Applications of combinatorial technologies to drug discovery. 2. Combinatorial organic synthesis, library screening strategies, and future directions

Dedenser: A Python Package for Clustering and Downsampling Chemical Libraries

The screening of chemical libraries is an essential starting point in the drug discovery process. While some researchers desire a more thorough screening of drug targets against a narrower scope of molecules, it is not uncommon for diverse screening sets to be favored during the early stages of drug discovery. However, a cost burden is associated with the screening of molecules, with potential drawbacks if particular areas of chemical space are needlessly overrepresented. To facilitate triaged sampling of chemical libraries and other collections of molecules, we have developed Dedenser, a tool for the downsampling of chemical clusters. Dedenser functions by reducing the membership of clusters within chemical point clouds while maintaining the initial topology or distribution in chemical space. Dedenser is a Python package that utilizes Hierarchical Density-Based Spatial Clustering of Applications with Noise to first identify clusters present in 3D chemical point clouds and then downsamples by applying Poisson disk sampling to clusters based on either their volume or density in chemical space. A command line interface tool and graphic user interface are available with Dedenser, which allow for the generation of chemical point clouds, using Mordred for QSAR descriptor calculations and uniform manifold approximation and projection for 3D embedding, as well as visualization. We hope that Dedenser will serve the community by enabling quick access to reduced collections of molecules that are representative of larger sets and selecting even distributions of molecules within clusters rather than single representative molecules from clusters. All code for Dedenser is open source and available at https://github.com/MSDLLCpapers/dedenser.

Perspectives on current approaches to virtual screening in drug discovery

INTRODUCTION

For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods.

AREAS COVERED

The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS.

EXPERT OPINION

The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.

Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research

Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.

Reductive N-Alkylation of Amines with Ketones Using Heterogeneous Polysilane-Palladium Catalysts under Continuous-Flow Conditions

This work reports a continuous-flow reductive N-alkylation of amines with ketones using molecular hydrogen. The reaction, performed with highly active polysilane-modified heterogeneous palladium catalysts, enables the efficient synthesis of diversely substituted amines under mild flow conditions. The developed catalyst exhibits sustained activity for 5 days (turnover number of >2400). Moreover, the utility of the method is demonstrated by the synthesis of a key intermediate of the active pharmaceutical ingredient teneligliptin.

Regioselective Transition Metal-Free Catalytic Ring Opening of 2H-Azirines by Phenols and Naphthols; One-Pot Access to Benzo- and Naphthofurans

Benzofuran and naphthofuran derivatives are synthesized from readily available phenols and naphthols. Regioselective ring openings of 2H-azirine followed by in situ aromatization using a catalytic amount of Brønsted acid have established the novelty of the methodology. The involvement of a series of 2H-azirines with a variety of phenols, 1-naphthols, and 2-naphthols showed the generality of the protocol. In-depth density functional theory calculations revealed the reaction mechanism with the energies of the intermediates and transition states of a model reaction. An alternate pathway of the mechanism has also been proposed with computer modeling.

The Discovery of Novel Antimicrobial Agents through the Application of Isocyanide-Based Multicomponent Reactions

Multicomponent reactions (MCR) have been used to synthesize a wide range of analogs from several classes of heterocyclic compounds, with multifaceted medicinal uses. The synthesis of highly functionalized molecules in a single pot is a unique property of MCR, allowing researchers to quickly assemble libraries of compounds of biological interest and uncover novel leads as possible therapeutic agents. Isocyanide-based multicomponent reactions have proven to be extremely effective at swiftly specifying members of compound libraries, particularly in the discovery of drugs. The understanding of structure-activity correlations that drive the development of new goods and technology requires structural variety in these libraries. In today's world, antibiotic resistance is a major ongoing problem that poses risks to public health. The implementation of isocyanide-based multicomponent reactions upholds a significant potential in this regard. By utilizing such reactions, new antimicrobial compounds can be discovered and subsequently used to fight against such concerns. This study discusses the recent developments in antimicrobial medication discovery using isocyanide-based multicomponent reactions (IMCRs). Furthermore, the article emphasizes the potential of IMCRs (Isocyanide-based multicomponent based reactions) in the near future.

