Search Results (1,649)

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

Methicillin-resistant Staphylococcus aureus (MRSA) remains a significant multidrug-resistant pathogen, frequently associated with persistent infections and biofilm formation, underscoring the urgent need for alternative antimicrobial strategies. Bioactive compounds derived from fungi have attracted considerable attention due to their structural diversity and demonstrated antibacterial activity against MRSA. This study employed a scientometric approach to assess global research trends, thematic evolution, and collaborative networks concerning fungi-derived anti-MRSA compounds. Bibliographic data were collected from the Scopus database, and a total of 1666 English-language articles and reviews published up to 2025 were analyzed using Bibliometrix/Biblioshiny and VOSviewer. The findings indicate a marked increase in research output after 2010, reflecting heightened scientific interest in fungal natural products for MRSA management. China and the United States emerged as leading contributors in terms of publication volume and international collaboration. Thematic analysis revealed a shift from broad antimicrobial screening to more specialized investigations, including antibiofilm activity, secondary metabolites, endophytic fungi, molecular docking, and antimicrobial resistance. Nonetheless, several challenges persist, such as insufficient mechanistic validation, limited toxicity and pharmacokinetic assessments, and a lack of clinically relevant in vivo studies. Overall, the field is increasingly multidisciplinary, integrating microbiology, natural product chemistry, and computational methodologies to advance the discovery of anti-MRSA agents. Full article

This review examines the emerging role of machine learning (ML) in pharmaceutical chemistry, with emphasis on molecular design, synthetic feasibility, and structure–property–performance (SPP) relationships. By enabling pre-synthesis prediction of physicochemical properties, reaction pathways, and pharmaceutical performance, ML can reduce empirical trial-and-error experimentation and support more efficient exploration of chemical space. A structured narrative review design with PRISMA-aligned systematic search elements was used to evaluate 101 studies, enabling transparent literature identification, eligibility screening, and thematic synthesis across heterogeneous ML applications in pharmaceutical chemistry. This review examines structure–property relationships (SPRs) and property–performance relationships (PPRs), with emphasis on key pharmaceutical endpoints such as solubility, permeability, stability, dissolution, and bioavailability. An integrated SPP framework is proposed to connect molecular structure, intermediate properties, and final performance outcomes while incorporating retrosynthetic analysis and experimental feedback and closed-loop optimization. Recent frontier developments are also discussed, including molecular foundation models, multimodal language–graph models, diffusion-based molecular generation, E(3)-equivariant models, and MolMIM-like latent-space optimization. This review also covers co-folding and joint ligand–protein modeling, Boltz-2-like affinity prediction, AlphaFold 3-related biomolecular interaction modeling, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Key limitations include dataset leakage, benchmark inconsistency, assay variability, conformational and protonation-state effects, reproducibility challenges, regulatory constraints, and the gap between computational prediction and prospective experimental validation. Future progress is expected to depend on hybrid physics–ML models, uncertainty-aware prospective validation, autonomous experimentation, explainable artificial intelligence, and sustainability-aware molecular design. Overall, ML is evolving from a predictive tool into a chemically informed decision-support framework for rational, synthesis-aware, and experimentally validated pharmaceutical development. Full article

Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the average headspace concentration but in the temporal shape of volatile organic compound (VOC) release under controlled heating. Conventional electronic noses obscure this signal: they rely on multi-sensor arrays, compress each response into summary statistics, and report accuracy only at the level of individual measurements. Whether a single low-cost metal–oxide–semiconductor (MOS) gas sensor can recover grade-defining aroma chemistry, and whether waveform-level modeling can exploit it, was therefore investigated. A portable electronic nose built around a Bosch BME688 sensor recorded 90 time series, each comprising four directly measured channels (temperature, humidity, pressure, gas sensor resistance) and a derived indoor-air-quality (IAQ) proxy computed from them by the on-chip BSEC library, from 16 commercial Turkish black teas across three quality grades. Two representations were compared on the same data: a feature-based pipeline reducing 25 statistical descriptors to seven principal components for six classifiers (best F1-macro = 0.624, MLP), and a raw-waveform Multi-Scale 1D-CNN with Squeeze–Excitation and temporal self-attention (MS-CNN-Attention). Under product-grouped cross-validation, the deep model reached F1-macro = 0.811 (+30%) and graded 14 of 16 products correctly by majority vote, against 11 of 16 for the MLP, with the largest gain in the medium grade (F1: 0.52 → 0.79), where summary-statistic compression destroys the release-kinetic signal. The contributions are threefold: one programmable MOS sensor operated as a thermal-desorption profiler rather than a sensor array; a direct comparison of feature-based classical learning against raw-waveform deep learning on the same small, non-normally distributed dataset; and a product-level decision-consistency metric suited to batch screening. Pairing a low-cost MOS sensor with waveform-level modeling offers a rapid, non-destructive route to aroma-chemistry-based tea quality screening. Full article

