Molecular Properties Research Articles

CO-CAVITY project: Molecular gas and star formation in void galaxies

Cosmic voids, distinguished by their low-density environment, provide a unique opportunity to explore the interplay between the cosmic environment and the processes of galaxy formation and evolution. Nevertheless, few data on the molecular gas have been obtained so far. In this paper, we continue the research performed in the CO-CAVITY pilot project to study the molecular gas content and properties in void galaxies in order to search for possible differences compared to galaxies that inhabit denser structures. We used the IRAM 30\,m telescope to observe the CO(1--0) and CO(2--1) emission of 106 void galaxies selected from the CAVITY survey. Together with data from the literature, we obtained a sample of 200 void galaxies with CO data. We conducted a comprehensive comparison of the specific star formation rate (sSFR = SFR/ the molecular gas fraction ( and the star formation efficiency (SFE = H_2 $) between the void galaxies and a comparison sample of galaxies in filaments and walls selected from the xCOLD GASS survey. We find no statistically significant difference between void galaxies and a comparison sample in the molecular gas fraction as a function of stellar mass for galaxies on the star-forming main sequence (SFMS). However, for void galaxies, the SFE is found to be constant across all stellar mass bins, while there is a decreasing trend with for the comparison sample. Finally, we find some indications for a smaller dynamical range in the molecular gas fraction as a function of distance to the SFMS in void galaxies. Overall, we find that the molecular gas properties of void galaxies are not very different from those of denser environments. The physical origin of the most significant difference that we find ---a constant SFE as a function of stellar mass in void galaxies--- is unclear and further investigation and higher-resolution data are required to gain further insight.

Synthesis and Structural Characterization of Carbazole‐Tailored Luminescent Triarylmethyl Radical and its Stable Cation

AbstractThe development of novel luminescent radicals, characterized by their unique doublet emission, endows a significant challenge. In this study, we reported the synthesis of a luminescent neutral radical, BCzAnM‐R, tailored by two carbazolyl groups and an anthryl group to achieve a nonalternant structure. It exhibited near‐infrared emission with a peak at 1020 nm in toluene. Interestingly, its corresponding cation, BCzAnM‐C, was synthesized through an unconventional SnCl2‐mediated reduction‐aromatization‐oxidation reaction in one‐pot and gram‐scale. The cation demonstrated remarkable stability for up to weeks in ambient conditions and facilitated the silica‐gel chromatography isolation as an organic salt with SnCl3− as the counter ion. The carbazolyl groups effectively modulate molecular structures, photophysical properties, and stabilities. Notably, BCzAnM‐R represents the first luminescent triarylmethyl radical with two carbazolyl groups directly attached to the central carbon.

Molecular properties of a triazole–Ce(III) complex with antioxidant activity: structure, spectroscopy, and relationships with related derivatives. Influence of the ligands in the complex

A novel Ce(III) complex with the triazole ligand 2b, which presents four H-bonded sites with amino acids of the MMP-2 receptor, was synthesized. The experimental IR and Raman spectra of this Ce(III) complex were well-interpreted based on their comparison to the theoretical scaled spectra using the scaling equations determined by two procedures and four density functional theory (DFT) levels. Therefore, the structure predicted for the synthesized Ce(III) complex was clearly characterized and confirmed. The potential antioxidant action of this complex was compared with the analogous La(III) complex, and it was found that the coordination of ligand 2b with Ce(III) improves the ligand’s ability to participate in single-electron transfer (SET), as observed in the ABTS·+ assay, and this complex seems to scavenge the stable radical much more actively compared to its La(III) counterpart. Additionally, interactions with potassium superoxide and sodium hypochlorite indicate a high pro-oxidant behavior of the complex. The effects of different ligands on the geometric parameters, atomic charges, and molecular properties of the Ce(III) complex were analyzed at four DFT levels, and several relationships were clearly established. These relationships can facilitate the selection of new ligands with improved properties in the design of novel lanthanide–triazole carboxylate complexes with promising biological activity. The ligand size increase in the complexes facilitates the electronic transfer of negative charge, and the low HOMO (highest occupied molecular orbital)–LUMO (lowest unoccupied molecular orbital) energy gap indicates a large reactivity and low energy for their excitation.

Machine Learning-Assisted Bayesian Optimization for the Discovery of Effective Additives for Dendrite Suppression in Lithium Metal Batteries.

