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Herein, we detail an extension of our research on the synthesis of a small library of furanoheliangolides and the characterization of the covalent interaction between goyazensolide and IPO5. Using a build‐couple‐pair strategy, we assembled a small library of germacrene‐type lactones and diversified them into eight groups of structurally different analogues. The germacrene lactones were synthesized using Sonogashira coupling and Barbier‐type macrocyclization, while the furanoheliangolides were further elaborated through gold‐catalyzed transannulation followed by esterification. This synthetic approach enabled the generation of a goyazensolide alkyne‐tagged cellular probe, which was used to identify the selective binding between goyazensolide and the oncoprotein importin‐5 (IPO5). Mass spectrometry analysis of the proteolytic digest from the reaction between the goyazensolide probe and a recombinant IPO5 indicated a covalent engagement at Cys560 of IPO5, which was confirmed by site‐directed mutagenesis. [ABSTRACT FROM AUTHOR]

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics‐based methods, such as dose–response metabolomics, stable isotope‐resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose‐dependent metabolite–drug interactions. Emerging techniques, including single‐cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field. [ABSTRACT FROM AUTHOR]

Natural products are a treasure trove for drug discovery, especially in the areas of infection, inflammation and cancer, due to their diverse bioactivities and complex, and varied structures. Chronic inflammation is closely related to many diseases, including complex diseases such as cancer and neurodegeneration. Improving target identification for natural products contributes to elucidating their mechanism of action and clinical progress. It also facilitates the discovery of novel druggable targets and the elimination of undesirable ones, thereby significantly enhancing the productivity of drug discovery and development. Moreover, the rise of polypharmacological strategies, considered promising for the treatment of complex diseases, will further increase the demand for target deconvolution. This review underscores strategies for identifying natural product targets (NPs) in the context of chronic inflammation over the past 5 years. These strategies encompass computational methodologies for early target discovery and the anticipation of compound binding sites, proteomics‐driven approaches for target delineation and experimental biology techniques for target validation and comprehensive mechanistic exploration. [ABSTRACT FROM AUTHOR]

Background: Identification of active components of traditional Chinese Medicine (TCM) and their respective targets is important for understanding the mechanisms underlying TCM efficacy. However, there are still no effective technical methods to achieve this. Methods: Herein, we have established a method for rapidly identifying targets of a specific TCM and interrogating the targets with their corresponding active components based on Isothermal Shift Assay (iTSA) and database interrogation. Results: We optimized iTSA workflow and identified 110 targets for Danhong injection (DHI) which is used as an effective remedy for cardiovascular and cerebrovascular diseases. Moreover, we identified the targets of the nine major ingredients found in DHI. Database interrogation found that the potential targets for DHI, in which we verified that ADK as the target for salvianolic acid A and ALDH1B1 as the target for protocatechualdehyde in DHI, respectively. Conclusion: Overall, we established a novel paradigm model for the identification of targets and their respective ingredients in DHI, which facilitates the discovery of drug candidates and targets for improving disease management and contributes to revealing the underlying mechanisms of TCM and fostering TCM development and modernization. Highlights: Utilizing the innovative iTSA approach, we identified the targets of DHI and their respective active components. Results demonstrated that salvianolic acid A in DHI targeted ADK while protocatechualdehyde targeted ALDH1B1 to exercise the functions of DHI. The established workflow can facilitate a deeper understanding of action mechanisms of TCM. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI's applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI's transformative impact on the pharmaceutical industry and its broader implications for healthcare. [ABSTRACT FROM AUTHOR]

Objectives: Network pharmacology is essential for understanding the multi-target and multi-pathway therapeutic mechanisms of traditional Chinese medicine. This study aims to evaluate the influence of database quality on target identification and to explore the therapeutic potential of rhynchophylline (Rhy) in treating overactive bladder (OAB). Methods: An OAB dataset was constructed through extensive literature screening. Using this dataset, we applied network pharmacology to predict potential targets for Rhy, which is known for its therapeutic effects but lacks a well-defined target profile. Predicted targets were validated through in vitro experiments, including DARTS and CETSA. Results: Our analysis identified Rhy as a potential modulator of the M3 receptor and TRPM8 channel in the treatment of OAB. Validation experiments confirmed the interaction between Rhy and these targets. Additionally, the GeneCards database predicted other targets that are not directly linked to OAB, corroborated by the literature. Conclusions: We established a more accurate and comprehensive dataset of OAB targets, enhancing the reliability of target identification for drug treatments. This study underscores the importance of database quality in network pharmacology and contributes to the potential therapeutic strategies for OAB. [ABSTRACT FROM AUTHOR]

Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small‐molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off‐target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off‐target effects we focused on the hybridization‐dependent, especially on the miRNA‐like off‐target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization‐dependent off‐target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization‐dependent off‐target effects, this review provides an overview of all major state‐of‐the‐art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off‐target effects and hopefully to avoid off‐target‐related adverse events of small RNA therapeutics. [ABSTRACT FROM AUTHOR]

Accurate identification of coastal hyperspectral remote sensing targets plays a significant role in the observation of marine ecosystems. Deep learning is currently widely used in hyperspectral recognition. However, most deep learning methods ignore the complex correlation and data loss problems that exist between features at different scales. In this study, Multi-scale attention reconstruction convolutional network (MARCN) is proposed to address the above issues. Firstly, a multi-scale attention mechanism is introduced into the network to optimize the feature extraction process, enabling the network to capture feature information at different scales and improve the target recognition performance. Secondly, the reconstruction module is introduced to fully utilize the spatial and spectral information of hyperspectral imagery, which effectively solves the problem of losing spatial and spectral information. Finally, an adaptive loss function, coupling cross-entropy loss, center loss, and feature space loss is used to enable the network to learn the feature representation and improve the accuracy of the model. The experimental results showed that the effectiveness of MARCN was validated with a recognition rate of 96.62%, and 97.92% on the YRE and GSOFF datasets. [ABSTRACT FROM AUTHOR]

The membrane composition of extracellular vesicles (EVs) largely reflects that of the plasma membrane of the cell of origin. We therefore hypothesized that EVs could be used for immunizations to generate monoclonal antibodies against well-known tumor antigens but possibly also against hitherto unknown tumor-associated target molecules. From an immunization experiment, we obtained a monoclonal antibody specific for SRRM2, an RNA-binding protein involved in splicing and a major component of nuclear speckles. Here, we used this antibody to demonstrate that SRRM2 is exposed on the surface of most cancer cell lines from various entities and, even more important, on cancer cells in vivo. Moreover, we demonstrated that SRRM2-specific CAR-T cells are functional in vitro and in vivo. Collectively, we identified SRRM2 as a promising new target molecule exposed on the cancer cell surface and showed that our SRRM2-specific antibody can be used as a basis for the development of new targeted cancer therapies. [ABSTRACT FROM AUTHOR]

An integrated platform was developed for NP-inspired PROTAC design, target identification and drug discovery. As a proof-of-concept study, this platform was applied to target characterization of 3-fluoro-10-hydroxylevodiamine, which highlighted the superiority of PROTAC technology in target identification of NPs and accelerated the process of NPs-based drug discovery. [Display omitted] • A new strategy combining PROTAC derivatization, quantitative proteomic analysis and binding affinity validation was developed for target identification and drug discovery of natural products. • The PROTAC-based target identitication strategy was successfully applied to characterize REXO4 as a direct target of 3-fluoro-10-hydroxylevodiamine. • Evodiamine-inspired PROTAC 13c showed potent antitumor activity and reduced toxic side effects through REXO4 degradation. Natural products (NPs) play a crucial role in the development of therapeutic drugs. However, it is still highly challenging to identify the targets of NPs. Besides, NPs usually exert their pharmacological activities via acting on multiple targets or pathways, which also poses great difficulties for the target identification of NPs. Inspired by our continuous efforts in designing drug-like protein degraders, this study introduced a successful example for the target identification and drug discovery of natural products evodiamine by employing PROTAC technology. Taking advantages of proteolysis targeting chimera (PROTAC), herein an integrated strategy combining PROTAC derivatization, quantitative proteomic analysis and binding affinity validation was developed for target identification and drug discovery of antitumor NP evodiamine. In this study, both highly potent PROTACs and negative controls were designed for quantitative proteomic analysis. Furthermore, REXO4 was confirmed as a direct target of 3-fluoro-10-hydroxylevodiamine, which induced cell death through ROS. In addition, the PROTAC 13c effectively degraded REXO4 both in vitro and in vivo , leading to potent antitumor activities and reduced toxic side effects. In summary, we developed an integrated strategy for the target identification and drug discovery of NPs, which was successfully applied to the PROTAC derivatization and target characterization of evodiamine. This proof-of-concept study highlighted the superiority of PROTAC technology in target identification of NPs and accelerated the process of NPs-based drug discovery, exhibiting broad application in NP-based drug development. [ABSTRACT FROM AUTHOR]

