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An operationally simple method for generating enantioenriched 2-oxazolidinones from N-Boc amines and monoor trans-disubstituted alkenes via chiral organoselenium catalysis is described. Critical to the success of the transformation was the inclusion of triisopropylsilyl chloride (TIPSCl), likely because it sequestered fluoride generated by the oxidant (N-fluorocollidinium tetrafluoroborate) throughout the reaction and suppressed side reactivity. The scope of both the amine and alkene substrates was explored, generating a variety of 2-oxazolidinones in modest to high yields with high enantioselectivities 

Most molecular diagram parsers recover chemical structure from raster images (e.g., PNGs). However, many PDFs include commands giving explicit locations and shapes for characters, lines, and polygons. We present a new parser that uses these born-digital PDF primitives as input. The parsing model is fast and accurate, and does not require GPUs, Optical Character Recognition (OCR), or vectorization. We use the parser to annotate raster images and then train a new multi-task neural network for recognizing molecules in raster images.We evaluate our parsers using SMILES and standard benchmarks, along with a novel evaluation protocol comparing molecular graphs directly that supports automatic error compilation and reveals errors missed by SMILES-based evaluation. On the synthetic USPTObenchmark, our born-digital parser obtains a recognition rate of 98.4% (1% higher than previous models) and our relatively simple neural parser for raster images obtains a rate of 85% using less training data than existing neural approaches (thousands vs. millions of molecules). 

Abstract Many of the greatest challenges facing society today likely have molecular solutions that await discovery. However, the process of identifying and manufacturing such molecules has remained slow and highly specialist dependent. Interfacing the fields of artificial intelligence (AI) and synthetic organic chemistry has the potential to powerfully address both limitations. The Molecule Maker Lab Institute (MMLI) brings together a team of chemists, engineers, and AI‐experts from the University of Illinois Urbana‐Champaign (UIUC), Pennsylvania State University, and the Rochester Institute of Technology, with the goal of accelerating the discovery, synthesis and manufacture of complex organic molecules. Advanced AI and machine learning (ML) methods are deployed in four key thrusts: (1) AI‐enabled synthesis planning, (2) AI‐enabled catalyst development, (3) AI‐enabled molecule manufacturing, and (4) AI‐enabled molecule discovery. The MMLI's new AI‐enabled synthesis platform integrates chemical and enzymatic catalysis with literature mining and ML to predict the best way to make new molecules with desirable biological and material properties. The MMLI is transforming chemical synthesis and generating use‐inspired AI advances. Simultaneously, the MMLI is also acting as a training ground for the next generation of scientists with combined expertise in chemistry and AI. Outreach efforts aimed toward high school students and the public are being used to show how AI‐enabled tools can help to make chemical synthesis accessible to nonexperts. 

The management and analysis of large in silico molecular libraries is pivotal in many areas of modern chemistry. The adoption and success of data-oriented approaches to chemical research is dependent on the ease of handling large collections of in silico molecular structures in a programmatic way. Herein, we introduce the MOLecular LIibrary toolkit, “molli”, which is a Python 3 chemoinformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation suitable for diversity analysis, and interface to robust three-dimensional visualization tools provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes command-line interface in addition to Python classes to streamline frequently used workflows. This work describes the development and implementation of molli 1.0 and highlights the available functionality. Parallel performance is benchmarked on various hardware platforms and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to NMR prediction workflow from CDXML files. 

Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)–catalyzed carbon-nitrogen (C–N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C–N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows. 

Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)–catalyzed carbon-nitrogen (C–N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C–N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows. 

Abstract Enantioselective diamination of alkenes represents one of the most straightforward methods to access enantioenriched, vicinal diamines, which are not only frequently encountered in biologically active compounds, but also have broad applications in asymmetric synthesis. Although the analogous dihydroxylation of olefins is well-established, the development of enantioselective olefin diamination lags far behind. Nevertheless, several successful methods have been developed that operate by different reaction mechanisms, including a cycloaddition pathway, a two-electron redox pathway, and a radical pathway. This short review summarizes recent advances and identifies limitations, with the aim of inspiring further developments in this area. 1 Introduction 2 Cycloaddition Pathway 3 Two-Electron Redox Pathway 3.1 Pd(0)/Pd(II) Diamination 3.2 Pd(II)/Pd(IV) Diamination 3.3 I(I)/I(III) Diamination 3.4 Se(II)/Se(IV) Diamination 4 One-Electron Radical Pathway 4.1 Cu-Catalyzed Diamination 4.2 Fe-Catalyzed Diamination 5 Summary and Outlook 

Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past is it intrinsically limtied and inefficient.  To address this problem, our laboratory has developed a fully informatics guided workflow to leverage the power of artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and optimization any class of catalyst for any transformation. This approach is mechanistically agnostic, but also serves as a discovery platform to identify high performing catalysts that can be subsequently investigated by with physical organic methods to identify the origins of selectivity.