Entire Irregularity Indices: A Comparative Analysis and Applications

Nitrate esters hold pivotal roles in pharmaceuticals, energetic materials, and atmospheric processes, motivating the development of efficient synthesis routes. Here, we present a novel catalytic method for the synthesis of nitrates via the direct O‐nitration of alcohols, addressing limitations of current traditional methods. Leveraging bench‐stable and recoverable N,6‐dinitrosaccharin reagent, our catalytic strategy employs magnesium triflate to achieve mild and selective O‐nitration of alcohols, offering broad substrate scope and unprecedentedly large functional group tolerance (e.g. alkenes, alkynes, carbonyls). DFT mechanistic studies reveal a dual role of the magnesium catalyst in the activation of both the nitrating reagent and the alcohol substrate. They also unveil a barrierless proton transfer upon formation of a widely‐accepted – yet elusive in solution – nitrooxonium ion intermediate. Overall, our work contributes to the development of mild, selective, and sustainable approaches to nitrates synthesis, with potential applications in drug discovery, materials science, and environmental chemistry.

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Identification of a novel turn-on albumin binding small-molecule bioprobe in live zebrafish and its potential application in drug discovery

Marine-derived fungi Trichoderma genus are serving as a valuable resource for structurally novel natural products encompassing a variety of chemical substances and diverse pharmacological applications. So far, about 78 metabolites have been structurally documented from the species of marine Trichoderma genus and a large proportion of these metabolites exhibited therapeutic properties with potential application in drug discovery. This mini-review focuses on the structures and bioactivities of these secondary metabolites from marine-derived Trichoderma species.

Abstract: Bioprinting technology has emerged as a powerful tool for building tissue and organ structures for drug discovery and regenerative medicine applications. In general, bioprinting uses a computer-controlled three-dimensional (3D) printing device to accurately deposit cells and biomaterials into precise geometries with the goal of creating anatomically correct biological structures. While traditional 3D printing uses metals, plastics, and polymers as printing materials or "ink", bioprinting deals with living cells and biological matrix. Hence, there are significant challenges to make a transition from traditional 3D printing to bioprinting, and ultimately achieve functional outcomes in bioprinted tissues. Therefore, it is critical that there is new technology development and in-depth basic research in bioprinted tissues, such as developing novel biomaterials specifically for use in bioprinting and biofabrication techniques, understanding the cell–matrix remodeling for the desired mechanical properties, and functional outcomes, establishing proper vascular perfusion, etc. Currently, there is active research going on bioprinting technology and its potential as a future source for tissue implants. This review paper overviews the current state of the art in bioprinting technology and focuses on the outcomes of the bioprinted tissues and their potential applications in drug discovery and regenerative medicine. Current challenges and limitations are highlighted, and future directions for next-generation bioprinting technology are also presented. Keywords: 3D bioprinting, vascularization, tissue regeneration, 3D tissue model, biofabrication, regenerative medicine

Lysine acetylation is a key mechanism that regulates chromatin structure; aberrant acetylation levels have been linked to the development of several diseases. Acetyl-lysine modifications create docking sites for bromodomains, which are small interaction modules found on diverse proteins, some of which have a key role in the acetylation-dependent assembly of transcriptional regulator complexes. These complexes can then initiate transcriptional programmes that result in phenotypic changes. The recent discovery of potent and highly specific inhibitors for the BET (bromodomain and extra-terminal) family of bromodomains has stimulated intensive research activity in diverse therapeutic areas, particularly in oncology, where BET proteins regulate the expression of key oncogenes and anti-apoptotic proteins. In addition, targeting BET bromodomains could hold potential for the treatment of inflammation and viral infection. Here, we highlight recent progress in the development of bromodomain inhibitors, and their potential applications in drug discovery.

Obtaining DNA nanostructures with potential applications in drug discovery, diagnostics, and electronics in a simple and affordable way represents one of the hottest topics in nanotechnological and medical sciences. Herein, we report a novel strategy to obtain structurally homogeneous DNA G‐wire nanostructures of known length, starting from the short unmodified G‐rich oligonucleotide d(5′‐CGGT‐3′–3′‐GGC‐5′) (1) incorporating a 3’–3′ inversion of polarity site. The reported approach allowed us to obtain long G‐wire assemblies through 5′–5′ π–π stacking interactions in between the tetramolecular G‐quadruplex building blocks that form when 1 is annealed in the presence of potassium ions. Our results expand the repertoire of synthetic methodologies to obtain new tailored DNA G‐wire nanostructures.