NRF2 Activation by Nitrogen Heterocycles: A Review

Several nitrogen heterocyclic analogues have been applied to clinical practice, and about 75% of drugs approved by the FDA contain at least a heterocyclic moiety. Thus, nitrogen heterocycles are beneficial scaffolds that occupy a central position in the development of new drugs. The fact that certain nitrogen heterocyclic compounds significantly activate the NRF2/ARE signaling pathway and upregulate the expression of NRF2-dependent genes, especially HO-1 and NQO1, underscores the need to study the roles and pharmacological effects of N-based heterocyclic moieties in NRF2 activation. Furthermore, nitrogen heterocycles exhibit significant antioxidant and anti-inflammatory activities. NRF2-activating molecules have been of tremendous research interest in recent times due to their therapeutic roles in neuroinflammation and oxidative stress-mediated diseases. A comprehensive review of the NRF2-inducing activities of N-based heterocycles and their derivatives will broaden their therapeutic prospects in a wide range of diseases. Thus, the present review, as the first of its kind, provides an overview of the roles and effects of nitrogen heterocyclic moieties in the activation of the NRF2 signaling pathway underpinning their antioxidant and anti-inflammatory actions in several diseases, their pharmacological properties and structural-activity relationship are also discussed with the aim of making new discoveries that will stimulate innovative research in this area.

Three Component Cascade Reaction of Cyclohexanones, Aryl Amines, and Benzoylmethylene Malonates: Cooperative Enamine-Brønsted Acid Approach to Tetrahydroindoles

A three-component cascade reaction comprising cyclic ketones, arylamines, and benzoylmethylene malonates has been developed to access 4,5,6,7-tetrahydro-1H-indoles. The reaction was achieved through cooperative enamine-Brønsted catalysis in high yields with wide substrate scopes. Mechanistic studies identified the role of the Brønsted acid catalyst and revealed the formation of an imine intermediate, which was confirmed by X-ray crystallography.

Caloxin-derived peptides for the inhibition of plasma membrane calcium ATPases

Plasma membrane calcium ATPases (PMCAs) are a family of transmembrane proteins responsible for the extrusion of cytosolic Ca²⁺ to the extracellular milieu. They are important players of the calcium homeostasis possibly implicated in some important diseases. The reference inhibitors of PMCA extruding activity are on one hand ortho-vanadate (IC₅₀ in the 30 mM range), and on the other a series of 12- to 20-mer peptides named caloxins (IC₅₀ in the 100 µM scale). As for all integral membrane proteins, biochemistry and pharmacology are difficult to study on isolated and/or purified proteins. Using a series of reference blockers, we assessed a pharmacological window with which we could study the functionality of PMCAs in living cells. Using this system, we screened for alternative versions of caloxins, aiming at shortening the peptide backbone, introducing non-natural amino acids, and overall trying to get a glimpse at the structure-activity relationship between those new peptides and the protein in a cellular context. We describe a short series of equipotent 5-residue long analogues with IC₅₀ in the low µM range.

Pyrazole Scaffolds: Centrality in Anti-Inflammatory and Antiviral Drug Design

BACKGROUND

Pyrazole is a component of a diversity of bioactive heterocyclic congeners with a broad-spectrum range of biological and pharmacological uses. Designing novel pyrazole and its analogues, revealing new routes for synthesizing this nucleus, exploring various potencies of that heterocycles, and looking for possible applications of pyrazoles are all becoming more important due to their numerous potential applications.

OBJECTIVES

Pyrazole scaffolds have been proven to be successful as anti-viral and anti-inflammatory therapeutic against multiple targets like HSV-1, NNRTI, H1N1, CoX-1, and CoX-2. Due to this miscellany in the biotic area, this moiety has engrossed the consideration of many scientists to study chemistry and pharmacological profile.

RESULTS

The review encompasses pyrazole having various scaffolds with multiple biological activities and attempts have also been made to correlate their structure-activity relationship. Multiple pyrazole correspondents have been synthesized as lead molecules and performed valuation for their actions.

CONCLUSION

The incorporation of pyrazole with other pharmacophores in the molecule might lead to novel potent therapeutic agents that will further help in designing potent lead molecules.