Even before the discovery of DNA, Claude Shannon developed a mathematical model of Mendelian inheritance. Unlike the widely recognized Hardy–Weinberg equilibrium, Shannon’s genetic algebra has received little scholarly attention. Here, we revisit Shannon’s algebra and develop two complementary extensions for modern population genetics. First, we formulate a finite population version of Shannon’s framework, moving beyond the idealized infinite population setting to propagate allelic and genotypic frequencies under realistic sampling conditions. Second, we combine Shannon’s algebra with an analytical genotype–phenotype mapping framework to characterize genotypic configurations compatible with observed phenotypic frequencies, using auxiliary variables to express the inherent degeneracy of the genotype–phenotype relationship. Together, these extensions provide a unified framework in which phenotypic observations constrain the underlying genetic structure, and these constraints are propagated through inheritance via Shannon’s algebra. The resulting analytical expressions for offspring phenotypic distributions apply to both complete and incomplete penetrance and extend naturally to multilocus systems. This work highlights Shannon’s algebra as a flexible analytical tool for two complementary problems: (i) forward propagation of genetic information in finite populations, and (ii) analytical description of phenotypic inheritance from inferred genotypic information. Full article

Aqueous zinc–iodine batteries (AZIBs) are emerging as highly promising candidates for next-generation, grid-scale energy storage due to the intrinsic safety of water-based electrolytes, the high theoretical capacity of the zinc anode, and the rapid conversion kinetics of the iodine cathode. However, the practical commercialization of AZIBs is severely impeded by formidable interfacial instabilities, including the uncontrollable growth of zinc dendrites, parasitic hydrogen evolution reactions (HER), and the notorious polyiodide (I₃⁻, I₅⁻) shuttle effect. These macroscopic degradation modes are fundamentally rooted in the robust [Zn(H₂O)₆]²⁺ primary solvation sheath and the immense thermodynamic driving force for polyiodide dissolution in highly polar aqueous media. To address these interconnected challenges, electrolyte engineering has evolved into the most potent, holistic strategy. This comprehensive review systematically evaluates the latest advancements in electrolyte engineering for AZIBs. We first deeply decipher the fundamental thermodynamic mechanisms governing Zn²⁺ desolvation and iodine multiphase conversion. Subsequently, we critically analyze cutting-edge regulation paradigms, including water-in-salt (WIS) and localized high-concentration electrolytes (LHCE), cosolvent networks, functional molecular additives, deep eutectic solvents (DES), and quasi-solid-state hydrogels. By integrating in situ/operando spectroscopic characterizations with multiscale theoretical computations (such as MD and DFT), we elucidate the structure–activity relationships at the atomic level. Finally, we provide strategic perspectives on the future trajectories of the field, emphasizing the stabilization of multi-electron (I⁻/I⁰/I⁺) halogen chemistry, AI-driven high-throughput screening, and the rigorous standardization of Ah-level pouch cell engineering for extreme-environment applications. Full article

Lead sulfide quantum dots (PbS QDs), as a functional-layer coating, enable non-volatile integration and neuromorphic computing in memristive structures to address the von Neumann bottleneck. Herein, the dual-interface mechanism of PbS QDs in the memristor film structure is reviewed. First, the local electric field enhancement effect generates tip electrode-like structures in the coating film through QD-mediated spatial charge gradients, thereby enabling precise control over the nucleation and growth of conductive filaments (CFs). As a result, the consistency of switching voltages and the thermal stability at elevated temperatures are significantly improved. Conversely, the anion reservoir effect exploits surface dangling bonds on QDs to efficiently capture anions from the dielectric layer, thereby synergistically regulating vacancy migration kinetics. This process enables zero-initialization behavior and ultra-low-power operation. In addition, the spatial distribution design and density modulation of QDs further reinforce both mechanisms. The structural optimization of QD/dielectric interface engineering can simultaneously improve cycling endurance and resistive switching uniformity. Furthermore, modification of QD surface chemistry through ligand decoration and passivation suppresses the stochasticity of ionic diffusion while improving the linearity of synaptic weight updates. This interfacial engineering strategy utilizing QDs as coating films advances the development of high-performance photonic–electronic systems for memory–computing convergence. Full article