Abstract Background Identification of active components of traditional Chinese Medicine (TCM) and their respective targets is important for understanding the mechanisms underlying TCM efficacy. However, there are still no effective technical methods to achieve this. Methods Herein, we have established a method for rapidly identifying targets of a specific TCM and interrogating the targets with their corresponding active components based on Isothermal Shift Assay (iTSA) and database interrogation. Results We optimized iTSA workflow and identified 110 targets for Danhong injection (DHI) which is used as an effective remedy for cardiovascular and cerebrovascular diseases. Moreover, we identified the targets of the nine major ingredients found in DHI. Database interrogation found that the potential targets for DHI, in which we verified that ADK as the target for salvianolic acid A and ALDH1B1 as the target for protocatechualdehyde in DHI, respectively. Conclusion Overall, we established a novel paradigm model for the identification of targets and their respective ingredients in DHI, which facilitates the discovery of drug candidates and targets for improving disease management and contributes to revealing the underlying mechanisms of TCM and fostering TCM development and modernization. Graphical Abstract

Identification of interactions between proteins and natural products or similar active small molecules is crucial for understanding of their mechanism of action on a molecular level. To search elusive, often labile, and low-abundant conjugates between proteins and active compounds, chemical proteomics introduces a feasible strategy that allows to enrich and detect these conjugates. Recent advances in mass spectrometry techniques and search algorithms provide unprecedented depth of proteome coverage and the possibility to detect desired modified peptides with high sensitivity. The chemical ‘linker’ connecting an active compound–protein conjugate with a detection tag is the critical component of all chemical proteomic workflows. In this review, we discuss the properties and applications of different chemical proteomics linkers with special focus on their fragmentation releasing diagnostic ions and how these may improve the confidence in identified active compound–peptide conjugates. The application of advanced search options improves the identification rates and may help to identify otherwise difficult to find interactions between active compounds and proteins, which may result from unperturbed conditions, and thus are of high physiological relevance.

Introduction: Natural products (NPs) play a crucial role in the development of therapeutic drugs. However, it is still highly challenging to identify the targets of NPs. Besides, NPs usually exert their pharmacological activities via acting on multiple targets or pathways, which also poses great difficulties for the target identification of NPs. Objectives: Inspired by our continuous efforts in designing drug-like protein degraders, this study introduced a successful example for the target identification and drug discovery of natural products evodiamine by employing PROTAC technology. Methods: Taking advantages of proteolysis targeting chimera (PROTAC), herein an integrated strategy combining PROTAC derivatization, quantitative proteomic analysis and binding affinity validation was developed for target identification and drug discovery of antitumor NP evodiamine. Results: In this study, both highly potent PROTACs and negative controls were designed for quantitative proteomic analysis. Furthermore, REXO4 was confirmed as a direct target of 3-fluoro-10-hydroxylevodiamine, which induced cell death through ROS. In addition, the PROTAC 13c effectively degraded REXO4 both in vitro and in vivo, leading to potent antitumor activities and reduced toxic side effects. Conclusion: In summary, we developed an integrated strategy for the target identification and drug discovery of NPs, which was successfully applied to the PROTAC derivatization and target characterization of evodiamine. This proof-of-concept study highlighted the superiority of PROTAC technology in target identification of NPs and accelerated the process of NPs-based drug discovery, exhibiting broad application in NP-based drug development.

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Centromere protein A (CENP‐A), a centromere‐specific histone H3 variant, is crucial for kinetochore positioning and chromosome segregation. However, its regulatory mechanism in human cells remains incompletely understood. A structure‐activity relationship (SAR) study of the cell‐cycle‐arresting indole terpenoid mimic JP18 leads to the discovery of two more potent analogs, (+)‐6‐Br‐JP18 and (+)‐6‐Cl‐JP18. Tubulin is identified as a potential cellular target of these halogenated analogs by using the drug affinity responsive target stability (DARTS) based method. X‐ray crystallography analysis reveals that both molecules bind to the colchicine‐binding site of β‐tubulin. Treatment of human cells with microtubule‐targeting agents (MTAs), including these two compounds, results in CENP‐A accumulation by destabilizing Cdh1, a co‐activator of the anaphase‐promoting complex/cyclosome (APC/C) E3 ubiquitin ligase. This study establishes a link between microtubule dynamics and CENP‐A accumulation using small‐molecule tools and highlights the role of Cdh1 in CENP‐A proteolysis. [ABSTRACT FROM AUTHOR]

Simple Summary: Human beings are always associated with small molecules throughout their lives. Chemical proteomics, which selectively identifies small-molecule targets, is an emerging approach for unraveling the mechanism of action of small molecules for therapeutic target identification and drug discovery. This review attempts to introduce representative techniques of chemical proteomics and summarizes examples of identifying small-molecule targets. Throughout the human lifespan, from conception to the end of life, small molecules have an intrinsic relationship with numerous physiological processes. The investigation into small-molecule targets holds significant implications for pharmacological discovery. The determination of the action sites of small molecules provide clarity into the pharmacodynamics and toxicological mechanisms of small-molecule drugs, assisting in the elucidation of drug off-target effects and resistance mechanisms. Consequently, innovative methods to study small-molecule targets have proliferated in recent years, with chemical proteomics standing out as a vanguard development in chemical biology in the post-genomic age. Chemical proteomics can non-selectively identify unknown targets of compounds within complex biological matrices, with both probe and non-probe modalities enabling effective target identification. This review attempts to summarize methods and illustrative examples of small-molecule target identification via chemical proteomics. It delves deeply into the interactions between small molecules and human biology to provide pivotal directions and strategies for the discovery and comprehension of novel pharmaceuticals, as well as to improve the evaluation of drug safety. [ABSTRACT FROM AUTHOR]

