Recently, reactions of allylidenhydrazones with tetracyanoethylene were found to lead to cyclobutanes-products of usually unfavorable (2 + 2) cycloaddition. Herein we computationally demonstrate that the (4 + 2) product of this reaction is severely destabilized by incomplete C-N bond formation, arising from a complex interplay of substituent electronic effects. We show how destabilization of a single bond in the front-runner product averts its formation and redirects chemical reaction toward an uncharacteristic pathway.The ligand-activated transcription factor nuclear receptor related-1 (Nurr1) exhibits great potential for neurodegenerative disease treatment, but potent Nurr1 modulators to further probe and validate the nuclear receptor as a therapeutic target are lacking. We have systematically studied the structure-activity relationship of the 4-amino-7-chloroquinoline scaffold contained in Nurr1 activators amodiaquine and chloroquine and discovered fragment-like analogues that activated Nurr1 in several cellular settings. The most active descendants promoted the transcriptional activity of Nurr1 on human response elements as monomer, homodimer, and heterodimer and markedly enhanced Nurr1-dependent gene expression in human astrocytes. As a tool to elucidate mechanisms involving in Nurr1 activation, these Nurr1 agonists induced robust recruitment of NCoR1 and NCoR2 co-regulators to the Nurr1 ligand binding domain and promoted Nurr1 dimerization. These findings provide important insights in Nurr1 regulation. The fragment-sized Nurr1 agonists are appealing starting points for medicinal chemistry and valuable early Nurr1 agonist tools for pharmacology and chemical biology.The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier molecular orbital highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels and their HOMO-LUMO gaps from the small-sized molecule data to larger molecules remains a challenge. Here, we develop a multilevel attention neural network, named DeepMoleNet, to enable chemical interpretable insights being fused into multitask learning through (1) weighting contributions from various atoms and (2) taking the atom-centered symmetry functions (ACSFs) as the teacher descriptor.  https://www.selleckchem.com/products/tertiapin-q.html  The efficient prediction of 12 properties including dipole moment, HOMO, and Gibbs free energy within chemical accuracy is achieved by using multiple benchmarks, both at the equilibrium and nonequilibrium geometries, including up to 110,000 records of data in QM9, 400,000 records in MD17, and 280,000 records in ANI-1ccx for random split evaluation. The good transferability for predicting larger molecules outside the training set is demonstrated in both equilibrium QM9 and Alchemy data sets at the density functional theory (DFT) level. Additional tests on nonequilibrium molecular conformations from DFT-based MD17 data set and ANI-1ccx data set with coupled cluster accuracy as well as the public test sets of singlet fission molecules, biomolecules, long oligomers, and protein with up to 140 atoms show reasonable predictions for thermodynamics and electronic structure properties. The proposed multilevel attention neural network is applicable to high-throughput screening of numerous chemical species in both equilibrium and nonequilibrium molecular spaces to accelerate rational designs of drug-like molecules, material candidates, and chemical reactions.Since many factors influence the coordination around a metal center, steric and electronic effects of the ligands mainly determine the connectivity and, thus, the final arrangement. This is emphasized on Hg(II) centers, which have a zero point stabilization energy and, thus, a flexible coordination environment. Therefore, the unrestricted Hg(II) geometry facilitates the predominance of the ligands during the structural inception. Herein, we synthesized and characterized a series of six Hg(II) complexes with general formula (Hg(Pip)2(dPy)) (Pip = piperonylate, dPy = 3-phenylpyridine (3-phpy) (1), 4-phenylpyridine (4-phpy) (2), 2,2'-bipyridine (2,2'-bipy) (3), 1,10-phenanthroline (1,10-phen) (4), 2,2'6',2'-terpyridine (terpy) (5), or di(2-picolyl)amine (dpa) (6)). The elucidation of their crystal structures revealed the arrangement of three monomers (3, 5, and 6), one dimer (4), and two coordination polymers (1 and 2) depending on the steric requirements of the dPy and predominance of the ligands. Besides, the study of their photophysical properties in solution supported by TD-DFT calculations enabled us to understand their electronic effects and the influence of the structural arrangement on them.Intrinsically disordered proteins play a crucial role in cellular phase separation, yet the diverse molecular forces driving phase separation are not fully understood. It is of utmost importance to understand how peptide sequence, and particularly the balance between the peptides' short- and long-range interactions with other peptides, may affect the stability, structure, and dynamics of liquid-liquid phase separation in protein condensates. Here, using coarse-grained molecular dynamics simulations, we studied the liquid properties of the condensate in a series of polymers in which the ratio of short-range dispersion interactions to long-range electrostatic interactions varied. As the fraction of mutations that participate in short-range interactions increases at the expense of long-range electrostatic interactions, a significant decrease in the critical temperature of phase separation is observed. Nevertheless, sequences with a high fraction of short-range interactions exhibit stabilization, which suggests compensation for the loss of long-range electrostatic interactions. Decreased condensate stability is coupled with decreased translational diffusion of the polymers in the condensate, which may result in the loss of liquid characteristics in the presence of a high fraction of uncharged residues. The effect of exchanging long-range electrostatic interactions for short-range interactions can be explained by the kinetics of breaking intermolecular contacts with neighboring polymers and the kinetics of intramolecular fluctuations. While both time scales are coupled and increase as electrostatic interactions are lost, for sequences that are dominated by short-range interactions, the kinetics of intermolecular contact breakage significantly slows down. Our study supports the contention that different types of interactions can maintain protein condensates, however, long-range electrostatic interactions enhance its liquid-like behavior.