Large-Scale Dried Reagent Reconstitution and Diffusion Control Using Microfluidic Self-Coalescence Modules

The positioning and manipulation of large numbers of reagents in small aliquots are paramount to many fields in chemistry and the life sciences, such as combinatorial screening, enzyme activity assays, and point-of-care testing. Here, a capillary microfluidic architecture based on self-coalescence modules capable of storing thousands of dried reagent spots per square centimeter is reported, which can all be reconstituted independently without dispersion using a single pipetting step and ≤5 μL of a solution. A simple diffusion-based mathematical model is also provided to guide the spotting of reagents in this microfluidic architecture at the experimental design stage to enable either compartmentalization, mixing, or the generation of complex multi-reagent chemical patterns. Results demonstrate the formation of chemical patterns with high accuracy and versatility, and simple methods for integrating reagents and imaging the resulting chemical patterns.

Marine Demospongiae: A Challenging Treasure of Bioactive Compounds

In the last decades, it has been demonstrated that marine organisms are a substantial source of bioactive compounds with possible biotechnological applications. Marine sponges, in particular those belonging to the class of Demospongiae, have been considered among the most interesting invertebrates for their biotechnological potential. In this review, particular attention is devoted to natural compounds/extracts isolated from Demospongiae and their associated microorganisms with important biological activities for pharmacological applications such as antiviral, anticancer, antifouling, antimicrobial, antiplasmodial, antifungal and antioxidant. The data here presented show that this class of sponges is an exciting source of compounds, which are worth developing into new drugs, such as avarol, a hydroquinone isolated from the marine sponge Disidea avara, which is used as an antitumor, antimicrobial and antiviral drug.

A Molecular Insight into Pyrazole Congeners as Antimicrobial, Anticancer, and Antimalarial Agents

BACKGROUND

Pyrazole is a bioactive heterocyclic congener found in a wide range of biological and pharmacological applications. Due to their multiple prospective uses, developing innovative pyrazoles and analogues, disclosing revolutionary ways for synthesizing this nucleus, investigating diverse potencies of that heterocycle, and seeking for possible applications of pyrazoles are all growing more significant Objectives: Pyrazole scaffolds have been proven to be successful as antimicrobial, anticancer, antimalarial therapeutic against multiple targets like DNA gyrase, topoisomerase IV, Hsp90, and several kinase enzymes. Its moiety has absorbed the attention of many scientists to research chemical and pharmacological profile due to this miscellany in the biotic region.

RESULTS

The review covers pyrazole scaffolds with a variety of biological functions, as well as attempts to connect the structure-activity relationship. Multiple pyrazole analogues have been produced as lead compounds, and their activities have been evaluated.

CONCLUSION

The combination of pyrazole with other pharmacophores in a molecule might lead to novel potent therapeutic medicines, which could aid in the development of potent lead compounds.

Benzofuran pyran hybrid prevents glucocorticoid induced osteoporosis in mice via modulation of canonical Wnt/β-catenin signaling

Glucocorticoid induced osteoporosis (GIOP) is the second most leading cause of osteoporosis. We have identified a compound, a benzofuran pyran hybrid compound 4e that has osteogenic potential and we wanted to assess its efficacy in GIOP in male mice. We assessed the effect of dexamethasone and compound 4e on primary osteoblasts using various cell based and immunofluorescence assays. For in vivo studies we administered methylprednisolone and compound 4e as a prophylactic measure in male Balb/c mice for 28 days and then evaluated the effect on bone microarchitecture by microCT, bone formation by histology along with clinically relevant bone markers. Compound 4e preserved osteoblast differentiation as evident by higher ALP positive cells and mineralization in compound treated groups. Compound 4e also increased the expression of osteogenic genes. This compound guarded β-catenin expression both in vitro and in vivo as confirmed by western blot and immunofluorescence assays. This led to the preservation of bone microarchitecture and cortical thickness at 2.5 mg kg⁻¹ and 5 mg kg⁻¹ doses. Further compound 4e enhanced bone formation rate and regulated osteocyte death. The osteogenic potential of compound 4e was reflected by an increased level of serum marker osteocalcin and decreased levels of SOST and CTX-I. Overall, Compound 4e is able to overcome the catabolic effect of dexamethasone on bone by targeting the canonical WNT/β-catenin signaling as evidenced by both in vitro and in vivo studies.