MicroRNA482 (miR482) is a conserved microRNA family in plants, playing critical regulatory roles in different biological activities. Though the members of the miR482 gene family have been identified in plants, a systematic study has not been reported yet. In the present research, 140 mature sequences generated by 106 precursors were used for molecular characterization, phylogenetic analysis, and target gene prediction, and the competing endogenous RNA (ceRNA) network mediated by miR482 was summarized. The length of mature sequences ranged from 17 nt to 25 nt, with 22 nt being the most abundant, and the start and end of the mature sequences had a preference for uracil (U). By sequence multiplex comparison, it was found that the mature sequences of 5p were clustered into one group, and others were clustered into the other group. Phylogenetic analysis revealed that the 140 mature sequences were categorized into six groups. Meanwhile, all the precursor sequences formed a stable hairpin structure, and the 106 precursors were divided into five groups. However, the expression of miR482 varied significantly between different species and tissues. In total, 149 target genes were predicted and their functions focused on single-organism process, cellular process, and cell and cell part. The ceRNA network of miR482 in tomato, cotton, and peanut was summarized based on related publications. In conclusion, this research will provide a foundation for further understanding of the miR482 gene family. [ABSTRACT FROM AUTHOR]

Metabolic disorders, including obesity, dyslipidemia, diabetes, nonalcoholic fatty liver disease, and metabolic syndrome, are characterized by insulin resistance, abnormalities in circulating cholesterol and lipid profiles, and hypertension. The most common pathophysiologies of metabolic disorders are glucose/lipid metabolism dysregulation, insulin resistance, inflammatory response, and oxidative stress. Although several agents have been approved for the treatment of metabolic disorders, there is still a strong demand for more efficacious drugs with less side effects. Natural products have been critical sources of drug research and discovery for decades. However, the usefulness of bioactive natural products is often limited by incomplete understanding of their direct cellular targets. In this review, we highlight the current understanding of the established and emerging molecular mechanisms of metabolic disorders. We further summarize the therapeutic effects and underlying mechanisms of natural products on metabolic disorders, with highlights on their direct cellular targets, which are mainly implicated in the regulation of glucose/lipid metabolism, insulin resistance, metabolic inflammation, and oxidative stress. Finally, this review also covers the clinical studies of natural products in metabolic disorders. These progresses are expected to facilitate the application of these natural products and their derivatives in the development of novel drugs against metabolic disorders. [ABSTRACT FROM AUTHOR]

Chemical proteomics using biotin probes of natural products have significantly advanced our understanding of molecular targets and therapeutic potential. This review highlights recent progress in the application of biotin probes of homoisoflavonoids for identifying binding proteins and elucidating mechanisms of action. Notably, homoisoflavonoids exhibit antiangiogenic, anti-inflammatory, and antidiabetic effects. A combination of biotin probes, pull-down assays, mass spectrometry, and molecular modeling has revealed how natural products and their derivatives interact with several proteins such as ferrochelatase (FECH), soluble epoxide hydrolase (sEH), inosine monophosphate dehydrogenase 2 (IMPDH2), phosphodiesterase 4 (PDE4), and deoxyhypusine hydroxylase (DOHH). These target identification approaches pave the way for new therapeutic avenues, especially in the fields of oncology and ophthalmology. Future research aimed at expanding the repertoire of target identification using biotin probes of homoisoflavonoids promises to further elucidate the complex mechanisms and develop new drug candidates. [ABSTRACT FROM AUTHOR]

Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50–100, 100–150, and 250–300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50–100, 100–150, 150–200, and 200–250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure‐based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts. [ABSTRACT FROM AUTHOR]

