chem. Int. Ed.2009, 8, 8409.b) C. Santi, M. Tiecco, L. Testaferri, C. Tomassini, S. Santoro, G. Bizzoca Phosphorus Sulfur Silicon Relat. Elem. 2008,183, 956. c) S. Santoro, C. Santi, M. Sabatini, L. Testaferri, M. Tiecco Adv.Synth. Catal. 2008, 350, 2881. d) S. Santoro, B. Battistelli, B. Gjoka, C-w.S.Si, L.Testaferri, M. Tiecco, C. Santi Synlett, 2010, 1402.