This study explored the potential of Raman spectroscopy to holistically monitor the glycolysis pathway kinetics as a function of time through the extracellular medium. Initially, the collinearity of individual metabolites of interest- glucose and lactic acid as a function of their concentration was tested followed by the sensitivity analysis of the approach by elucidating the limits of detection (0.85mM and 2.8mM) and quantification (2.5mM and 9.5mM) for glucose and lactic acid respectively in the biological range. In the process several datamining approaches were also explored. Finally, the A549 cell culture was used for kinetic spectral acquisition of the extracellular medium mimicking the kinetic glycolysis assay as a function of time under different pathway modulations. The spectra were resolved and fitted with a kinetically constrained-model (A→B→C) using the multivariate curve resolution- alternating least squares tool for all the modulated conditions to elucidate the pathway kinetics and the rate of change. The rate of change of the resolved components for the stimulated condition (k1: 0.005min-1, k2: 0.011min-1) was approximately twice as that of the control (k1: 0,045min-1; k2: 0.049min-1) while the inhibited condition (k1: 0.025min-1, k2: 0.017min-1) was substantially slower. The technique is superior to the targeted current gold standard kinetic assay approach, in that it is holistic in nature and has potential applications in drug discovery, bioprocessing, disease diagnostics, etc. Furthermore, this approach overcomes the limitations of the omics/multiomics approaches, limited to a snapshot of cellular metabolism. This study serves as a guideline for future, more complex subcellular kinetic spectroscopy experiments.

ABSTRACTIntroduction: Centipedes are one of the oldest and most successful lineages of venomous terrestrial predators. Despite their use for centuries in traditional medicine, centipede venoms remain poorly studied. However, recent work indicates that centipede venoms are highly complex chemical arsenals that are rich in disulfide-constrained peptides that have novel pharmacology and three-dimensional structure.Areas covered: This review summarizes what is currently known about centipede venom proteins, with a focus on disulfide-rich peptides that have novel or unexpected pharmacology that might be useful from a therapeutic perspective. The authors also highlight the remarkable diversity of constrained three-dimensional peptide scaffolds present in these venoms that might be useful for bioengineering of drug leads.Expert opinion: Like most arthropod predators, centipede venoms are rich in peptides that target neuronal ion channels and receptors, but it is also becoming increasingly apparent that many of these peptides have novel or unexpected pharmacological properties with potential applications in drug discovery and development.

In recent years, machine learning models have shown substantial progress in predicting molecular properties. However, integrating molecular graph structures with sequence information continues to present a significant challenge. In this paper, we introduce Multi-MoleScale, a novel multi-scale framework designed to address this challenge. By combining Graph Contrastive Learning (GCL) with sequence-based models like BERT, Multi-MoleScale enhances the prediction of molecular properties by capturing both structural and contextual representations of molecules. Specifically, the model leverages GCL to effectively capture the intrinsic graph-based features of molecules while utilizing BERT’s pretraining capabilities to learn the contextual relationships within molecular sequences. The contrastive learning component enables Multi-MoleScale to distinguish between relevant and irrelevant molecular features, thereby enhancing its predictive accuracy across diverse molecular types. To assess the performance of our method, we conducted experiments on several widely used public datasets, including 12 molecular property datasets, the ADMET dataset, and 14 breast cancer cell line datasets. The results show that Multi-MoleScale consistently outperforms existing deep learning and self-supervised learning approaches. Notably, the model does not require handcrafted features, making it highly adaptable and versatile for a variety of molecular discovery tasks. This makes Multi-MoleScale a promising tool for applications in drug discovery, materials science, and other molecular research fields. Our data and code are available at https://github.com/pdssunny/Multi-MoleScale. Supplementary InformationThe online version contains supplementary material available at 10.1186/s13321-025-01126-w.