The traditional drug discovery process is expensive, time-consuming, and often leads to a high failure rate. The development of numerous new medications in the pharmaceutical sciences is only one example of how the advancement of artificial intelligence has opened up exciting new opportunities for developing intelligent modelling. Machine learning and deep learning are two examples of artificial intelligence that can sift through large datasets in search of promising new drugs. AI algorithms can predict the binding affinity of molecules to specific targets, helping researchers narrow down the pool of potential drug candidates. Pharmacokinetics and pharmacodynamic are essential aspects of drug development. Drug formulation development requires extensive testing and optimization of various parameters. AI models can quickly analyze data from multiple experiments and identify the most promising formulations, saving time and resources. New pharmaceuticals may be developed and brought to market at a reduced cost and in a shorter amount of time with the use of AI-based optimisation approaches. Absorption, Distribution, Metabolism, and Excretion (ADME) are only some of the aspects of pharmacological physiology that may be modelled and predicted with the use of artificial intelligence. By integrating AI models into the drug development process, researchers can gain a deeper understanding of a drug's pharmacokinetic and pharmacodynamic properties. This knowledge helps in designing drugs with improved efficacy and reduced side effects. So, in present topic authors tried to give insights how AI is playing a transformative role in pharmaceutical sciences. As AI technology continues to advance, the future of pharmaceutical sciences looks brighter than ever. [ABSTRACT FROM AUTHOR]

Background: Microbial communities are important drivers of global biogeochemical cycles, xenobiotic detoxification, as well as organic matter decomposition. Their major metabolic role in ecosystem functioning is ensured by a unique set of enzymes, providing a tremendous yet mostly hidden enzymatic potential. Exploring this enzymatic repertoire is therefore not only relevant for a better understanding of how microorganisms function in their natural environment, and thus for ecological research, but further turns microbial communities, in particular from extreme habitats, into a valuable resource for the discovery of novel enzymes with potential applications in biotechnology. Different strategies for their uncovering such as bioprospecting, which relies mainly on metagenomic approaches in combination with sequence-based bioinformatic analyses, have emerged; yet accurate function prediction of their proteomes and deciphering the in vivo activity of an enzyme remains challenging. Results: Here, we present environmental activity-based protein profiling (eABPP), a multi-omics approach that extends genome-resolved metagenomics with mass spectrometry-based ABPP. This combination allows direct profiling of environmental community samples in their native habitat and the identification of active enzymes based on their function, even without sequence or structural homologies to annotated enzyme families. eABPP thus bridges the gap between environmental genomics, correct function annotation, and in vivo enzyme activity. As a showcase, we report the successful identification of active thermostable serine hydrolases from eABPP of natural microbial communities from two independent hot springs in Kamchatka, Russia. Conclusions: By reporting enzyme activities within an ecosystem in their native state, we anticipate that eABPP will not only advance current methodological approaches to sequence homology-guided enzyme discovery from environmental ecosystems for subsequent biocatalyst development but also contributes to the ecological investigation of microbial community interactions by dissecting their underlying molecular mechanisms. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation of the effect of clutter on the performance of microwave imaging and target identification using ground-penetrating radar (GPR) for buried objects. Signals collected by the GPR were formed to be B-scan and 3D images to carry out the microwave imaging of buried objects. Poles extracted using the short-time matrix pencil method from a late-time response of the received signal collected at a specific position were exploited to identify buried objects. The experiment was set up by constructing a large box filled with sand where an L-shaped metallic sheet was buried. The GPR was installed along with a 2-dimensional scanner above the box to image and identify the buried metallic sheet. In order to investigate the effect of the clutter on the performance of the GPR-based microwave imaging and target identification, small rocks were arranged on the sand surface, and their density was then varied. The experimental results have shown that clutter density significantly affects the sharpness of the B-scan and 3D images. The rocks result in an additional surface layer in the images. Moreover, the results have also shown that the clutter does not affect the performance of the identification of the buried object. From the results, the poles obtained from the GPR scanning inside and outside the buried object region differed. This confirms that the poles of the buried object should be different from those without the object. The poles of the buried object remain constant, although rocks on the sand surface exist since the clutter affects only the early-time response (not the late-time response). With varying clutter densities, the underlying poles were significantly changed, but this does not affect the identification of the buried object. [ABSTRACT FROM AUTHOR]

Background and Purpose: Because of cervical cancer (CC) metastasis, the prognosis of diagnosed patients is poor. However, the molecular mechanisms and therapeutic approach for metastatic CC remain elusive. Experimental Approach: In this study, we first evaluated the effect of resveratrol (RSV) on CC cell migration and metastasis. Via an activity‐based protein profiling (ABPP) approach, a photoaffinity probe of RSV (RSV‐P) was synthesized, and the protein targets of RSV in HeLa cells were identified. Based on target information and subsequent in vivo and in vitro validation experiments, we finally elucidated the mechanism of RSV corresponding to its antimetastatic activity. Key Results: The results showed that RSV concentration‐dependently suppressed CC cell migration and metastasis. A list of proteins was identified as the targets of RSV, through the ABPP approach with RSV‐P, among which fatty acid binding protein 5 (FABP5) attracted our attention based on The Cancer Genome Atlas (TCGA) database analysis. Subsequent knockout and overexpression experiments confirmed that RSV directly interacted with FABP5 to inhibit fatty acid transport into the nucleus, thereby suppressing downstream matrix metalloproteinase‐2 (MMP2) and matrix metalloproteinase‐9 (MMP9) expression, thus inhibiting CC metastasis. Conclusions and Implications: Our study confirmed the key role of FABP5 in CC metastasis and provided important target information for the design of therapeutic lead compounds for metastatic CC. [ABSTRACT FROM AUTHOR]