Bioprospecting for extremozymes from unique ecological niches is crucial for developing robust biocatalysts for green chemistry. Here, we report the de novo hybrid genome assembly of Penicillium crustosum CTM10622, isolated from the humid montane forest of El Feïdja National Park, Tunisia. Using Illumina NextSeq™ 500 and Nanopore PromethION 2 Solo, a highly contiguous 31.38 Mb assembly (N50 = 1.94 Mb; 98.3% BUSCOs) was achieved. This robust genomic foundation enabled the identification of an extensive hydrolase repertoire, leading to the discovery of a novel alkaline serine lipase, PCLIP, subsequently heterologously expressed in Pichia pastoris. Recombinant rPCLIP exhibited a high specific activity (15,000 U/mg at pH 10, 65 °C) and exceptional thermostability, with half-lives of 14 and 8 h at 80 and 90 °C, respectively. The enzyme’s identity as a serine lipase was confirmed by its complete inhibition by Orlistat or tetrahydrolipstatin (THL) (51 µM), PMSF (5 mM), and diisopropylfluorophosphate (DIFP) (2 mM). To determine its substrate specificity, advanced computational approaches, including convolutional neural network-based docking and explicitly solvated molecular dynamics, were employed to compare rPCLIP with its homologue PCrL, a recombinant serine alkaline lipase from Penicillium crustosum Thom P22. While rPCLIP showed optimal experimental activity toward short-chain glyceryl tributyrate, simulations revealed that long-chain trioctanoin acts as a ‘thermodynamic trap’ due to over-stabilization. Conversely, the rigid rPCrL favors tricaprylin, driven by a ‘hydrophobic engine’ effect where the solvated environment forces chain burial with minimal entropic penalty. The findings demonstrate that rPCLIP specificity is driven by a delicate interplay of geometric complementarity, Van der Waals enthalpy, and conformational entropy. Full article

Background/Objectives: Antimicrobial resistance and bacterial persistence underscore the need to develop new chemotypes with multifunctional antibacterial mechanisms. This study aimed to design, synthesize, and evaluate curcumin-inspired 3,5-diarylidene-4-piperidones as versatile small molecules exhibiting antibacterial, antibiofilm, anti-efflux, DNA gyrase-inhibitory, and antiproliferative properties. Methods: A targeted series of triazole-conjugated 3,5-diarylidene-4-piperidones was synthesized through copper-catalyzed azide-alkyne cycloaddition click chemistry and subsequently characterized using standard spectroscopic techniques. The compounds were assessed for antibacterial activity against Staphylococcus aureus, Enterococcus faecalis, and Escherichia coli. Selected active compounds underwent further evaluation for DNA gyrase inhibition, antibiofilm activity against multidrug-resistant S. aureus ATCC 33591, ethidium bromide accumulation, and antiproliferative effects on HCT116 and MCF7 cancer cells, with RPE1 cells serving as a control to evaluate cytotoxicity in normal cells. Additionally, computational studies, including QSAR analysis and molecular docking, were conducted to bolster structure–activity relationships and provide mechanistic insights. Results: Several derivatives demonstrated selective antibacterial activity against Gram-positive bacteria, particularly S. aureus, while exhibiting limited or no efficacy against E. coli. Compounds 7n and 7l emerged as the most potent against S. aureus, with minimum inhibitory concentrations (MICs) of 7.8 and 8.2 μM, respectively. Notably, compound 7l inhibited S. aureus DNA gyrase supercoiling, displaying an IC₅₀ of 3.20 μM, comparable to ciprofloxacin. Compound 7e exhibited the strongest antibiofilm activity against multidrug-resistant S. aureus, whereas compound 7a resulted in the highest accumulation of ethidium bromide, indicating robust anti-efflux activity. Antiproliferative assays revealed that select halogenated derivatives were effective against HCT116 and MCF7 cells, while the most promising antibacterial compounds exhibited minimal cytotoxicity toward RPE1 cells. Quantitative structure–activity relationship (QSAR) and docking studies supported the observed structure–activity relationships and suggested potential interactions with the ATPase binding site of DNA gyrase B. Conclusions: Triazole-conjugated 3,5-diarylidene-4-piperidones are promising multifunctional scaffolds with selective anti-S. aureus activity, antibiofilm and anti-efflux properties, and, for compound 7l, potent DNA gyrase inhibition. These findings support further optimization of this chemotype as a platform for developing antibacterial agents with polymechanistic activity. Full article