Evaluation of a new series of pyrazole derivatives as a potent epidermal growth factor receptor inhibitory activity: QSAR modeling using quantum-chemical descriptors

Pyrazole derivatives correspond to a family of heterocycle molecules with important pharmacological and physiological applications. At present, we perform a density functional theory (DFT) calculations and a quantitative structure-activity relationship (QSAR) evaluation on a series of 1-(4,5-dihydro-1H-pyrazol-1-yl) ethan-1-one and 4,5-dihydro-1H-pyrazole-1-carbothioamide derivatives as an epidermal growth factor receptor (EGFR) inhibitory activity. We thus propose a virtual screening protocol based on a machine-learning study. This theoretical model relates the studied compounds' biological activity to their calculated physicochemical descriptors. Moreover, the linear regression function is used to validate the model via the evaluation of Q² _ext and Q² _cv parameters for external and internal validations, respectively. Our QSAR model shows a good correlation between observed activities IC₅₀ and predicted ones. Our model allows us to mitigate time-consuming problems and waste chemical and biological products in the preclinical phases.

Copper-Catalyzed Cross-Coupling of Benzylic C-H Bonds and Azoles with Controlled N-Site Selectivity

Azoles are important motifs in medicinal chemistry, and elaboration of their structures via direct N-H/C-H coupling could have broad utility in drug discovery. The ambident reactivity of many azoles, however, presents significant selectivity challenges. Here, we report a copper-catalyzed method that achieves site-selective cross-coupling of pyrazoles and other N-H heterocycles with substrates bearing (hetero)benzylic C-H bonds. Excellent N-site selectivity is achieved, with the preferred site controlled by the identity of co-catalytic additives. This cross-coupling strategy features broad scope for both the N-H heterocycle and benzylic C-H coupling partners, enabling application of this method to complex molecule synthesis and medicinal chemistry.

Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES

Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED - a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.

Direct and Divergent Solid-Phase Synthesis of Azobenzene and Spiropyran Derivatives

Here, we report a solid-phase approach to synthesize azobenzene and spiropyran derivatives. The divergent synthesis process requires no purification steps to obtain the desired product with a 28-55% yield, depending on the specific compound. For the spiropyran compounds, solid-phase resin cleavage is performed under mild conditions to minimize spiropyran ring opening. The solid-phase method enables the synthesis of a library of azobenzene and spiropyran derivatives without the need to develop purification strategies for each derivative.

Design of Novel Drug-like Molecules Using Informatics Rich Secondary Metabolites Analysis of Indian Medicinal and Aromatic Plants

BACKGROUND

Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known.

OBJECTIVE

This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach.

METHODS

We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs.

RESULTS AND CONCLUSION

The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.

A Review on Recent Advances in Nitrogen-Containing Molecules and Their Biological Applications

The analogs of nitrogen-based heterocycles occupy an exclusive position as a valuable source of therapeutic agents in medicinal chemistry. More than 75% of drugs approved by the FDA and currently available in the market are nitrogen-containing heterocyclic moieties. In the forthcoming decade, a much greater share of new nitrogen-based pharmaceuticals is anticipated. Many new nitrogen-based heterocycles have been designed. The number of novel N-heterocyclic moieties with significant physiological properties and promising applications in medicinal chemistry is ever-growing. In this review, we consolidate the recent advances on novel nitrogen-containing heterocycles and their distinct biological activities, reported over the past one year (2019 to early 2020). This review highlights the trends in the use of nitrogen-based moieties in drug design and the development of different potent and competent candidates against various diseases.

Design, in silico studies, and synthesis of new 1,8-naphthyridine-3-carboxylic acid analogues and evaluation of their H1R antagonism effects

New 1,8-naphthyridine-3-carboxylic acid derivatives were designed, synthesized and evaluated for their in vivo antihistaminic activity on guinea pig trachea by using chlorpheniramine as the standard drug. It was found that compound 5a1 displayed a promising bronchorelaxant effect in conscious guinea pigs using the in vivo model. A molecular docking study was performed to understand the molecular interaction and binding mode of the compounds in the active site of the H1 receptor. Furthermore, in silico computational studies were also performed to predict the binding modes and pharmacokinetic parameters of these derivatives. Prior to the start of experimental lab work, PASS software was used to predict the biological activities of these compounds. An in silico PASS, Swiss ADME assisted docking approach was found to be suitable to derive and synthesize effective antihistaminic agents for the present study.