Abstract Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics‐based methods, such as dose–response metabolomics, stable isotope‐resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose‐dependent metabolite–drug interactions. Emerging techniques, including single‐cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

BackgroundCoronary artery calcification (CAC) is associated with an increased risk of mortality and cardiovascular events. However, none therapeutic drugs have been proven effective for CAC treatment. The objective of this study was to identify potential therapeutic targets for CAC through the utilization of Mendelian randomization (MR) and colocalization analysis.MethodsThe expression quantitative trait loci (eQTLs) of 16,943 genes from the eQTLGen consortium and protein quantitative trait loci (pQTLs) of 4,412 proteins from a plasma proteome were utilized as genetic instruments. Genetic associations with CAC were derived from a GWAS meta-analysis of 26,909 individuals. The MR and colocalization analysis were utilized to identify potential target genes.ResultsA total of 671 genes were found to be significantly associated with the risk of CAC based on transcriptomic MR analysis at a false discovery rate

Proteolysis targeting chimeras (PROTACs), as a novel therapeutic drug model, has received widespread attention from academia and the pharmaceutical industry. PROTAC technology has led researchers to focus on developing chemical biology tool properties due to the unique operating mechanism and protein dynamic regulatory properties. In recent years the rapid development of PROTAC technology has gradually made PROTACs an essential tool for target identification and validation. To further promote the application of PROTAC tools in drug discovery and basic medical science research, this review distinguished target identification and validation concepts. Furthermore, research progress in PROTAC technology was summarized.

Abstract For understanding a chemical compound’s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top $$20\%$$ 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling. Graphical Abstract

Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.

In this work we connect machine learning techniques, in particular kernel machine learning, to inverse source and scattering problems. We show the proposed kernel machine learning has demonstrated generalization capability and has a rigorous mathematical foundation. The proposed learning is based on the Mercer kernel, the reproducing kernel Hilbert space, the kernel trick, as well as the mathematical theory of inverse source and scattering theory, and the restricted Fourier integral operator. The kernel machine learns a multilayer neural network which outputs an \epsilon -neighborhood average of the unknown or its nonlinear transformation. We then apply the general architecture to the multifrequency inverse source problem for a fixed observation direction and the Born inverse medium scattering problem. We establish a mathematically justified kernel machine indicator with demonstrated capability in both shape identification and parameter identification, under very general assumptions on the physical unknowns. More importantly, stability estimates are established in the case of both noiseless and noisy measurement data. Of central importance is the interplay between a restricted Fourier integral operator and a corresponding Sturm--Liouville differential operator. Several numerical examples are presented to demonstrate the capability of the proposed kernel machine learning. [ABSTRACT FROM AUTHOR]

Recent attempts to estimate the biomass of chokka squid Loligo reynaudii on the southeast coast of South Africa by acoustic surveys of the inshore jig-fishing area are described. Pilot surveys of aggregations, covering part of the fishing grounds, were conducted in 2019 and 2020, followed by a multiphase survey of the entire grounds within the 50-m depth contour by two vessels in 2021. Biomass estimates were made using previous in situ and ex situ estimates of the target strength of L. reynaudii at 38 kHz, and our knowledge of the physical characteristics of spawning aggregations from acoustic studies over the past two decades. Biomass estimates from the 2021 survey ranged from 343 tonnes (coefficient of variation [CV] 47%) for targets conclusively identified as squid, to 2 365 tonnes (CV 9%) if all targets that could possibly have been squid were included. An estimate of 635 tonnes (CV 20%) is proposed as the most reasonable from the survey, although its worth as an absolute estimate is questionable mainly because of identification and target-strength uncertainties. We conclude that a series of wide-area acoustic estimates could provide an additional index of relative abundance for fitting in the current squid assessment model. This would be useful in managing the jig fishery and for improving our understanding of changes in chokka squid abundance and distribution in response to changes in the environment. [ABSTRACT FROM AUTHOR]