Background: The population of Serbia faces a significant burden from cardiovascular diseases (CVDs). This study aimed to computationally investigate genetic factors that contribute to the prevalence of these diseases by examining the possible involvement of common variants on lipid metabolism. Methods: We examined how a variant prevalent in the Serbian population, chr7:g.56019730G>A in the PSPH gene, affects the phosphoserine phosphatase (PSP) protein interaction network, particularly involved in lipid metabolism. The Informational Spectrum Method (ISM), method for the analysis of protein sequence based on amplitude changes, was applied to single out the top 10 affected interactors. Their further functional annotation identified the pathways in which they jointly participate with PSP. An additional strategy encompassed the investigation of variant combinations in all analyzed genes and potential relevance of prevalent variant combinations on lipid metabolism. Results: The PSP interactions affected by the R49W variant, such as SHMT1/2, were primarily in pathways associated with serine, glycine, and sphingolipid metabolism, highly relevant for CVD etiology. Further, we identified frequent variant combinations within the LRCH1, CEP126, PIK3CG, and PIKFYVE genes in the Serbian cohort. Conclusions: This study underscores the importance of investigating genetic variant combinations in complex diseases, and provides a hypothesis generating foundation for future research into the relationship between these genes and cardiovascular diseases. Full article

This two-year study proposes the application of industrial computed tomography (CT) as a complementary technique to conventional capacity and internal resistance measurements for evaluating not only the state of health (SOH) of different lithium-ion battery types used in electric vehicles, but also to predict its past. While commonly used assessment methods primarily focus on electrical properties of batteries, industrial CT allows non-destructive, three-dimensional visualization and systematic evaluation of internal structural changes within individual battery cells and allows to compare different lithium battery type internal structure changes. The study investigates two lithium-ion battery chemistries: lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC). The effects of different discharge rates (1C, 2C, and 3C) on battery degradation were analyzed by comparing CT scan data obtained for the cells in their initial (new) condition and after reaching 60% SOH following cycling-induced aging. The findings provide improved understanding of the physical processes associated with battery aging under varying discharge conditions, enabling a more complete evaluation of battery health. Full article

Background/Objectives: Poly (ADP-ribose) polymerase (PARP1) has emerged as a promising therapeutic target in human breast cancer particularly in BRCA1/2 mutation carriers where a synthetic lethal interaction leads to massive tumor cell death upon specific inhibitors’ administration. Current clinically approved PARP inhibitors (Talazoparib and Olaparib) show outstanding therapeutic capabilities but suffer from severe side effects. Most importantly, some of them can cause life-threatening cardiotoxicity through hERG off-target effects. Here, we performed an extensive study to identify lead compounds with improved binding modes and favorable predicted pharmacokinetics using an integrated computational strategy. Methods: An artificial intelligence-driven drug design (AIDDISON™ v2023) workflow was employed to search ultra-large chemical space libraries for active compounds, which were then optimized via computer-aided methods to form a PARP-Tailored Database (PTD). This database was then analyzed through a virtual screening workflow, molecular docking studies, molecular dynamics (MD) simulations, MM/GBSA binding free energy calculations, DFT analysis and ADME/Tox predictions using the Schrödinger suite (v2023-2), MobaXterm v25.2, Gaussian 16.0, ProTox-3 and Pred-hERG v5.0 respectively. Results: Three compounds (1a–1c) were identified as promising candidates. Among them 1a appeared to be the most active compound with a favorable docking score (−9.488 kcal/mol) that is not only higher than 1b and 1c but also higher than that of Talazoparib (−6.778 kcal/mol). MD simulations of 1a–1c in the active site revealed an average RMSD of ~2.5–3.6 Å which is better compared to the parent Talazoparib (5.6 Å). Interestingly, on the 250 ns extended MD study, 1a exhibited a slightly reduced RMSD between 2.4 and 3.2 Å, whereas Talazoparib retained higher fluctuations of ~5 Å to 6 Å. MM/GBSA binding energy analysis indicated 1a to have better predicted binding affinity (−67.820 kcal/mol), which is also better than Talazoparib (−63.734 kcal/mol). DFT calculations showed good electronic properties and in silico ADMET studies also indicated 1a to have good drug-likeness and lower predicted hepatotoxicity and cardiotoxicity risk. Conclusions: These findings identify compound 1a as a promising lead, while compounds 1b and 1c remain viable candidates for further optimization. However, experimental validation is critical to confirm the predicted biological activity and safety profiles. Full article

