Reductive Aza-Pauson-Khand Reaction of Nitriles.

γ-Lactams are valuable heterocycles in synthetic chemistry and drug development. Here, we report a reductive aza-Pauson-Khand reaction (aza-PKR) of an alkyne, a nitrile, and Co2(CO)8. A wide array of bicyclic α,ß-unsaturated γ-lactams containing two adjacent stereocenters, including an all-carbon quaternary center, from alkyne-tethered malononitriles are efficiently accessed in high diastereoselectivity. Preliminary mechanistic investigations by experiments and DFT calculations reveal that the reaction undergoes an aza-PKR process followed by a in situ reduction. The reducing reagent generated in situ from water also provides a practical tool for deuterium incorporation into the γ-position of lactams using D2O as the deuterium source. This study represents a new mode for [2 + 2 + 1] cycloaddition that enables the direct use of nitrile in aza-heterocycle synthesis.

General and Practical Potassium Methoxide/Disilane-Mediated Dehalogenative Deuteration of (Hetero)Arylhalides.

Herein we describe a general, mild and scalable method for deuterium incorporation by potassium methoxide/hexamethyldisilane-mediated dehalogenation of arylhalides. With CD3CN as a deuterium source, a wide array of heteroarenes prevalent in pharmaceuticals and bearing diverse functional groups are labeled with excellent deuterium incorporation (>60 examples). The ipso-selectivity of this method provides precise access to libraries of deuterated indoles and quinolines. The synthetic utility of our method has been demonstrated by the incorporation of deuterium into complex natural and drug-like compounds.

Event trigger identification for biomedical events extraction using domain knowledge.

MOTIVATION: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. RESULTS: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework.

Trifluoromethanesulfonyl azide as a bifunctional reagent for metal-free azidotrifluoromethylation of unactivated alkenes.

Vicinal trifluoromethyl azides have widespread applications in organic synthesis and drug development. However, their preparation is generally limited to transition-metal-catalyzed three-component reactions. We report here a simple and metal-free method that rapidly provides these building blocks from abundant alkenes and trifluoromethanesulfonyl azide (N3SO2CF3). This unprecedented two-component reaction employs readily available N3SO2CF3 as a bifunctional reagent to concurrently incorporate both CF3 and N3 groups, which avoids the use of their expensive and low atom economic precursors. A wide range of functional groups, including bio-relevant heterocycles and amino acids, were tolerated. Application of this method was further demonstrated by scale-up synthesis (5 mmol), product derivatization to CF3-containing medicinal chemistry motifs, as well as late-stage modification of natural product and drug derivatives.

Detosylative (Deutero)alkylation of Indoles and Phenols with (Deutero)alkoxides.

An efficient strategy for N/O-(deutero)alkylation of indoles and phenols with alkoxides/alcohols as the alkylation reagents is described. The consecutive detosylation/alkylation transformations feature mild reaction conditions, high ipso-selectivity, and good functional group tolerance (>50 examples). A one-pot selective N-alkylation of unprotected indoles with alcohols and TsCl is also realized. The application of this method is demonstrated by the introduction of isotope-labeled (CD3 and 13CH3) groups using the readily accessible labeled alcohols and the synthesis of pharmaceuticals.

Synthesis of Sultams and Cyclic N-Sulfonyl Ketimines via Iron-Catalyzed Intramolecular Aliphatic C-H Amidation.

Cyclic sulfonamides (sultams) play a unique role in drug discovery and synthetic chemistry. A direct synthesis of sultams by an intramolecular C(sp3)-H amidation reaction using an iron complex in situ derived from Fe(ClO4)2 and aminopyridine ligand is reported. This strategy features a readily available catalyst and tolerates a broad variety of substrates as demonstrated by 22 examples (up to 89% yield). A one-pot iron-catalyzed amidation/oxidation procedure for the synthesis of cyclic N-sulfonyl ketimines is also realized with up to 92% yield (eight examples). The synthetic utility of the method is validated by a gram-scale reaction and derivatization of the products to ring-fused sultams.

Iron-Catalyzed Intramolecular Amination of Aliphatic C-H Bonds of Sulfamate Esters with High Reactivity and Chemoselectivity.

It is challenging to develop simple and low cost catalytic systems while maintaining high reactivity and selectivity. An iron-catalyzed intramolecular C-H amination of sulfamate esters using simple and cheap ligands is reported with general substrate scope (31 examples, up to 95% yield). The addition of second ligand, bipyridine, is able to accelerate the reaction and increase the yield. The ready availability of these iron catalysts provides a promising approach to selective introduction of nitrogen into hydrocarbon feedstock.

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

OBJECTIVES: Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. METHODS AND MATERIAL: In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. RESULTS: Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. CONCLUSION: The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely described by hidden topics and structures of the sentences.

Biomedical relation extraction: from binary to complex.

Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches. In recent years, more interests have been shifted to the extraction of complex relations such as biomolecular events. While complex relations go beyond binary relations and involve more than two arguments, they might also take another relation as an argument. In the paper, we conduct a thorough survey on the research in biomedical relation extraction. We first present a general framework for biomedical relation extraction and then discuss the approaches proposed for binary and complex relation extraction with focus on the latter since it is a much more difficult task compared to binary relation extraction. Finally, we discuss challenges that we are facing with complex relation extraction and outline possible solutions and future directions.