Chemical Literature Research Articles

Getting the Numbers Right in Medicinal Chemistry.

Medicinal Chemistry is a quantitative scientific discipline. Hence, it is of utmost importance that scientists working in this field pay rigorous attention to the correct representation of their quantitative data. However, browsing the Medicinal Chemistry literature, it becomes obvious that this is very often not the case. In particular, results from biological assays are too frequently reported with unreasonable numbers of significant figures. In this Perspective, it is argued that this is poor practice that sheds an unfortunate light on the discipline of Medicinal Chemistry and that therefore, more rigorous policies regarding the presentation of quantitative data in Medicinal Chemistry publications should be considered.

Structure-property Relationships Reported for the New Drugs Approved in 2023.

Drug-like properties play pivotal roles in drug adsorption, distribution, metabolism, excretion, and toxicity. Therefore, efficiently optimizing these properties is essential for the successful development of novel therapeutics. Understanding the structure-property relationships of clinically approved drugs can provide valuable insights for drug design and optimization strategies. Among the new drugs approved in 2023, which include 31 small-molecule drugs in the US, the structureproperty relationships of nine drugs were compiled from the medicinal chemistry literature, in which detailed information on pharmacokinetic and/or physicochemical properties was reported not only for the final drug but also for its key analogs generated during drug development. The structure- property relationships of nine newly approved drugs are summarized, including three kinase inhibitors and three G-protein-coupled receptor antagonists. Several optimization strategies, such as bioisosteric replacement and steric handle installation, have successfully produced clinical candidates with enhanced physicochemical and pharmacokinetic properties. The summarized structure- property relationships demonstrate how appropriate structural modifications can effectively improve overall drug-like properties. The ongoing exploration of structure-property relationships of clinically approved drugs is expected to offer valuable guidance for developing future drugs.

Game-Changer in Chemistry: Applying Advanced Techniques to Unveil Dark Matter Interactions

Aim: This systematic review examines potential connections between dark matter and chemical processes, focusing on weak interactions similar to neutrinos and a proposed experimental design for detecting dark matter interactions at the nuclear level. Neutrinos, though weakly interacting, may cluster under extreme conditions, providing new insights into dark matter detection (Friedland & Giannotti 2008) [8]. Background: Dark matter, constituting 85% of the universe’s mass, is invisible to direct detection methods (Bertone & Hooper, 2018; Peebles, 1982) [4, 12]. While its gravitational influence shapes cosmic structures and environments for chemical reactions, this review emphasizes potential interactions between dark matter and atomic nuclei, applying neutrino detection methods as a framework (Liddle & Lyth, 2000) [11]. Although neutrinos generally do not interact strongly with matter, under specific extreme or hypothetical conditions, they might cluster into larger structures. These conditions could include increased neutrino mass, stronger gravitational interactions, or the presence of a new force (Garrett & Duda 2011) [9]. Such scenarios might occur near black holes or in the early universe (Bahcall et al. 1999) [3]. This idea adds an intriguing dimension to the potential behaviors of neutrinos and could influence how we approach dark matter detection. Method: To maintain consistency: A comprehensive review of astrophysics, chemistry, and particle physics literature was conducted to explore detection techniques used in neutrino research and apply them to dark matter interactions (Cirelli, 2009) [5]. A hypothetical experiment involving nuclear resonance and weak nuclear interactions was proposed. Results: Neutrino detection technologies offer a valuable model for experiments aimed at capturing dark matter interactions. (Friedland & Giannotti, 2008) [8]. This could lead to breakthroughs in understanding dark matter's role in nuclear chemistry. Conclusion: Dark matter's elusive nature remains a significant challenge for detection, but its potential weak interactions with nuclei, akin to those of neutrinos, offer a promising experimental pathway. This review highlights the importance of experimental designs targeting these subtle interactions and suggests that discovering dark matter's chemical-like properties could revolutionize the field of chemistry (Garrett & Duda, 2011; Bahcall et al., 1999) [9, 3].

