Metabotropic glutamate receptor 2 (mGlu2) is a known target for treating several central nervous system (CNS) disorders. To develop a viable positron emission tomography (PET) ligand for mGlu2, we identified new candidates 5a-i that are potent negative allosteric modulators (NAMs) of mGlu2. Among these candidates, 4-(2-fluoro-4-methoxyphenyl)-5-((1-methyl-1H-pyrazol-3-yl)methoxy)picolinamide (5i, also named as [11C]MG2-1812) exhibited high potency, high subtype selectivity, and favorable lipophilicity. Compound 5i was labeled with positron-emitting carbon-11 (11C) to obtain [11C]5i in high radiochemical yield and high molar activity by O-[11C]methylation of the phenol precursor 12 with [11C]CH3I. In vitro autoradiography with [11C]5i showed heterogeneous radioactive accumulation in the brain tissue sections, ranked in the order cortex > striatum > hippocampus > cerebellum ≫ thalamus > pons. PET study of [11C]5i indicated in vivo specific binding of mGlu2 in the rat brain. Based on the [11C]5i scaffold, further optimization for new candidates is underway to identify a more suitable ligand for imaging mGlu2.Many traditional quantitative structure-activity relationship (QSAR) models are based on correlation with high-dimensional, highly variable molecular features in their raw form, limiting their generalizing capabilities despite the use of large training sets. They also lack elements of causality and reasoning. With these issues in mind, we developed a method for learning higher-level abstract representations of the effects of the interactions between molecular features and biology.  https://www.selleckchem.com/products/Romidepsin-FK228.html  We named the representations as the reason vectors. They are composed of a series of computed activity of substructures obtained from stepwise reconstruction of the molecule. This representation is very different from fingerprints, which are composed of molecular features directly. These vectors capture reasons of bioactivity of chemicals (or absence thereof) in an abstract form, uncover causality in interactions between chemical features, and generalize beyond specific chemical classes or bioactivity. Reason vectors contain only a few key attributes and are much smaller than molecular fingerprints. They allow vague and conceptual similarity searches, less susceptible to failure on novel combinations of query molecule features and more likely to identify reasons of activity in chemical classes that are absent in training data. Reason vectors can be compared with each other and their activity can be computed by matching with vectors from molecules with known bioactivity. A single molecule produces as many reason vectors as heavy atoms in it, and a simple count of these vectors in a series of activity ranges is all what is needed to predict its bioactivity. Thus, the prediction method is devoid of gradient optimization or statistical fitting.The splitting of dinitrogen into nitride complexes emerged as a key reaction for nitrogen fixation strategies at ambient conditions. However, the impact of auxiliary ligands or accessible spin states on the thermodynamics and kinetics of N-N cleavage is yet to be examined in detail. We recently reported N-N bond splitting of a Mo(μ2η1η1-N2)Mo-complex upon protonation of the diphosphinoamide auxiliary ligands. The reactivity was associated with a low-spin to high-spin transition that was induced by the protonation reaction in the coordination periphery, mainly based on computational results. Here, this proposal is evaluated by an XAS study of a series of linearly N2 bridged Mo pincer complexes. Structural characterization of the transient protonation product by EXAFS spectroscopy confirms the proposed spin transition prior to N-N bond cleavage.Lithium-ion batteries (LIBs) are of tremendous importance for our society, but their limited lifetime still poses a great challenge. For a better understanding of battery cycling and degradation, operando analytical measurements are invaluable. In this work, we demonstrate that operando 7Li nuclear magnetic resonance (NMR) spectroscopy can be applied to full LIBs. We exemplify this on LiNi0.8Mn0.1Co0.1O2 (NMC811)/graphite cells, which are typical high-energy LIBs. Employing industry-standard electrodes, our operando cells show realistic cycling performance at practical rates, which allows us to conduct experiments at different rates and temperatures and to draw conclusions on the performance of LIBs. The NMR experiments monitor processes in both electrodes individually, including Li-ion mobility and its changes with temperature. Moreover, Li metal deposition on graphite is observed at low temperature, which is an important degradation mechanism in LIBs and a severe safety hazard. Our experiments offer unique insights into this Li metal deposition process under different charging conditions.Knowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs' synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe3+ ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe3+ ion with a wide concentration range (0-150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material.