Chemistry Discovery Research Articles

Covalent Organic Frameworks: Chemical Approaches to Designer Structures and Built-In Functions.

A new approach has been developed to design organic polymers using topology diagrams. This strategy enables covalent integration of organic units into ordered topologies and creates a new polymer form, that is, covalent organic frameworks. This is a breakthrough in chemistry because it sets a molecular platform for synthesizing polymers with predesignable primary and high-order structures, which has been a central aim for over a century but unattainable with traditional design principles. This new field has its own features that are distinct from conventional polymers. This Review summarizes the fundamentals as well as major progress by focusing on the chemistry used to design structures, including the principles, synthetic strategies, and control methods. We scrutinize built-in functions that are specific to the structures by revealing various interplays and mechanisms involved in the expression of function. We propose major fundamental issues to be addressed in chemistry as well as future directions from physics, materials, and application perspectives.

Reaction-Based Luminescent Probes for Reactive Sulfur, Oxygen, and Nitrogen Species: Analytical Techniques and Recent Progress.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTReaction-Based Luminescent Probes for Reactive Sulfur, Oxygen, and Nitrogen Species: Analytical Techniques and Recent ProgressBriley J. BeznerBriley J. BeznerDepartment of Chemistry, Southern Methodist University, Dallas, Texas 75205-0314, United StatesMore by Briley J. BeznerView Biography, Lucas S. RyanLucas S. RyanDepartment of Chemistry, Southern Methodist University, Dallas, Texas 75205-0314, United StatesMore by Lucas S. RyanView Biography, and Alexander R. Lippert*Alexander R. LippertDepartment of Chemistry and Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205-0314, United States*E-mail: [email protected]More by Alexander R. LippertView Biographyhttp://orcid.org/0000-0003-4396-0848Cite this: Anal. Chem. 2020, 92, 1, 309–326Publication Date (Web):November 3, 2019Publication History Published online4 November 2019Published inissue 7 January 2020https://doi.org/10.1021/acs.analchem.9b04990Copyright © 2019 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views3948Altmetric-Citations72LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (2 MB) Get e-AlertsSUBJECTS:Anions,Hydrogen sulfide,Nitrogen,Probes,Sulfur Get e-Alerts

Lilly Research Award Program (LRAP): A Successful Academia-Industry Partnership Model in the Context of Flow Chemistry for Drug Discovery.

The Lilly Research Award Program (LRAP) provides academic researchers worldwide with a gate to partner with Lilly internal scientists who are working on basic and applied research to collaboratively advance novel impactful projects. The pre-competitive nature of these projects is the most relevant feature as it permits the shared publication of the research outcomes immediately. In this article, this highly successful initiative is reviewed in the context of general academia-industry collaborations and the lessons learned from different shared projects, in the area of innovative continuous flow chemistry, will be discussed.

Organotypic and Microphysiological Models of Liver, Gut, and Kidney for Studies of Drug Metabolism, Pharmacokinetics, and Toxicity.

Despite extensive breakthroughs in chemistry, molecular biology, and genetics in the last decades, the success rates of drug development projects remain low. To improve predictions of clinical efficacy and safety of new compounds, a plethora of 3D culture methods of human cells have been developed in which the cultured cells retain physiologically and functionally relevant phenotypes for multiple weeks. Here, we critically review current paradigms for organotypic cultures of human liver, gut, and kidney such as perfused microchips, spheroids, and hollow fiber bioreactors and discuss their utility for understanding drug pharmacokinetics, metabolism, and toxicity. Furthermore, bioprinting and the microfluidic integration of different tissue models to mimic systemic drug effects are highlighted as promising technological trends. In the last part of the review, we discuss important considerations regarding the choice of culture substratum material to limit adverse effects such as drug absorption while facilitating the phenotypic maintenance of cultured cells. We conclude that recent advances in organotypic and microphysiological culture models of human tissues can improve drug development and contribute to an amelioration of clinical attrition rates. However, further validation, benchmarking, and consolidation efforts are needed to achieve more widespread dissemination and eventually regulatory acceptance of these novel tools.

Modular Sulfondiimine Synthesis Using a Stable Sulfinylamine Reagent.