On the relevance of glycosyl oxonium ions to 1,2-cis-selective O-glycosylation in ether solvents

Since no later than the 1970s, organic chemists have speculated on the role of glycosyl oxonium ions in chemical O-glycosylation. Such species result from the attack of ethers on glycosyl oxocarbenium ions and are invoked to explain 1,2-cis-selectivity in ether solvents. However, a systematic study to probe this phenomenon appears to be lacking in the chemical literature. Herein, we study the effects of solvent, counteranion, protecting group electron-withdrawing effects, and acceptor on O-glycosylation stereoselectivity with D-glucosyl trichloroacetimidate donors. While many of these transformations proceed with 1,2-cis-selectivity, our results suggest that glycosyl oxonium ions play minimal, if any, role in O-glycosylation.

Development of Abraham model correlations for predicting the solubility of crystalline organic compounds and inorganic gases in ethyl formate

ABSTRACT Abraham model correlations are derived for predicting the solubility of crystalline organic compounds and inorganic gases dissolved in ethyl formate based on published experimental data gathered from the chemical literature. The mathematical expressions presented in the current study described the observed solubility data to within an overall standard deviation of 0.12 log units. Comparison of the Abraham model expressions for ethyl formate versus our recently published equations for ethyl acetate reveals that of the two organic solvents, ethyl acetate exhibits a better H-bond acceptor character, while ethyl formate engages in the stronger dipolar interactions with neighbouring solute molecules.

On Detour Index of Join of Hamilton-connected Graphs

In this paper we contribute to the literature of computational chemistry by providing exact expressions for the detour index of joins of Hamilton-connected ( H C ) graphs. This improves upon existing results by loosening the requirement of a molecular graph being Hamilton-connected and only requirement certain subgraphs to be Hamilton-connected.

Bibliometric analysis : Green chemistry trends and issues in chemistry education from 2019 to 2024

This study analyzes green chemistry research trends in chemistry education. This study used a quantitative bibliometric approach. The number of publications analyzed is 104 publication documents from 2019 – 2024. This research collects, processes, and filters information in Scopus journals and articles. Metadata results show that the distribution of publication frequency peaked in 2019, with 26 documents identified. The green chemistry research area is dominated by chemistry research (31.3%). The country with the most documents and the most productive in publishing green chemistry is the United States, with 30 papers identified. At the same time, Indonesia is ranked fifth as the most productive country in publishing green chemistry, with 10 documents identified. Canada ranked second with 17 papers, and Germany ranked third with 15 documents. The institutions that contributed the most came from Germany: the University of Bremen, with 11 papers 10.58%, and the University of Toronto with 8 documents (7.69%). The authors with the most citations are Chen Tse-Lun et al., with 245 citations. Meanwhile, when viewed from the number of documents published by the author, Eilks I. has 11 papers with a contribution of 4.91%. There are 5 clusters with the most popping keywords: green chemistry, human, and chemical reaction. Research and publications on this topic have been sparse in the past five years. Surveys and analyses of green chemistry literature are essential because tracking research trends in green chemistry in chemistry education is vital to directing the future.

Applications of MALDI mass spectrometry in forensic science.

Forensic chemistry literature has grown exponentially, with many analytical techniques being used to provide valuable information to help solve criminal cases. Among them, matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS), particularly MALDI MS imaging (MALDI MSI), has shown much potential in forensic applications. Due to its high specificity, MALDI MSI can analyze a wide variety of compounds in complex samples without extensive sample preparation, providing chemical profiles and spatial distributions of given analyte(s). This review introduces MALDI MS(I) to forensic scientists with a focus on its basic principles and the applications of MALDI MS(I) to the analysis of fingerprints, drugs of abuse, and their metabolites in hair, medicine samples, animal tissues, and inks in documents.

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics.