In the pursuit of enhancing the performance and safety of lithium (Li)-metal batteries, the discovery of effective electrolyte additives to suppress Li dendrites has emerged as a paramount objective. In this study, we employ an inverse design strategy to identify potential additives for dendrite mitigation. Two key mechanisms, namely, the formation of robust solid electrolyte interphase layers and the leveling mechanism, serve as the foundation for our investigation. Our inverse design strategy is guided by molecular properties such as the lowest unoccupied molecular orbital energy and interaction energy upon Li surface adsorption. An active learning process utilizing Bayesian optimization (BO) was utilized to identify potential molecules with ideal properties. Through this screening process, we uncover a collection of 62 molecules with the potential to act as SEI-forming additives, along with 106 molecules for leveling additives, both surpassing the performance of established additives reported in the literature. This work highlights the potential of BO methods in computationally based inverse design of materials for many applications, and the discovered additives could potentially boost the commercialization of Li-metal batteries.

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction.

The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree's superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

DFT molecular modeling investigation and optical properties characterization of CR-39 films

Abstract In this study, the structural and optical characteristics of CR-39 films were investigated both theoretically and experimentally. A number of parameters for the CR-39 structure were theoretically computed by the density functional theory (DFT) method at B3LYP/6–311 G (p, d) level of theory. FTIR and UV/Vis spectroscopy were used to determine the structure compositions and the optical parameters of the CR39 film, respectively. The computed and experimental findings show good concordance. It was found that the CR-39 compound’s total energy, dipole moment, and energy difference between the LUMO and HOMO were, respectively, −994.575 a.u., 2.772 Debye, and 7.045 eV. The MEP showed a progressive change in color, with blue signifying a low electron density and red denoting a high electron density linked to nucleophilic reactivity and electrophilic reactivity. Further, the positive charges on all hydrogen atoms, which range from 0.16506 to 0.22032 a.u., imply that they are acceptors. The positive H34 is strongly produced when electrons from the negatively charged C17 are taken up. Because they are donor atoms, part of the carbon atoms in the structure is negative, while the other atoms are positive. The highest electronegative atoms H23 and H24 were substituted, resulting in the extremely negative carbon atom C7 (−0.32436). According to the experimentally determined absorption coefficient and energy gap values, CR-39 films may find application in optoelectronic devices.

Molecular quantum chemical data sets and databases for machine learning potentials

Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials.
In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis.
Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra.

The use of machine learning (ML) algorithms in molecular simulations has become commonplace in recent years. There now exists, for instance, a multitude of ML force field algorithms that have enabled simulations approaching ab initio level accuracy at time scales and system sizes that significantly exceed what is otherwise possible with traditional methods. Far fewer algorithms exist for predicting rotationally equivariant, tensorial properties such as the electric polarizability. Here, we introduce a kernel ridge regression algorithm for machine learning of the polarizability tensor. This algorithm is based on the bond polarizability model and allows prediction of the tensor components at the cost similar to that of scalar quantities. We subsequently show the utility of this algorithm by simulating gas phase Raman spectra of biphenyl and malonaldehyde using classical molecular dynamics simulations of these systems performed with the recently developed MACE-OFF23 potential. The calculated spectra are shown to agree very well with the experiments and thus confirm the expediency of our algorithm as well as the accuracy of the used force field. More generally, this work demonstrates the potential of physics-informed approaches to yield simple yet effective machine learning algorithms for molecular properties.

Investigation of linear and microscopic nonlinear optical responses of 1-indanone compounds in different environments based on polarity models

Molecular spectroscopic and nonlinear features can indicate positive changes by solvent molecules. In this work, DFT and spectroscopic techniques were used to study the polarity effects of different solvent environments. Polarity-based models were used for studying solvent induced interactions on the optical features of new groups of biomolecules. Despite the significant contribution of general effects on the molecular absorption spectra, there is considerable competition between general and specific environmental effects on the molecular emission properties. Under this condition, strong hydrogen bonds tend to increase molecular nonlinear responses. The same results were observed for the low order (first and second order) nonlinearity of biomolecules. Therefore, the studies on the environment effects on the biomolecules’ first order nonlinearity can give valuable information about higher-order optical responses. Moreover, 1-Indanone compounds with high nonlinearity can be considered as an effective element in designing optical devices.