This study focuses on developing advanced methods for the identification and classification of objects in complex environments. Over the past two years, there has been an increase in the use of advanced technologies in various challenging scenarios. This research is centered on accurately identifying targets and tracking them. The study addresses challenges related to object detection in multi-dimensional and intricate settings, taking into account natural conditions like rain and fog, as well as technical limitations such as camera capabilities. Special emphasis is placed on data collection for training the identification model, followed by extensive data preprocessing, including cleaning, labeling, and augmentation. The research employs YOLO and Deep Sort machine learning algorithms, focusing on improving the accuracy and reliability of target recognition and increasing data processing speed to minimize misidentification risks. The integration of YOLO, known for its quick real-time object detection, with Deep Sort, which excels in detailed feature extraction and classification, forms the basis of our methodology. This fusion is a complex combination of both models' strengths, with YOLO quickly identifying relevant objects and Deep Sort performing an in-depth analysis. The experimental phase involves extensive testing of the models in varied weather conditions and settings to evaluate performance under challenging circumstances. This work aims to enhance object identification techniques in complex environments, a critical aspect for the effectiveness of various advanced operations. The findings are expected to significantly contribute to the field by enabling quicker and more accurate target identification. [ABSTRACT FROM AUTHOR]

Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing. [ABSTRACT FROM AUTHOR]

For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/. Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling. [ABSTRACT FROM AUTHOR]

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms—a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial–mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF. [ABSTRACT FROM AUTHOR]

Abstract Metabolic disorders, including obesity, dyslipidemia, diabetes, nonalcoholic fatty liver disease, and metabolic syndrome, are characterized by insulin resistance, abnormalities in circulating cholesterol and lipid profiles, and hypertension. The most common pathophysiologies of metabolic disorders are glucose/lipid metabolism dysregulation, insulin resistance, inflammatory response, and oxidative stress. Although several agents have been approved for the treatment of metabolic disorders, there is still a strong demand for more efficacious drugs with less side effects. Natural products have been critical sources of drug research and discovery for decades. However, the usefulness of bioactive natural products is often limited by incomplete understanding of their direct cellular targets. In this review, we highlight the current understanding of the established and emerging molecular mechanisms of metabolic disorders. We further summarize the therapeutic effects and underlying mechanisms of natural products on metabolic disorders, with highlights on their direct cellular targets, which are mainly implicated in the regulation of glucose/lipid metabolism, insulin resistance, metabolic inflammation, and oxidative stress. Finally, this review also covers the clinical studies of natural products in metabolic disorders. These progresses are expected to facilitate the application of these natural products and their derivatives in the development of novel drugs against metabolic disorders.

Abstract Centromere protein A (CENP‐A), a centromere‐specific histone H3 variant, is crucial for kinetochore positioning and chromosome segregation. However, its regulatory mechanism in human cells remains incompletely understood. A structure‐activity relationship (SAR) study of the cell‐cycle‐arresting indole terpenoid mimic JP18 leads to the discovery of two more potent analogs, (+)‐6‐Br‐JP18 and (+)‐6‐Cl‐JP18. Tubulin is identified as a potential cellular target of these halogenated analogs by using the drug affinity responsive target stability (DARTS) based method. X‐ray crystallography analysis reveals that both molecules bind to the colchicine‐binding site of β‐tubulin. Treatment of human cells with microtubule‐targeting agents (MTAs), including these two compounds, results in CENP‐A accumulation by destabilizing Cdh1, a co‐activator of the anaphase‐promoting complex/cyclosome (APC/C) E3 ubiquitin ligase. This study establishes a link between microtubule dynamics and CENP‐A accumulation using small‐molecule tools and highlights the role of Cdh1 in CENP‐A proteolysis.

Infections and diseases caused by parasitic nematodes have a major adverse impact on the health and productivity of animals and humans worldwide. The control of these parasites often relies heavily on the treatment with commercially available chemical compounds (anthelmintics). However, the excessive or uncontrolled use of these compounds in livestock animals has led to major challenges linked to drug resistance in nematodes. Therefore, there is a need to develop new anthelmintics with novel mechanism(s) of action. Recently, we identified a small molecule, designated UMW-9729, with nematocidal activity against the free-living model organism Caenorhabditis elegans. Here, we evaluated UMW-9729's potential as an anthelmintic in a structure-activity relationship (SAR) study in C. elegans and the highly pathogenic, blood-feeding Haemonchus contortus (barber's pole worm), and explored the compound-target relationship using thermal proteome profiling (TPP). First, we synthesised and tested 25 analogues of UMW-9729 for their nematocidal activity in both H. contortus (larvae and adults) and C. elegans (young adults), establishing a preliminary nematocidal pharmacophore for both species. We identified several compounds with marked activity against either H. contortus or C. elegans which had greater efficacy than UMW-9729, and found a significant divergence in compound bioactivity between these two nematode species. We also identified a UMW-9729 analogue, designated 25, that moderately inhibited the motility of adult female H. contortus in vitro. Subsequently, we inferred three H. contortus proteins (HCON_00134350, HCON_00021470 and HCON_00099760) and five C. elegans proteins (F30A10.9, F15B9.8, B0361.6, DNC-4 and UNC-11) that interacted directly with UMW-9729; however, no conserved protein target was shared between the two nematode species. Future work aims to extend the SAR investigation in these and other parasitic nematode species, and validate individual proteins identified here as possible targets of UMW-9729. Overall, the present study evaluates this anthelmintic candidate and highlights some challenges associated with early anthelmintic investigation.