Background:Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC's proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

Title: Investigation of Kinetically Favorable Chromium Speciation Distribution for Iron Chromium Redox Flow Batteries

Chromium is a promising active material for redox flow battery (RFB) electrolytes due to its low cost and reduction potential very near the negative limit of the electrochemical window of water. However, many challenges exist with the performance of the Cr3+/2+, most notably sluggish electron-transfer kinetics and parasitic hydrogen evolution during the charge cycle. This presentation will focus on our recent efforts to overcome these challenges by understanding the interplay between electrolyte composition, speciation, and electron-transfer kinetics for the aqueous Cr3+/2+ redox couple.Among the earliest published work on flow batteries was a program based at NASA introducing the iron chromium redox flow battery (ICRFB) [1,2]. These studies were valuable in demonstrating both the promise and the complications of chromium for flow battery electrolytes. The NASA team went to great lengths to enhance chromium redox kinetics and suppress the hydrogen evolution via extensive modification of the carbon negative electrode with Au, Pb, and Bi additives. The present study is based on a key observation from this early work that the electron-transfer rates for Cr-chloride complexes depend strongly on the inner-sphere coordination environment, and specifically the number of chloride ions directly bound to the Cr center.Extensive prior studies from the inorganic coordination chemistry literature further demonstrate that Cr(III) chloride complexes undergo slow equilibration in aqueous solutions to form distributions of four species where the coordination environment around the metal center can be described generally as [CrClx(H2O)6-x](3-x)+ with 0 < x < 3 [3,4]. Each of these species yields a distinct optical absorption signature, enabling quantification of the composition with UV-VIS spectroscopy. Our overarching hypothesis, based on the early NASA work, was that increased chloride coordination (i.e., higher values of x in CrClx(H2O)6-x) would yield enhanced electron-transfer kinetics by reducing inner-sphere reorganization barriers associated with aquo ligands, or by enhancing catalytic mechanisms mediated by chloride surface adsorbates. Thus, we measured optical absorbance and electrochemical reversibility for Cr(III) chloride solutions with HCl, NaCl, and LiCl supporting electrolytes over a range of total chloride concentration from 0.1 to ~10 mol/L.Representative results are summarized in Figure 1 for 0.1 M CrCl3 in aqueous LiCl. Notably, at Au electrodes the reversibility of the Cr3+/2+ redox feature increases dramatically as chloride concentration increases above 5 mol/L (Figure 1a). This shift in reversibility is broadly correlated with UV-VIS results (Figure 1b) demonstrating that the primary Cr(III) species present initially remains the predominant species in the aqueous environment for high concentrations of chloride over time, but the predominant species shifts significantly at lower concentrations of LiCl. Ongoing work, which will be discussed in this presentation, is focused on full quantification of the relationship between speciation and electron-transfer kinetics as well as performance properties for ICRFBs with a range of supporting electrolyte concentrations. We anticipate that understanding the most kinetically favorable speciation distribution will allow us to develop improved electrolytes for functional ICRFBs.

DNA Genetics and UHPLC-Q-TOF-MS Analysis of Phytochemicals for Asparagus racemosus Roots

The medicinal herb Asparagus racemosus has been used in traditional medicine to treat various diseases such as cough, diarrhoea, diabetes, gastric issues, gonorrhoea, headaches, piles, rheumatism, and even lactation enhancement. This study explores the genetic information and phytochemicals of the species native to Gia Lai province, Vietnam before its conservation and cultivation. Five species of A. racemosus were analyzed using the trnL-e/trnL-f regions sequence. A. racemosus roots were extracted by water then Ultra-High-Performance Liquid Chromatography coupled with Quadrupole/Time-Of-Flight Mass Spectrometry (UHPLC-Q-TOF-MS) spectroscopy was used to screen for their phytochemicals. We have confirmed the DNA genetics of A. racemosus species collected in Gia Lai, Vietnam. In water extract of A. racemosus roots, UHPLC-Q-TOF-MS tentatively identified two flavonoids (Quercetin-3-glucuronide, Rutin), five steroidal saponins (Shatavarin I, Shatavarin IV, Shatavarin IX, Asparacoside, Asparanin A), and two steroids (β-sitosterol, Daucosterol). The experimental findings confirm the A. racemosus species for conservation and cultivation in Vietnam and contribute the benefits to the chemical literature of Vietnamese natural flora. A. racemosus should be further studied for pharmaceutical activities.

OpenChemIE: An Information Extraction Toolkit for Chemistry Literature.

Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly investigated extracting reactions from single modalities. In this paper, we present OpenChemIE to address this complex challenge and enable the extraction of reaction data at the document level. OpenChemIE approaches the problem in two steps: extracting relevant information from individual modalities and then integrating the results to obtain a final list of reactions. For the first step, we employ specialized neural models that each address a specific task for chemistry information extraction, such as parsing molecules or reactions from text or figures. We then integrate the information from these modules using chemistry-informed algorithms, allowing for the extraction of fine-grained reaction data from reaction condition and substrate scope investigations. Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69.5%. Additionally, the reaction extraction results of OpenChemIE attain an accuracy score of 64.3% when directly compared against the Reaxys chemical database. OpenChemIE is most suited for information extraction on organic chemistry literature, where molecules are generally depicted as planar graphs or written in text and can be consolidated into a SMILES format. We provide OpenChemIE freely to the public as an open-source package, as well as through a web interface.

Deciphering complexity in Pd–catalyzed cross-couplings

Understanding complex reaction systems is critical in chemistry. While synthetic methods for selective formation of products are sought after, oftentimes it is the full reaction signature, i.e., complete profile of products/side-products, that informs mechanistic rationale and accelerates discovery chemistry. Here, we report a methodology using high-throughput experimentation and multivariate data analysis to examine the full signature of one of the most complicated chemical reactions catalyzed by palladium known in the chemical literature. A model Pd-catalyzed reaction was selected involving functionalization of 2-bromo-N-phenylbenzamide and multiple bond activation pathways. Principal component analysis, correspondence analysis and heatmaps with hierarchical clustering reveal the factors contributing to the variance in product distributions and show associations between solvents and reaction products. Using robust data from experiments performed with eight solvents, for four different reaction times at five different temperatures, we correlate side-products to a major dominant N-phenyl phenanthridinone product, and many other side products.

Ensemble multiclassification model for predicting developmental toxicity in zebrafish

In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.

Analysis of the Physicochemical Properties of Anti-Schistosomal Compounds to Identify Next-Generation Leads.

To investigate the physicochemical properties of anti-schistosomal compounds reported between 2008 and 2023, a simple but extensive literature scrutiny was conducted. Keywords were searched in Chemical Abstracts Service (CAS) SciFinder and primary medicinal chemistry and pharmacology literature to locate publications with compounds displaying ex vivo and/or in vivo anti-schistosomal activity. A total of 57 repurposed U.S. Food and Drug Administration (FDA)-approved drugs, hits and their derivatives were manually extracted, curated and compared to known anti-schistosomal oral drugs in view of establishing trends of calculated critical molecular properties. From this analysis, it was determined that more than 65% of the compounds display cLogD7.4 > 3 values, whereas oxamniquine, metrifonate and praziquantel (PZQ), previous and currently used oral anti-schistosomal drugs, possess lower cLogD7.4 values (≤2.5). Furthermore, the lipophilicity associated with PZQ corresponds to a highly permeable and sparingly soluble compound, characteristics that favor drug absorption and compound penetration in the parasite. These physicochemical properties together with PZQ's anti-schistosomal activity make PZQ an essential medicine for the treatment of schistosomiasis and demonstrate the importance of finding the right balance among potency (e.g., EC50 < 5 and 0.5 μM), cell permeability (e.g., Papp > 2 × 106 cm/s) and kinetic aqueous solubility (e.g., >10 μM) to provide high-quality hits and/or leads for the discovery of new oral anti-schistosomal therapeutics.

Nitrodiazene oxides: a unique nitrogen- and oxygen-containing functional group.

Nitrogen-/oxygen-containing functional groups (N/O groups) may be found in a wide variety of areas such as agriculture, drug design and energetic materials. Exploring the chemistry and synthesis of N/O groups is desirable as compounds containing their functionality may prove to be invaluable in a variety of fields. A unique N/O functional group which may offer additional insight into the design of high-heteroatom content systems is the 2-nitrodiazene-1-N-oxide group (NDO group). While unique on their own, NDOs combine the well-known azoxy (N(O)=N) and nitro (-NO2) groups into a single unique N/O functional group. Although NDOs may offer superior densities and enthalpies of formations relative to their nitro counterparts, NDOs have been significantly less investigated than their nitro-bearing counterparts. This work will discuss NDOs in chemical literature from their initial discovery to modern synthesis techniques, energetic properties and chemical stability.

Further Results for Some Eccentric Based Indices.