Imaging-based spatial transcriptomics has advanced from low-plex single-molecule fluorescence in situ hybridization to a diverse set of highly multiplexed platforms, with recent multimodal and pathology-compatible capabilities. Despite major differences in chemistry, coding, and imaging strategies across different platforms, their biological interpretation often converges on a few notable computational biology problems. This review examines imaging-based spatial transcriptomics through the lens of data interpretation and applications, focusing on the analytical framework that converts raw fluorescence signals or accompanying in situ sequencing data into molecule-, cell-, and tissue-level representations. We discuss the key challenges in preprocessing, registration, restoration, feature detection, barcode decoding, molecule calling, cell segmentation, transcript assignment, probabilistic cell typing, spatial-domain inference, and atlas integration. We also highlight how optical crowding, tissue thickness, panel bias, and multimodal complexity increase computational difficulty. Finally, we summarize applications of imaging-based spatial transcriptomics techniques, ranging from subcellular RNA localization to atlas-scale and pathology-aware spatial analysis. Full article

The aim of this research was to identify the wealth of secondary metabolites in the Chilean lichen Sticta caulescens, applying green chemistry approaches and comparing the following two extraction methods: (a) conventional maceration with methanol, and (b) microwave-assisted extraction (MAE) using ethyl lactate as a solvent. In addition, chemoinformatic and chemotaxonomic studies were conducted on S. caulescens and other species of the genus Sticta, which have been reported in previous studies. A UHPLC/ESI-MS/MS analysis allowed for the identification of 32 metabolites obtained from maceration and 33 from MAE, considering carbohydrates, aromatic compounds, acids, depsides, depsidones, dibenzofurans, lipids, anthraquinones, and triterpenes. Maceration using methanol yielded a slightly higher extract percentage than with ethyl lactate (6.3% versus 5.0%), while MAE extracted an almost identical spectrum of metabolites using ethyl lactate,—though including one compound detected only under MAE conditions. This highlighted both the method efficiency and selectivity. This study also incorporates a comprehensive chemoinformatic and chemotaxonomic analysis of secondary metabolites across 12 Sticta species. A computational comparison (Morgan fingerprints, Jaccard similarity, hierarchical clustering, Murcko scaffolds) demonstrated that S. caulescens is one of the most chemically diverse species, closely related to S. cordillerana, and forming part of a major chemotaxonomic lineage, which is characterized by high scaffold richness and shared aromatic/depsidone biosynthetic pathways. Full article

Synthesis of bifunctional cytisine–squaramide derivatives bearing a single amino acid moiety has revealed an unexpected and intriguing chemical challenge. During modification of cytisine squaramates with α-amino acids, base-sensitive amido esters readily underwent hydrolysis, forming poorly soluble amido-acid side products that resisted standard purification and initially obscured their identity. Persistent observation of these elusive precipitates prompted a deliberate co-crystallization approach, which unambiguously revealed their supramolecular nature using single-crystal X-ray diffraction. With this insight, optimized purification strategies allowed isolation of analytically pure Cyt-SQ-OH and its derivatives, which were characterized by complementary spectroscopic techniques, X-ray crystallography and computational studies. Furthermore, the DFT-optimized parameters of all compounds were determined, providing additional insight into their structural and electronic properties. This work highlights the interplay between reactivity, solubility, and supramolecular assembly in cytisine–squaramide-amino acid hybrids, providing a robust platform for future exploration of multifunctional conjugates with potential applications in medicinal chemistry, molecular recognition, and materials science. Full article