New 1,8-naphthyridine-3-carboxylic acid derivatives were designed, synthesized and evaluated for their in vivo antihistaminic activity on guinea pig trachea by using chlorpheniramine maleate as the standard drug.

Design and Solid-Phase Parallel Synthesis of 2,4,5-Trisubstituted Thiazole Derivatives via Cyclization Reaction with a Carbamimidothioate Linker

Preparation of 2,4,5-trisubstituted thiazole derivatives via a new solid-phase synthetic route has been conducted in this study. The synthetic route begins with the synthesis of a core skeleton 2,4-diamino(thiazol-5-yl)-substituted phenylmethanone resin obtained through a cyclization reaction with a carbamimidothioate linker. The core skeleton was substituted with diverse building blocks such as amines, alkyl halides, and acid chlorides. The products were cleaved from the solid support via a TFA/CH₂Cl₂ cleavage cocktail. Overall, the strategy permits the incorporation of three points of diversity into the thiazole ring system with good overall yields ( Lee , T. ; et al. J. Comb. Chem. 2009 , 11 ( 2 ), 288 - 293 ). Finally, the library of 2,4,5-trisubstituted thiazole derivatives showed oral bioavailability through calculation of the physicochemical properties.

Advances in the Solid-Phase Synthesis of Pyrimidine Derivatives

This Review describes the existing synthetic approaches for the solid-phase synthesis (SPS) of differently substituted and fused pyrimidine derivatives. These synthetic strategies are classified on the basis of the different synthetic routes leading to the particular type of pyrimidine heterocycle formed. The Review discusses the application of a variety of polystyrene derived supports for the construction of pyrimidine rings. The effect of microwave heating on the solid-phase synthesis is also addressed in the review.

Construction of Druglike 2-Amido Benzo[ d]imidazole Analogues via Desulfurative Cyclization of Thiourea Intermediate Resin on Solid-Phase

A 2-amido benzo[ d]imidazole library has been constructed by solid-phase synthesis. The key step of this solid-phase synthesis involves the preparation of polymer-bound 2-amino benzo[ d]imidazole resin through desulfurative cyclization of thiourea resin using 2-chloro-1,3-dimethylimidazolinium chloride and N, N-diisopropylethylamine in dichloromethane (DCM), and the resin is then functionalized by acylation at the 2-amine position to afford 2-amidobenzo[ d]imidazole resin. In the case of 2-amidobenzo[ d]imidazole resin having a p-I or m-NO₂, the resin was further functionalized by Suzuki/Sonogashira-coupling ( p-I) and reduction to the primary amine ( m-NO₂) followed by acylation. Finally, the functionalized 2-amido-benzo[ d]imidazole resin was cleaved from the polymer support by treatment with a cocktail of trifluoroacetic acid and DCM. As a result, we obtained 2-amidobenzo[ d]imidazole analogues in high yield and good purities.

Industrial medicinal chemistry insights: neuroscience hit generation at Janssen

The role of medicinal chemistry has changed over the past 10 years. Chemistry had become one step in a process; funneling the output of high-throughput screening (HTS) on to the next stage. The goal to identify the ideal clinical compound remains, but the means to achieve this have changed. Modern medicinal chemistry is responsible for integrating innovation throughout early drug discovery, including new screening paradigms, computational approaches, novel synthetic chemistry, gene-family screening, investigating routes of delivery, and so on. In this Foundation Review, we show how a successful medicinal chemistry team has a broad impact and requires multidisciplinary expertise in these areas.