Topological indices have a significant role in determining a molecule's topology in the light of theoretical chemistry. This study aimed to find new bounds for some eccentric-based topological indices. We used comparison theorem for integrals. We found new bounds for the eccentric-based harmonic index and atom-bond connectivity index. Our results are better than previous results in the literature. This study has contributed to the chemical graph theory literature by giving a new method to find eccentric-based topological indices with lower and upper bounds.

Scaling the Process Chemistry of a COVID-19 Antiviral Pharmaceutical Down for a Multistep Synthesis Experiment in the Undergraduate Teaching Laboratory.

Molnupiravir is an orally bioavailable direct acting antiviral agent that received emergency use authorization in late 2021 from the FDA for the treatment of patients with mild, moderate, or severe COVID-19. This prodrug is metabolized into a ribonucleoside that is incorporated into the viral RNA during replication. Its tautomerization between cytidine- and uridine-like forms ultimately causes multiple irreversible errors in the genetic code of the virus, which prevents successful viral replication. There are multiple process chemistry routes for molnupiravir synthesis published in the literature that attempt to maximize synthetic yield while minimizing cost and waste, which are goals similar to those of an implementable educational laboratory experiment for the teaching laboratory. We have developed a multiweek laboratory module for undergraduate students in which students conduct a multistep synthesis of molnupiravir. Specifically, our Organic Chemistry II Laboratory students performed the final two steps of molnupiravir synthesis using procedures derived directly from the published process chemistry literature. We utilized this opportunity to introduce students to reading and interpreting these primary experimental sources. Students obtained authentic characterization data via high pressure liquid chromatography (HPLC) and nuclear magnetic resonance (NMR) spectroscopy to assess the conversion and purity of their products at each synthetic step. We report our in-lab activities and student generated data as well as suggestions for how this laboratory experiment could be tailored to meet similar learning objectives in other courses, such as medicinal chemistry or capstone laboratory courses, and as a function of available instrumentation.

Structure-property Relationships Reported for the New Drugs Approved in 2022.

The structure-property relationship illustrates how modifying the chemical structure of a pharmaceutical compound influences its absorption, distribution, metabolism, excretion, and other related properties. Understanding structure-property relationships of clinically approved drugs could provide useful information for drug design and optimization strategies. Among new drugs approved around the world in 2022, including 37 in the US, structure- property relationships of seven drugs were compiled from medicinal chemistry literature, in which detailed pharmacokinetic and/or physicochemical properties were disclosed not only for the final drug but also for its key analogues generated during drug development. The discovery campaigns for these seven drugs demonstrate extensive design and optimization efforts to identify suitable candidates for clinical development. Several strategies have been successfully employed, such as attaching a solubilizing group, bioisosteric replacement, and deuterium incorporation, resulting in new compounds with enhanced physicochemical and pharmacokinetic properties. The structure-property relationships hereby summarized illustrate how proper structural modifications could successfully improve the overall drug-like properties. The structure-property relationships of clinically approved drugs are expected to continue to provide valuable references and guides for the development of future drugs.

Experiment-based Abraham model solute descriptors for 2,3-dimethoxybenzoic acid from measured solubility data

ABSTRACT Mole fraction solubilities have been experimentally determined for 2,3-dimethoxybenzoic acid dissolved in propanenitrile and diisopropyl ether, in two more alkyl alkanoates (tert-butyl acetate, methyl butanoate), and in ten more alcohols (1-pentanol, 1-hexanol, 1-heptanol, 1-octanol, 2-methyl-1-propanol, 2-butanol, 2-pentanol, 3-methyl-1-butanol, 4-methyl-2-pentanol, cyclopentanol) at 298.15 K using a spectrophotometric method of chemical analysis based on the Beer-Lambert law. Experiment-based solute descriptors for the monomeric form of the dissolved carboxylic acid solute have been calculated using the results from our solubility measurements, combined with published solubility data taken from the chemical literature. The calculated solute descriptors provide a reasonably accurate mathematical description of the observed solubility data, with the overall standard deviation between the observed and back-calculated values being 0.074 log units. The solute descriptors further suggest that the carboxylic acid solute does not undergo intramolecular hydrogen bonding in the 28 organic solvents considered in the current study.