Sulfondiimines—the double aza-analogues of sulfones—hold significant potential as leads in discovery chemistry, yet their application in this arena has been held back by the scarcity of appropriate synthetic routes. Existing methods employ sulfides as substrates, and rely on consecutive imination reactions using the hazardous reagent O-mesitylenesulfonyl hydroxylamine. Here we report a method for sulfondiimine synthesis that does not begin with a sulfide or a thiol, and instead employs two Grignard reagents and a bespoke sulfinylamine (R—N=S=O) reagent as starting materials. Lewis acid-mediated assembly of these three components provides efficient access to a series of sulfilimine intermediates. A novel rhodium-catalyzed imination of these electron-rich sulfilimines then delivers a varied range of sulfondiimines featuring orthogonal N-functionalization. Conditions for the selective manipulation of both N-atoms of the sulfondiimines are reported, allowing access to a broad range of mono- and difunctionalized products. The oxidation of the sulfilimine intermediates is also described, and provides a complementary route to sulfoximines.

A Bounty of New Challenging Targets in Oncology for Chemical Discovery

A Bounty of New Challenging Targets in Oncology for Chemical Discovery

Fluorine-containing substituents: metabolism of the α,α-difluoroethyl thioether motif.

We report the metabolism of the recently introduced α,α-difluoroethyl thioether motif to explore further its potential as a substituent for bioactives discovery chemistry. Incubation of two aryl–SCF2CH3 ethers with the model yeast organism Cunninghamella elegans, indicates that the sulfur of the thioether is rapidly converted to the corresponding sulfoxide, and then significantly more slowly to the sulfone. When the substrate was (p-OMe)PhSCF2CH3, then the resultant (demethylated) phenol sulfoxide had an enantiomeric excess of 60%, and when the substrate was the β-substituted-SCF2CH3 naphthalene, then the enantiomeric excess of the resultant sulfoxide was 54%. There was no evidence of defluorination, unlike the corresponding oxygen ether (p-OMe)PhOCF2CH3, which was converted to the (demethylated) phenol acetate ester during C. elegans incubation. We conclude that the aryl–S–CF2CH3 motif is metabolised in a similar manner to aryl–SCF3, a motif that is being widely explored in discovery chemistry. It is however, significantly less lipophilic than aryl-SCF3 which may offer a practical advantage in tuning overall pharmacokinetic profiles of molecules in development.

Linking biosynthetic and chemical space to accelerate microbial secondary metabolite discovery.

ABSTRACTSecondary metabolites can be viewed as a chemical language, facilitating communication between microorganisms. From an ecological point of view, this metabolite exchange is in constant flux due to evolutionary and environmental pressures. From a biomedical perspective, the chemistry is unsurpassed for its antibiotic properties. Genome sequencing of microorganisms has revealed a large reservoir of Biosynthetic Gene Clusters (BGCs); however, linking these to the secondary metabolites they encode is currently a major bottleneck to chemical discovery. This linking of genes to metabolites with experimental validation will aid the elicitation of silent or cryptic (not expressed under normal laboratory conditions) BGCs. As a result, this will accelerate chemical dereplication, our understanding of gene transcription and provide a comprehensive resource for synthetic biology. This will ultimately provide an improved understanding of both the biosynthetic and chemical space. In recent years, integrating these complex metabolomic and genomic data sets has been achieved using a spectrum of manual and automated approaches. In this review, we cover examples of these approaches, while addressing current challenges and future directions in linking these data sets.

Chemists quantify the rate of historical chemical discovery

Scientists don’t know the limits of chemical space—the collection of all possible molecules—but a new analysis concludes that chemists have discovered new compounds in that space at an exponential rate over the past 200 years (Proc. Natl. Acad. Sci. U.S.A. 2019, DOI: 10.1073/pnas.1816039116). Guillermo Restrepo of Max Planck Institute for Mathematics in the Sciences and colleagues used the Reaxys database to generate a list of more than 14 million compounds reported in scientific journals and patents between 1800 and 2015. Plotting the number of molecules reported each year revealed that new compound discoveries grew 4.4% annually. The paper “provides what is bound to be a useful historical tool,” says Ronald Brashear, director of the Donald F. and Mildred Topp Othmer Library of Chemical History at the Science History Institute. Brashear says this computational approach to history is a valuable complement to other methods used by historians. In addition to observing

Enabling chiral separations in discovery chemistry with open-access chiral supercritical fluid chromatography.