Programmable disorder in random DNA tilings

Scaling up the complexity and diversity of synthetic molecular structures will require strategies that exploit the inherent stochasticity of molecular systems in a controlled fashion. Here we demonstrate a framework for programming random DNA tilings and show how to control the properties of global patterns through simple, local rules. We constructed three general forms of planar network-random loops, mazes and trees-on the surface of self-assembled DNA origami arrays on the micrometre scale with nanometre resolution. Using simple molecular building blocks and robust experimental conditions, we demonstrate control of a wide range of properties of the random networks, including the branching rules, the growth directions, the proximity between adjacent networks and the size distribution. Much as combinatorial approaches for generating random one-dimensional chains of polymers have been used to revolutionize chemical synthesis and the selection of functional nucleic acids, our strategy extends these principles to random two-dimensional networks of molecules and creates new opportunities for fabricating more complex molecular devices that are organized by DNA nanostructures.

Benzofuran-2-acetic ester derivatives induce apoptosis in breast cancer cells by upregulating p21Cip/WAF1 gene expression in p53-independent manner

Breast cancer is the most common malignancy and the leading cause of cancer-related death in women worldwide. High toxicity of used chemotherapeutics and resistance of cancer cells to treatments are a driving force for searching the new drug candidates for breast cancer therapy. In this study, we tested the antiproliferative effects of a series of benzofuran-2-acetic methyl ester derivatives, synthesized by a palladium-catalyzed carbonylative heterocyclization approach, on breast cancer cells. We observed that benzofuran compounds bearing a phenyl or tert-butyl substituent α to the methoxycarbonyl group significantly inhibited anchorage-dependent and -independent cell growth, and induced G0/G1 cell cycle arrest in human estrogen receptor alpha positive (MCF-7 and T47D) and in triple negative MDA-MB-231 breast cancer cells, without affecting growth of MCF-10A normal breast epithelial cells. Mechanistically, benzofuran derivatives enhanced the cyclin-dependent kinase inhibitor p21^Cip/WAF1 expression at both mRNA and protein levels and this occurs transcriptionally in an Sp1-dependent manner. Moreover, benzofuran derivatives induced apoptosis, increased poly (ADP-ribose) polymerase cleavage and Bax/Bcl-2 ratio along with a marked DNA fragmentation along with a marked DNA fragmentation and a strong increase in TUNEL-positive breast cancer cells. Overall, we provide evidence that the newly tested benzofuran derivatives showed antiproliferative and pro-apoptotic activities against breast cancer cells regardless estrogen receptor status, suggesting their possible clinical development as anticancer agents.

Review: biologically active pyrazole derivatives

Nitrogen-containing heterocyclic compounds and their derivatives have historically been invaluable as a source of therapeutic agents.

A Dual Culture Assay for Detection of Antimicrobial Activity

Combinatorial chemistry has opened a new realm of chemical diversity in the search for useful therapeutics as well as the ability to generate chemical libraries of hundreds of thousands to millions of discrete compounds. For the biologist, the goal is to screen these large libraries quickly and to obtain as much information in the primary screen as possible. Ideally, a primary screen would not only identify potential lead compounds but also yield information about the specificity, toxicity, and potency of that compound. Toward this end, a primary screen has been developed in which two organisms are cocultured, either bacteria, yeast, or mammalian cells, in the presence of a combinatorial library. For example, bacteria and yeast are cocultured either in liquid or in agar. When exposed to compounds from the combinatorial library, individual compounds are found which inhibit bacterial growth antibacterialls, inhibit yeast growth (antifungals), or inhibit both (potential toxins). This screening method is simple, rapid, and eliminates many of the false positives usually encountered in antimicrobial screening.

Solid-Phase Synthesis of 1,3,4-Thiadiazole Derivatives via Desulfurative Cyclization of Thiosemicarbazide Intermediate Resin

A 1,3,4-thiadiazole library was constructed by solid-phase organic synthesis. The key step of this solid-phase synthesis involves the preparation of polymer-bound 2-amido-5-amino-1,3,4-thiadiazole resin by the cyclization of thiosemicarbazide resin using p-TsCl as the desulfurative agent, followed by the functionalization of the resin by alkylation, acylation, alkylation/acylation, and Suzuki coupling reactions. Both the alkylation and acylation reactions chemoselectively occurred at the 2-amide position of 2-amido-5-amino-1,3,4-thiadiazole resin and the 5-amine position of 2-amido-5-amino-1,3,4-thiadiazole resin, respectively. Finally, these functionalized 1,3,4-thiadiazole resins were treated with trifluoroacetic acid in dichloromethane, affording diverse 1,3,4-thiadiazole analogs in high yields and purities. The 1,3,4-thiadiazole analogs show a different distribution of physicochemical and biological properties compared with our previously constructed 1,3,4-oxadiazole and 1,3,4-thiadiazole libraries in a range of orally available drug properties.