Target identification remains a critical challenge in inorganic drug discovery to deconvolute potential polypharmacology. Herein, we describe an improved approach to prioritize candidate protein targets based on a combination of dose-dependent chemoproteomics and treatment effects in living cancer cells for the rhenium tricarbonyl compound TRIP. Chemoproteomics revealed 89 distinct dose-dependent targets with concentrations of competitive saturation between 0.1 and 32 μM despite the broad proteotoxic effects of TRIP. Target-response networks revealed two highly probable targets of which the Fe-S cluster biogenesis factor NUBP2 was competitively saturated by free TRIP at nanomolar concentrations. Importantly, TRIP treatment led to a down-regulation of Fe-S cluster containing proteins and upregulated ferritin. Fe-S cluster depletion was further verified by assessing mitochondrial bioenergetics. Consequently, TRIP emerges as a first-in-class modulator of the scaffold protein NUBP2, which disturbs Fe-S cluster biogenesis at sub-cytotoxic concentrations in ovarian cancer cells.

Synthetic aperture radar (SAR) is widely used for ship target detection with the application of deep learning techniques. However, in certain complex environments, such as near shore or with small ships, the problem of false alarms and missed detections still exists. To address these issues, a high-precision ship target detection method named DBW-YOLO, which builds upon YOLOv7-tiny as its foundational network, is proposed in this article. The proposed method consists of the following main steps. First, a feature extraction enhancement network based on deformable convolution network is introduced to obtain more comprehensive feature representations across various ship types. Second, an adaptive feature recognition method based on BiFormer attention mechanism is proposed to strengthen detection accuracy, which is more beneficial to capture near shore ships and small ships. Third, a wise intersection-over-union based on dynamic nonmonotonic focusing mechanism is proposed to generate the loss function, which improves the convergence speed and generalization ability. Consequently, the DBW-YOLO method trains a more robust model that better utilizes samples from near shore and small ships. To verify the effectiveness of this method, two SAR datasets, HRSID and SSDD, are employed for performance evaluation. Compared to other widely-used methods, the mAP value of DBW-YOLO reachs 88.84% and 99.18% on the HRSID and SSDD datasets, respectively. The findings indicate that DBW-YOLO method outperforms other representative SAR ship detection methods in both accuracy and overall performance.

With the rapid growth of social media culture, the use of offensive or hateful language has surged, which necessitates the development of effective abusive language detection models for online platforms. This paper focuses on developing a multi-class classification model to identify different types of offensive language. The input data is taken in the form of labeled tweets and is classified into offensive language detection, offensive language categorization, and offensive language target identification. The data undergoes pre-processing, which removes NaN value and punctuation, as well as performs tokenization followed by the generation of a word cloud to assess data quality. Further, the tf-idf technique is used for the selection of features. In the case of classifiers, multiple deep learning techniques, namely, bidirectional gated recurrent unit, multi-dense long short-term memory, bidirectional long short-term memory, gated recurrent unit, and long short-term memory, are applied where it has been found that all the models, except long short-term memory, achieved a high accuracy of 99.9 % for offensive language target identification. Bidirectional LSTM and multi-dense LSTM obtained the lowest loss and RMSE values of 0.01 and 0.1, respectively. This research provides valuable insights and contributes to the development of effective abusive language detection methods to promote a safe and respectful online environment. The insights gained can aid platform administrators in efficiently moderating content and taking appropriate actions against offensive language.

The widespread use of artemisinin (ART) and its derivatives has significantly reduced the global burden of malaria; however, malaria still poses a serious threat to global health. Although significant progress has been achieved in elucidating the antimalarial mechanisms of ART, the most crucial target proteins and pathways of ART remain unknown. Knowledge on the exact antimalarial mechanisms of ART is urgently needed, as signs of emerging ART resistance have been observed in some regions of the world. Here, we used a combined strategy involving mass spectrometry-coupled cellular thermal shift assay (MS-CETSA) and transcriptomics profiling to identify a group of putative antimalarial targets of ART. We then conducted a series of validation experiments on five prospective protein targets, demonstrating that ART may function against malaria parasites by interfering with redox homeostasis, lipid metabolism, and protein synthesis processes. Taken together, this study provides fresh perspectives on the antimalarial mechanisms of ART and identifies several crucial proteins involved in parasite survival that can be targeted to combat malaria.