Immunopathological markers and cell types linked to COVID-19 symptom manifestation

BackgroundNumerous studies have investigated the molecular properties that contribute to the symptoms of COVID-19, such as the virus’s genetic makeup, its replication mechanisms, and how it interacts with host cells. However, identifying the immunopathological properties, such as the immune system’s response, cytokine levels, and the presence of specific biomarkers, that are associated with the severity of the infection remains crucial for developing effective treatments and preventions.MethodsWe analyzed blood protein factor profiles from 420 individuals to identify features differentiating between test-negative healthy, asymptomatic, and symptomatic individuals using statistical comparison and the least absolute shrinkage and selection operator (i.e., LASSO) algorithm. Additionally, we examined single-cell RNA sequencing data from 141 individuals to identify specific cell types associated with the COVID-19 symptoms.ResultsHealthy individuals who tested negative had distinct blood protein factor levels compared to asymptomatic individuals. We identified two key protein factors, Serpin A10 and Complement C9, that differentiate between asymptomatic and symptomatic patients. Symptomatic patients showed lower levels of CD4+ T naïve, CD4+ T effector & memory, and CD8+ T naïve cells, along with higher levels of CD14+ classical monocytes compared to asymptomatic patients. Additionally, CD16+ non-classical monocytes, major producers of C1QA/B/C, appeared to contribute to the observed Complement C9 levels.ConclusionsThese findings advance our understanding of the immunopathological mechanisms underlying COVID-19 and may inform the development of targeted therapies and preventative measures. Future research should focus on further elucidating these mechanisms and exploring their potential clinical applications in managing COVID-19 severity.

Supramolecular Control of Helicene Circularly Polarized Luminescence Emitters in Molecular Solids and Bright Nanoparticles

AbstractCircularly polarized luminescence (CPL) from chiral molecules is attracting much attention due to its potential use in optical materials. However, formulation of CPL emitters as molecular solids typically deteriorates photophysical properties in the aggregated state leading to quenching and unpredictable changes in CPL behavior impeding materials development. To circumvent these shortcomings, a supramolecular approach can be used to isolate cationic dyes in a lattice of cyanostar‐anion complexes that suppress aggregation‐caused quenching and which we hypothesize can preserve the synthetically‐crafted chiroptical properties. Herein, we verify that supramolecular assembly of small‐molecule ionic isolation lattices (SMILES) allows translation of molecular ECD and CPL properties to solids. A series of cationic helicenes that display increasing chiroptical response is investigated. Crystal structures of three different packing motifs all show spatial isolation of dyes by the anion complexes. We observe the photophysical and chiroptical properties of all helicenes are seamlessly translated to water soluble nanoparticles by the SMILES method. Also, a DMQA helicene is used as FRET acceptor in SMILES nanoparticles of intensely absorbing rhodamine antennae to generate an 18‐fold boost in CPL brightness. These features offer promise for reliably accessing bright materials with programmable CPL properties.

Correlations between molecular weight, mechanical properties and morphology of micro-injection molded polyoxymethylene (POM)

Correlations between molecular weight, mechanical properties and morphology of micro-injection molded polyoxymethylene (POM)

Comparative study of various molecular feature representations for solvation free energy predictions of neutral species

Predicting molecular properties with the help of Neural Networks is a common way to substitute or enhance comprehensive quantum-chemical calculations. One of the problems facing researchers is the choice of vectorization approach to representing the solvent and the solute for the estimator model. In this work, 10 different approaches have been investigated for both organic solute and solvent including vectorizers that relied on macroscopic parameters, functional groups classification, molecular graphs, or atomic coordinates. A variation of the Bag of Bonds approach called JustBonds, trained on the MNSol database, showed the best overall performance resulting in RMSD < 2 kcal/mol for the blind dataset that contains the solutes not presented in the training subset and < 1 kcal/mol on records from Solv@TUM database, which is close to contemporary continuum models. We have also demonstrated that the most important bags usually contain heteroatom and play a key role in the solvation process. Furthermore, the small role of solvent vectorization was demonstrated and revealed that approaches based on functional groups or macroscopic solvent parameters are often enough to efficiently represent solvent media.

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

This study investigates the potential of leveraging molecular properties, as determined by MD-LOVIs software and machine learning techniques, to predict the ability of compounds to cross the blood-brain barrier (BBB). Accurate prediction of BBB permeation is critical for the development of central nervous system (CNS) drugs. The study applies various machine learning models, including both classification and regression techniques, to predict BBB passage and molecular activity. Notably, classification models such as GBM-AWV (accuracy = 0.801), GLM-CN (accuracy = 0.808), SVMPoly-means (accuracy = 0.980), SVMPoly-AC (accuracy = 0.980), SVMPoly-MI_TI_SI (accuracy = 0.900), SVMPoly-GI (accuracy = 0.900), RF-means (accuracy = 0.870), and GLM-means (accuracy = 0.818) demonstrate high accuracy in predicting BBB passage. In contrast, regression models like ES-RLM-AG (R2 = 0.902), IB-IBK (R2 = 0.82), IB-Kstar (R2 = 0.834), IB-MLP (R2 = 0.843), and DRF-AWV (R2 = 0.810) exhibit strong performance in predicting molecular activity. The results show that classification models like GBM-AWV, GLM-CN, and SVMPoly variants, as well as regression models like ES-RLM-AG and IB-MLP, achieve high performance, demonstrating the effectiveness of machine learning in predicting BBB permeability. The computational methods employed in this study include the MD-LOVIs software for generating molecular descriptors and several machine learning algorithms, including gradient boosting machines (GBM), generalized linear models (GLM), support vector machines (SVM) with polynomial kernels, random forests (RF), ensemble regression models, and instance-based learning algorithms. These models were trained and validated using various datasets to predict BBB passage and molecular activity, with the performance metrics reported for each model. Standard computational techniques were employed throughout, ensuring the reliability of the predictions.