Sucrose and KF quenching system for solution phase parallel synthesis

The KF, sucrose (table sugar) exploited as quenching system in solution phase parallel synthesis. Excess of electrophiles were covalently trapped with hydroxyl functionality of sucrose and due to polar nature of sucrose derivative was solubilize in water. Potassium fluoride used to convert various excess electrophilic reagents such as acid chlorides, sulfonyl chlorides, isocyanates to corresponding fluorides, which are less susceptible for hydrolysis and subsequently sucrose traps these fluorides and dissolves them in water thus removing them from reaction mixture. Various excess electrophilic reagents such as acid chlorides, sulfonyl chlorides, and isocyanates were quenched successfully to give pure products in excellent yields.

Assay Miniaturization for Ultra-High Throughput Screening of Combinatorial and Discrete Compound Libraries: A 9600-Well (0.2 Microliter) Assay System

Combinatorial chemistry has opened a new realm of chemical entities in the search for novel therapeutics. Combinatorial chemistry is currently adding hundreds of thousands of compounds to similar numbers available from years of synthesis by medicinal chemistry. It is not unreasonable to expect that over the next several years, nearly a million compounds will be available for screening against each therapeutic target. The number of potential targets will also be increasing with the advances in genomics. With the increasing number of compounds to be screened against an increasing number of targets, it is becoming increasingly difficult and costly to obtain the required amounts of key biological material needed to screen these compounds. One obvious solution is to miniaturize the assays so that the biological reagent supply doesn't need to increase. To this end, we have developed an ultra-high throughput screening system comprised of a new plate design (9600-well), detection system, and liquid handling system. This new format is capable of performing assays in as little as 0.2 Al. The results obtained from this system compare favorably to those obtained in the standard 96-well format.

Active machine learning-driven experimentation to determine compound effects on protein patterns

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

DOI:http://dx.doi.org/10.7554/eLife.10047.001

Biomedical scientists have invested significant effort into making it easy to perform lots of experiments quickly and cheaply. These “high throughput” methods are the workhorses of modern “systems biology” efforts. However, we simply cannot perform an experiment for every possible combination of different cell type, genetic mutation and other conditions. In practice this has led researchers to either exhaustively test a few conditions or targets, or to try to pick the experiments that best allow a particular problem to be explored. But which experiments should we pick? The ones we think we can predict the outcome of accurately, the ones for which we are uncertain what the results will be, or a combination of the two?

Humans are not particularly well suited for this task because it requires reasoning about many possible outcomes at the same time. However, computers are much better at handling statistics for many experiments, and machine learning algorithms allow computers to “learn” how to make predictions and decisions based on the data they’ve previously processed.

Previous computer simulations showed that a machine learning approach termed “active learning” could do a good job of picking a series of experiments to perform in order to efficiently learn a model that predicts the results of experiments that were not done. Now, Naik et al. have performed cell biology experiments in which experiments were chosen by an active learning algorithm and then performed using liquid handling robots and an automated microscope. The key idea behind the approach is that you learn more from an experiment you can’t predict (or that you predicted incorrectly) than from just confirming your confident predictions.

The results of the robot-driven experiments showed that the active learning approach outperforms strategies a human might use, even when the potential outcomes of individual experiments are not known beforehand. The next challenge is to apply these methods to reduce the cost of achieving the goals of large projects, such as The Cancer Genome Atlas.

DOI:http://dx.doi.org/10.7554/eLife.10047.002

Nickel(ii) complex covalently anchored on core shell structured SiO2@Fe3O4 nanoparticles: a robust and magnetically retrievable catalyst for direct one-pot reductive amination of ketones

A magnetic silica based nickel nanocatalyst has been fabricated for the direct one-pot reductive amination of ketones.