Pseudouridine-modified RNA probe for label-free electrochemical detection of nucleic acids on 2D MoS nanosheets.

RNA modification, particularly pseudouridine (Ψ), has played an important role in the development of the mRNA-based COVID-19 vaccine. This is because Ψ enhances RNA stability against nuclease activity and decreases the anti-RNA immune response. Ψ also provides structural flexibility to RNA by enhancing base stacking compared with canonical nucleobases.

Revealing factors influencing polymer degradation with rank-based machine learning.

The efficient treatment of polymer waste is a major challenge for marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing polymer materials in the future. The small number of available datasets on degradability and the diversity of their experimental means and conditions hinder large-scale analysis.

Koopmans' Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules.

Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule.

Synthesis, optical properties and crystal structure of (,)-1,3-(3,4:9,10-dibenzododeca-1,11-diene-5,7-diyne-1,12-di-yl)benzene.

The de-hydro-benzannulene (,)-1,3-(3,4:9,10-dibenzododeca-1,11-diene-5,7-diyne-1,12-di-yl)benzene, CH, was successfully synthesized photocatalyst-assisted stereoselective reductive de-sulfonyl-ation of 1,3-bis-{1-phenyl-sulfonyl-2-[2-(tri-methyl-silylethyn-yl)phen-yl]ethen-yl}benzene, CHOSSi, and subsequent desilylative cyclization of the resulting (,)-bis-silyl-protected dienyne, CHSi. The structure of the de-hydro-benzannulene thus obtained was confirmed by single-crystal X-ray analysis; three benzene rings are connected to one another by a 1,3-butadiynylene and a pair of ethenylene arrays. Although the π-system expanded efficiently in the de-hydro-benzannulene, it was observed that the butadiynylene and ethenylene arrays were strained, showing smaller [171.

Anion-π Interactions in Monolayers Formed by Amphiphilic Electron-Deficient Aromatic Compounds at Air/Water Interfaces.

Biological systems precisely and selectively control ion binding through various chemical reactions, molecular recognition, and transport by virtue of effective molecular interactions with biological membranes and proteins. Because ion binding is inhibited in highly polar media, recognition systems for anions in aqueous media, which are relevant to biological and environmental systems, are still limited. In this study, we explored the anion binding of Langmuir monolayers formed by amphiphilic naphthalenediimide (NDI) derivatives with a series of substituents at air/water interfaces via anion-π interactions.

Selective Detection of Toxic C1 Chemicals Using a Hydroxylamine-Based Chemiresistive Sensor Array.

Formaldehyde (FA) is a deleterious C1 pollutant commonly found in the interiors of modern buildings. C1 chemicals are generally more toxic than the corresponding C2 chemicals, but the selective discrimination of C1 and C2 chemicals using simple sensory systems is usually challenging. Here, we report the selective detection of FA vapor using a chemiresistive sensor array composed of modified hydroxylamine salts (MHAs, ArCHONH·HCl) and single-walled carbon nanotubes (SWCNT).

Automatic Differentiation for the Direct Minimization Approach to the Hartree-Fock Method.

Automatic differentiation (AD) has become an important tool for optimization problems in computational science, and it has been applied to the Hartree-Fock method. Although the reverse-mode AD is more efficient than the forward-mode, eigenvalue calculation in the self-consistent field (SCF) method has impeded the use of the reverse-mode AD. Here, we propose a method to directly minimize Hartree-Fock energy under the orthonormality constraint of the molecular orbitals using reverse-mode AD by avoiding eigenvalue calculation.

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.

To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules.

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.

Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM).

High reactivity of HO vapor on GaN surfaces.

Understanding the process of oxidation on the surface of GaN is important for improving metal-oxide-semiconductor (MOS) devices. Real-time X-ray photoelectron spectroscopy was used to observe the dynamic adsorption behavior of GaN surfaces upon irradiation of HO, O, NO, and NO gases. It was found that HO vapor has the highest reactivity on the surface despite its lower oxidation power.

De novo creation of a naked eye-detectable fluorescent molecule based on quantum chemical computation and machine learning.

Designing fluorescent molecules requires considering multiple interrelated molecular properties, as opposed to properties that straightforwardly correlated with molecular structure, such as light absorption of molecules. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum chemical computation (QC) to develop fluorescent molecules, which are garnering significant attention in various disciplines. Using massive parallel computation (1024 cores, 5 days), the DNMG has produced 3643 candidate molecules.

Integrating Incompatible Assay Data Sets with Deep Preference Learning.

A large amount of bioactivity assay data is already accumulated in public databases, but the integration of these data sets for quantitative structure-activity relationship (QSAR) studies is not straightforward due to differences in experimental methods and settings. We present an efficient deep-learning-based approach called Deep Preference Data Integration (DPDI). For integrating outcome variables of different assay types, a surrogate variable is introduced, and a neural network is trained such that the total order induced by the surrogate variable is maximally consistent with given data sets.

Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization.

In order to accurately understand and estimate molecular properties, finding energetically favorable molecular conformations is the most fundamental task for atomistic computational research on molecules and materials. Geometry optimization based on quantum chemical calculations has enabled the conformation prediction of arbitrary molecules, including ones. However, it is computationally expensive to perform geometry optimizations for enormous conformers.

Cortisol induces follicle regression, while FSH prevents cortisol-induced follicle regression in pigs.

During follicular development, a few dominant follicles develop to large antral dominant follicles, whereas the remaining follicles undergo atretic degeneration. Because vascularization on the follicular surface is a morphological feature of dominant follicles, we previously classified these follicles as vascularized follicles (VFs) and non-VFs (NVFs). In NVFs, progesterone producing genes were expressed similarly to that in VFs; however, the progesterone concentration in follicular fluid was low in large NVFs.

Unique Photophysical Properties of 1,8-Naphthalimide Derivatives: Generation of Semi-stable Radical Anion Species by Photo-Induced Electron Transfer from a Carboxy Group.

The development of anion sensors for selective detection of a specific anion is a crucial research topic. We previously reported a selective photo-induced colorimetric reaction of 1-methyl-3-(-(1,8-naphthalimidyl)ethyl)imidazolium (MNEI) having a cationic receptor in the presence of molecules having multiple carboxy groups, such as succinate, citrate, and polyacrylate. However, the mechanism underlying this reaction was not clarified.

Semi-enzymatic synthesis of pseudouridine.

Modifications of RNA molecules have a significant effect on their structure and function. One of the most common modifications is the isomerization from uridine to pseudouridine. Despite its prevalence in natural RNA sequences, organic synthesis of pseudouridine has been challenging because of the stereochemistry requirement and the sensitivity of reaction steps to moisture.

Black-Box Optimization for Automated Discovery.

In chemistry and materials science, researchers and engineers discover, design, and optimize chemical compounds or materials with their professional knowledge and techniques. At the highest level of abstraction, this process is formulated as black-box optimization. For instance, the trial-and-error process of synthesizing various molecules for better material properties can be regarded as optimizing a black-box function describing the relation between a chemical formula and its properties.

Spontaneous regression of breast cancer with immune response: a case report.

Background: Spontaneous regression (SR) is a rare phenomenon in which a cancer disappears or remits without treatment. We report a case of breast cancer that showed spontaneous tumor regression in the surgical specimen after core needle biopsy.

Case Presentation: A 59-year-old woman came to our hospital complaining of a painful lump in the right breast.

NMR-TS: de novo molecule identification from NMR spectra.

Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum.

Enantiomeric Excess Dependent Splitting of NMR Signal through Dynamic Chiral Inversion and Coligand Exchange in a Coordination Complex.

Nuclear magnetic resonance (NMR) spectroscopy cannot be used to discriminate enantiomers, and NMR resonances of enantiomeric mixtures are generally not affected by enantiomeric excess (). Here, we report that a coordination complex (·2Zn·3), where is a salen-like prochiral ligand and is an exchangeable acetate coligand, exhibits symmetrical splitting of one of the H NMR resonances of with the degree of splitting linearly proportional to of the chiral guest coligand , 2-phenoxypropionic acid. Despite the well-defined chirality in the crystal structure of ·2Zn·3, concurrent fast chiral inversion and coligand exchange in solution renders ·2Zn·3 the primary example of prochiral solvating agent (-CSA) based on a coordination complex.

Pushing property limits in materials discovery boundless objective-free exploration.

Materials chemists develop chemical compounds to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space.

Dynamics-Function Analysis in Catalytic RNA Using NMR Spin Relaxation and Conformationally Restricted Nucleotides.

A full understanding of biomolecular function requires an analysis of both the dynamic properties of the system of interest and the identification of those dynamics that are required for function. We describe NMR methods based on metabolically directed specific isotope labeling for the identification of molecular disorder and/or conformational transitions on the RNA backbone ribose groups. These analyses are complemented by the use of synthetic covalently modified nucleotides constrained to a single sugar pucker, which allow functional assessment of dynamics by selectively removing a minor conformer identified by NMR from the structural ensemble.

Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies.

This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using a molecule generator and a DFT simulator, and attempted to generate novel photofunctional molecules whose lowest excited states lie at desired energetic levels.

Coupling between conformational dynamics and catalytic function at the active site of the lead-dependent ribozyme.

In common with other self-cleaving RNAs, the lead-dependent ribozyme (leadzyme) undergoes dynamic fluctuations to a chemically activated conformation. We explored the connection between conformational dynamics and self-cleavage function in the leadzyme using a combination of NMR spin-relaxation analysis of ribose groups and conformational restriction via chemical modification. The functional studies were performed with a -methanocarbacytidine modification that prevents fluctuations to C2'-endo conformations while maintaining an intact 2'-hydroxyl nucleophile.