HiMolformer: Integrating graph and sequence representations for predicting liver microsome stability with SMILES

In the initial stages of drug discovery or pre-clinical studies, understanding the metabolic stability of new molecules is crucial. Recently, research on pre-trained deep learning for molecular property prediction has been actively progressing, with various models being made open-source. However, most of these models rely on either 2D graph or 1D sequence for training, and the representation varies depending on the data format used. Consequently, combining multiple representations can broaden the scope of learning and may potentially be a manageable and most effective method to enhance performance.Therefore, we propose a novel hybrid model for predicting metabolic stability, which integrates representations from both graph-based and sequence-based models pre-trained for molecular features. This approach utilizes the combined strengths of 2D topological and 1D sequential information of molecules. HiMol, a graph-based graph neural network (GNN) model, and Molformer, a sequence-based Transformer model, were selected for integration, thus we named it HiMolformer. HiMolformer demonstrated superior performance compared to other models. We also focus on regression task for prediction with a empirical dataset from Korea Chemical Bank (KCB), comprising 3,498 molecules with mouse liver microsome (MLM) and human liver microsome (HLM) data obtained from actual metabolic reaction experiments. To the best of our knowledge, it is the first attempt to develop MLM and HLM prediction models using regression with a single SMILES input. The source code of this model is available at https://github.com/YUNSEOKWOO/HiMolformer.

Molecular docking and ADME evaluation of plant-based bioactive molecules targeting nonsense-mediated mRNA decay pathway factors to modulate tumorigenesis

Cancer is a global health challenge that requires continuous efforts to discover effective anticancer drugs. Phytochemicals are compounds found in plants that often have medicinal properties. They possess a wide range of bioactive properties, including anticancer activity. Their multiple mechanisms of action in different physiological processes in humans make them promising candidates in the anticancer therapeutics development. The presence of these compounds makes plants valuable resources for traditional medicine and modern pharmaceutical research as well. Natural products from plants and marine sources are being used to find new anticancer agents. In humans, different cellular pathways are involved in the tumorigenesis process. Many studies have shown the role of the nonsense-mediated mRNA decay (NMD) pathway in the process of tumorigenesis. This NMD pathway is controlled by multiple proteins. In this study, we conducted a molecular docking analysis of 50 phytochemicals against the human NMD factor up-frameshit2 (UPF2) protein. The results of the molecular docking experiment and ADME properties indicate that 4 of these molecules (Genistein, Trihydroxyflavone, Baicalein and Epigallocatechin) have the potential to modulate the NMD pathway. Furthermore, these molecules comply with Lipinski's rule of five. The effects of these 4 phytochemicals may be further evaluated using in vitro and in vivo methods for novel anticancer therapeutic development.

Water as a Structural Marker in Gelatin Hydrogels with Different Cross-Linking Nature

We have studied the molecular properties of water in physically and chemically cross-linked gelatin hydrogels by FTIR-spectroscopy, NMR relaxation, and diffusivity and broadband dielectric spectroscopy, which are sensitive to dynamical properties of water, being a structural marker of polymer network. All experiments demonstrated definite reinforcement of the hydrogel net structure and an increase in the amount of hydrate water. FTIR experiments have shown that the chemical cross-linking of gelatin molecules initiates an increase in the collagen-like triple helices “strength”, as a result of infused restriction on protein molecular mobility. The “strengthening” of protein chains hinders the mobility of protein fragments, introducing complex modifications into the structural properties of water which are remained practically unchanged up to up to 30–40 °C.

Synthesis, Biological Evaluation, Molecular Docking Studies and ADMET Prediction of Oxindole-Based Hybrids for the Treatment of Tuberculosis.