Graph-based molecular representation learning is essential for predicting molecular properties in drug discovery and materials science. Despite its importance, current approaches struggle with capturing the intricate molecular relationships and often rely on limited chemical knowledge during training. Multimodal fusion, which integrates information from graph and other data sources together, has emerged as a promising approach for enhancing molecular property prediction. However, existing studies explore only a narrow range of modalities, and the optimal integration stages for multimodal fusion remain largely unexplored. Furthermore, the reliance on auxiliary modalities poses challenges, as such data is often unavailable in downstream tasks. Here, we present MMFRL (Multimodal Fusion with Relational Learning), a framework designed to address these limitations by leveraging relational learning to enrich embedding initialization during multimodal pre-training. MMFRL enables downstream models to benefit from auxiliary modalities, even when these are absent during inference. We also systematically investigate modality fusion at early, intermediate, and late stages, elucidating their unique advantages and trade-offs. Using the MoleculeNet benchmarks, we demonstrate that MMFRL significantly outperforms existing methods with superior accuracy and robustness. Beyond predictive performance, MMFRL enhances explainability, offering valuable insights into chemical properties and highlighting its potential to transform real-world applications in drug discovery and materials science.

Artificial intelligence (AI) has grown widely into diverse areas of research over the last decade. Publications distributed over the years disclose that biochemistry and analytical chemistry are incorporating AI to the utmost level. Enormous complex data sets are produced by analytical instruments comprising a treasure of information which is very helpful to analytical chemists in characterization. Despite this, we lack capabilities in data analysis and algorithms, which restricts our aptitude to discover and employ this data entirely. Machine learning and AI are being instigated to deal with this current challenge by accelerating several other applications in analytical chemistry. A critical valuation of illustrative reports amalgamating AI with analytical chemistry, spectroscopies, and several sensors has been discussed widely. This comprehensive review evaluates the AI advancement in data interpretation, the assistance of AI in analyte identification and quantification in proteomics and metabolomics, AI databases for primary and secondary metabolites information, regulatory and ethical guidelines in analytical chemistry incorporating AI, machine learning applications in drug discovery, agriculture, cosmetic, and food and separation techniques. In summary, this current review puts forward a thorough examination concerning the advancements of AI in different domains of chemistry and strives to facilitate comprehension of its forthcoming trajectories.

The acid-base dissociation constant (pKa) characterizes a molecule's tendency to donate or accept protons, thereby fundamentally influencing its physicochemical properties and behavior. Consequently, pKa is pivotal to diverse applications in drug discovery, including virtual screening and drug design. However, accurate pKa prediction remains challenging, due to intricate atomic interactions among ionizable groups and the scarcity of experimental data. To address these, we propose TripKa, a quantum-informed aqueous pKa predictor that employs a triplet interaction network to model complex thermodynamic coupling in multisite protonation equilibria. TripKa is fine-tuned using quantum-level descriptors as training targets, enabling the extraction of physical insights into molecular acid-base dissociation properties from limited high-quality pKa data. Our proposed TripKa outperforms state-of-the-art methods on multiple pKa benchmarks, including the SAMPL6-8 and Novartis data sets, achieving reductions of over 10% in both mean absolute error and root-mean-square error. Beyond pKa prediction, TripKa further serves as a foundational model for diverse molecular properties prediction and interaction modeling tasks, benefiting from its chemically informed pKa-pretraining.

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

Efficient molecular property prediction is crucial in bioinformatics and cheminformatics, with applications in drug discovery, materials science, and chemical engineering. This paper introduces MolHFCNet, a graph neural network designed to enhance molecular representation learning. At its core, the n-Hierarchical Features Combining (n-HFC) module aggregates information across multiple hierarchical feature spaces, effectively capturing both local and global graph structures. Unlike conventional models, n-HFC maintains computational complexity comparable to a single full-dimensional graph layer while supporting either 2D or 3D molecular graphs, ensuring flexibility across tasks. Furthermore, we propose a novel graph pretraining strategy that integrates predictive and contrastive learning, enabling the model to capture local chemical interactions and global molecular contexts for robust embeddings. Experimental results on benchmark datasets demonstrate MolHFCNet’s superior accuracy and efficiency compared to state-of-the-art methods, highlighting the potential of high-order hierarchical feature learning for advancing molecular graph analysis. Our code is available at https://github.com/ndlongvn/MolHFCNet.