Work from this paper details a novel walk-up open-access (OA) approach to enable chiral analytical method development and preparative separation of enantiomers in early discovery chemistry using supercritical fluid chromatography (SFC). We have demonstrated the success of this OA approach using immobilized chiral stationary phases (CSPs). After screening a diverse set of racemic drug candidates, we have concluded that a simplified OA chiral SFC platform can successfully purify approximately 60% of the analysed racemates. This streamlined OA workflow enables medicinal chemists with limited expertise in chiral method development to successfully and rapidly purify enantiomers for their projects using Waters UPC2 and Prep100-SFC instrumentation.

Drug candidates disclosed in Orlando

As the ACS national meeting wound down, medicinal chemists flocked to a massive ballroom to hear the stories of eight potential drugs at the start of their likely years-long journey through the clinic to one day reach patients—or so drugmakers hope. Always a highly anticipated event, the “First-Time Disclosure of Clinical Candidates” session rewards curious chemists with glimpses of drug-compound structures not previously made public. This year’s session was organized by Erin DiMauro, Merck & Co.’s executive director of discovery chemistry. Half of the talks in the lineup revealed immuno-oncology compounds that enlist humans’ immune system to combat tumors. Other presenters discussed drug-development programs tackling diabetes, resistant bacterial infections, and Parkinson’s disease. Read our full story online to find out more about these drug candidates, including their structures at cenm.ag/drugdisclosuresorlando.

Beyond Reductionist Thinking in Chemistry for Sustainability

Great breakthroughs in chemistry over the past two centuries have been accomplished largely through reductionist methods. However, the incredible sustainability challenges that we face as a civilization are systems challenges, requiring careful combination of knowledge gained from reductionist approaches with integrative-systems thinking to inform designs for a sustainable future.

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.

High-throughput computational screening for chemical discovery mandates the automated and unsupervised simulation of thousands of new molecules and materials. In challenging materials spaces, such as open shell transition metal chemistry, characterization requires time-consuming first-principles simulation that often necessitates human intervention. These calculations can frequently lead to a null result, e.g., the calculation does not converge or the molecule does not stay intact during a geometry optimization. To overcome this challenge toward realizing fully automated chemical discovery in transition metal chemistry, we have developed the first machine learning models that predict the likelihood of successful simulation outcomes. We train support vector machine and artificial neural network classifiers to predict simulation outcomes (i.e., geometry optimization result and degree of ⟨ S2⟩ deviation) for a chosen electronic structure method based on chemical composition. For these static models, we achieve an area under the curve of at least 0.95, minimizing computational time spent on nonproductive simulations and therefore enabling efficient chemical space exploration. We introduce a metric of model uncertainty based on the distribution of points in the latent space to systematically improve model prediction confidence. In a complementary approach, we train a convolutional neural network classification model on simulation output electronic and geometric structure time series data. This dynamic model generalizes more readily than the static classifier by becoming more predictive as input simulation length increases. Finally, we describe approaches for using these models to enable autonomous job control in transition metal complex discovery.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.

A Toolbox for the Identification of Modes of Action of Natural Products.

Natural products have long played a leading role as direct source of drugs or as a means to inspire informed molecular design. Indeed, natural products have been biologically prevalidated as protein-binding motifs by millions of years of evolutionary pressure. Despite the tailored architectures, and the ever-growing chemistry toolbox to aid access such privileged structures, identifying the modes of action by which these molecules can be harnessed as therapeutics remains a major bottleneck in discovery chemistry. Herein, an overview of cheminformatics methods applied to the identification of modes of action of natural products is given, and a discussion of successful case studies is provided. A special focus is given to machine learning methods that may help to streamline the development of natural products into drug leads.

A quantitative uncertainty metric controls error in neural network-driven chemical discovery.

Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale chemical space exploration can only be realized if it is straightforward to identify when molecules and materials are outside the model's domain of applicability. Established uncertainty metrics for neural network models are either costly to obtain (e.g., ensemble models) or rely on feature engineering (e.g., feature space distances), and each has limitations in estimating prediction errors for chemical space exploration. We introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry. The calibrated performance of this approach exceeds widely used uncertainty metrics and is readily applied to models of increasing complexity at no additional cost. Tightening latent distance cutoffs systematically drives down predicted model errors below training errors, thus enabling predictive error control in chemical discovery or identification of useful data points for active learning.

Catalyst: The Metaphysics of Chemical Reactivity

Leroy (Lee) Cronin has been thinking about programming chemistry since he was 8 years old, when he got his first computer and chemistry set. He is now the Regius Professor of Chemistry at the University of Glasgow. He and his team are trying to make artificial life forms, find alien life, explore the digitization of chemistry, and construct chemical computers. S. Hessam M. Mehr is a post-doctoral researcher at the University of Glasgow. He earned his PhD at the University of British Columbia (2017). His research focuses on probabilistic and information-theoretic methods in chemistry and the discovery of chemical novelty. Jaroslaw M. Granda is a postdoctoral research associate at the University of Glasgow. He obtained his PhD at the Institute of Organic Chemistry of the Polish Academy of Sciences (2014). His research interests focus on the automation of organic synthesis and artificial-intelligence-driven systems for chemical discovery.

Mathematical structural descriptors and mutagenicity assessment: a study with congeneric and diverse datasets$

ABSTRACTQuantitative bioactivity and toxicity assessment of chemical compounds plays a central role in drug discovery as it saves a substantial amount of resources. To this end, high-performance computing has enabled researchers and practitioners to leverage hundreds, or even thousands, of computed molecular descriptors for the activity prediction of candidate compounds. In this paper, we evaluate the utility of two large groups of chemical descriptors by such predictive modelling, as well as chemical structure discovery, through empirical analysis. We use a suite of commercially available and in-house software to calculate molecular descriptors for two sets of chemical mutagens – a homogeneous set of 95 amines, and a diverse set of 508 chemicals. Using calculated descriptors, we model the mutagenic activity of these compounds using a number of methods from the statistics and machine-learning literature, and use robust principal component analysis to investigate the low-dimensional subspaces that characterize these chemicals. Our results suggest that combining different sets of descriptors is likely to result in a better predictive model – but that depends on the compounds being modelled and the modelling technique being used.

Direct Copper-Catalyzed Three-Component Synthesis of Sulfonamides

First introduced into medicines in the 1930s, the sulfonamide functional group continues to be present in a wide range of contemporary pharmaceuticals and agrochemicals. Despite their popularity in the design of modern bioactive molecules, the underpinning methods for sulfonamide synthesis are essentially unchanged since their introduction, and rely on the use of starting materials with preinstalled sulfur-functionality. Herein we report a direct single-step synthesis of sulfonamides that combines two of the largest monomer sets available in discovery chemistry, (hetero)aryl boronic acids and amines, along with sulfur dioxide, using a Cu(II) catalyst, to deliver a broad range of sulfonamides. Sulfur dioxide is provided by the surrogate reagent DABSO. The reaction tolerates broad variation in both coupling partners, including aryl, heteroaryl and alkenyl boronic acids, as well as cyclic and acyclic alkyl secondary amines, and primary anilines. We validate the method by showing that a variety of drugs, and drug-fragments, can be incorporated into the process.

Global medicinal chemistry and GPCR conference: interview with Stevan Djuric.

Stevan Djuric speaks to Benjamin Walden, Commissioning Editor. Stevan Djuric is head of the global Medicinal Chemistry Leadership Team at AbbVie and is also Vice President of the Discovery Chemistry and Technology organization within their Discovery organization and chemistry outsourcing activities. He spoke at the Global-Medicinal-Chemistry and GPCR summit on the imperative to develop chemistry related technology that can reduce cycle time, cost of goods and improve probability of success. To this end, he discussed his efforts in the chemistry technology area with a focus on integrated synthesis-purification bioassay, and flow photochemistry and high temperature chemistry platforms.