With a projected mortality toll of 1.4 million in 2019, tuberculosis (TB) continues to be a significant public health concern around the world. Studies of novel treatments are required due to decreased bioavailability, increased toxicity, increased side effects, and resistance of several first- and second-line TB therapies, including isoniazid and ethionamide. This study reports the synthesis of oxindole-based hybrids as potent InhA inhibitors targeting Mycobacterium tuberculosis. The synthesized compounds (5a-5e and 8a-8c) were evaluated for their anti-mycobacterial activity against Mycobacterium tuberculosis and nontuberculous mycobacteria (NTMs), viz. M. abscessus (ATCC 19977), M. fortuitum (ATCC 6841), and M. chelonae (ATCC 35752) using the Microplate Alamar Blue Assay (MABA). Molecular docking studies were performed using AutoDock Vina to explore the binding interactions of these compounds with the InhA enzyme (PDB: 2NSD). Additionally, biochemical and histopathological studies were conducted to assess the hepatotoxicity of the lead compounds. Insilico molecular properties and ADMET properties of the synthesized compounds were predicted using SwissADME and Deep-PK online tools to assess their drug-likeness. Among the tested compounds, 8b exhibited significant anti-mycobacterial activity with a minimum inhibitory concentration (MIC = 1 μg/mL) comparable to the reference drug ethambutol. Further, the compound demonstrated a binding affinity and orientation similar to the reference inhibitor 4PI, indicating its potential as a potent InhA inhibitor, and was found to be stabilized within the binding pocket of InhA through H-bonding, hydrophobic and van der Waal's interactions. Besides, the compounds hepatotoxicity assessment studies depicted that 8b showed no significant liver dysfunction or damage to liver tissues. Additionally, 8b adhered to Lipinski's rule of five and Veber's rule, displaying favourable pharmacokinetic and drug-like properties, including high human intestinal absorption, distribution, and acceptable metabolic stability and excretion. Compound 8b emerged as a promising candidate for further optimization and development as a therapeutic agent for tuberculosis, offering a new avenue for tackling tuberculosis.

Structural, molecular, and physicochemical properties of starch in high-amylose durum wheat lines

There is growing interest in the development of high-amylose cereals such as wheat due to their functional properties and nutritional value in foods. The increase in amylose content is associated with significant changes in the physicochemical, molecular, and structural characteristics of wheat starch which could affect its processing quality. The present study aimed to analyze the physicochemical, molecular, and structural characteristics of high-amylose starch from three durum wheat near isogenic lines (NILs), each obtained using three different recurrent parents. Morphology, granule size distribution, pasting and thermal properties, X-ray diffraction, and structural features were determined. The NILs showed the highest amylose content (65.3 and 69.8%), granules with an elongated shape, size between the wild-type and the mutant, restricted swelling in the pasting profile, the highest average and final gelatinization temperature, and B-polymorphism. The starches from the NILs showed the highest molar mass of amylopectin (AP) (7.0–7.6 × 107 Da) and the lowest amylose (AM) (1.1–1.8 × 106 Da), an issue associated with the biosynthesis of both components. The study on the debranched sample by high-performance size-exclusion chromatography showed that the high-amylose starch had the highest content of long B2 and B3 chains of AP (23.9–26.6%), which was corroborated with the high-performance anion-exchange chromatography analysis where the chains with degree of polymerization >37 had the highest content (18.3–19.1%). The PC1 is highly associated with the MwAM and the AM fraction in the debranched starch analyzed by HPSEC. Characterizing high-amylose durum wheat starch provides information to determine the structure-function relationship and design strategies to produce crosses with structural characteristics of AM and AP for specific applications.

Potential effects of pH changes in the micro-environment during desalination on infant powder: The perspective of pH shift affecting whey protein molecular structure and function

In order to simulate the effect of pH environment change caused by different desalting methods on whey protein (WP) in actual production, the WP was treated under different acidic pH (2.0–6.0) and pH shift (pH recovery). The molecular structure, properties, and functions of the proteins were further analyzed. The results showed that the spatial structure of WP was unfolded under acidic conditions, α-helix and β-sheet were transformed into random coils, hydrophobic residues and free sulfhydryl groups were exposed. The particle size of WP was increased, and the emulsification, foaming and thermal stability were improved significantly. Especially after pH2 treatment, WP has the largest expansion degree, emulsification and foaming properties increased to 232.96 m2/g and 158.83 %. After pH recovery, the partially unfolded structure refolded, reducing the surface hydrophobicity, forming a smaller particle size of soluble protein particles, solubility has increased to 83.95 % compared to the natural. However, the emulsification, foamability, and thermal stability after pH shift are slightly reduced compared to acidic conditions, making it easier to form a gel after heating.