In recent years, there has been a growing interest in the research on NOD-like receptor thermal protein domain associated protein 3(NLRP3) inflammasome inhibitors in the treatment of inflammatory diseases. The NLRP3 inflammasome is integral to the innate immune response, and its abnormal activation can lead to the release of pro-inflammatory cytokine, consequently facilitating the progression of various pathological conditions. Therefore, investigating the pharmacological inhibition pathway of the NLRP3 inflammasome represents a promising strategy for the treatment of inflammation-related diseases. Currently, the Food and Drug Administration(FDA) has not approved drugs targeting the NLRP3 inflammasome for clinical use due to concerns regarding liver toxicity and gastrointestinal side effects associated with chemical small molecule inhibitors in clinical trials. Natural small molecule compounds such as polyphenols, flavonoids, and alkaloids are ubiquitously found in animals, plants, and other natural substances exhibiting pharmacological activities. Their abundant sources, intricate and diverse structures, high biocompatibility, minimal adverse reactions, and superior biochemical potency in comparison to synthetic compounds have attracted the attention of extensive scholars. Currently, certain natural small molecule compounds have been demonstrated to impede the activation of the NLRP3 inflammasome via various action mechanisms, so they are viewed as the innovative, feasible, and minimally toxic therapeutic agents for inhibiting NLRP3 inflammasome activation in the treatment of both acute and chronic inflammatory diseases. Hence, this study systematically examined the effects and potential mechanisms of natural small molecule compounds derived from traditional Chinese medicine on the activation of NLRP3 inflammasomes at their initiation, assembly, and activation stages. The objection is to furnish theoretical support and practical guidance for the effective clinical application of these natural small molecule inhibitors.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/metabolism* ; Inflammation/drug therapy* ; Anti-Inflammatory Agents/therapeutic use* ; Humans ; Animals ; Disease Models, Animal ; Biological Products/therapeutic use* ; Drug Discovery ; Medicine, Chinese Traditional/methods*

Traditional Chinese medicine(TCM), with diverse structural types of active components and remarkable clinical efficacy, holds a significant position in the pharmacological research. As the key substances, active components of TCM are of great importance in revealing the material basis of TCM efficacy and mechanism of action. However, the conventional approaches of discovering active components in TCM are characterized by tedious procedures, lengthy cycles, and unclear mechanisms, which struggle to meet the current demands for drug development. In recent years, major breakthroughs have been made in target discovery technologies, and new drug targets are constantly being discovered, which has facilitated the development of target-driven approaches. The target-guided active component discovery strategy provides a new paradigm for discovering active components in TCM. This article systematically summarizes two mainstream target-based technologies-virtual screening and ligand fishing-for TCM active component discovery. By analyzing relevant application cases, this article evaluates the strengths and limitations of each technology. The review aims to provide frameworks for expediting bioactive component discovery in complex systems like TCM, so as to accelerate the development of innovative drugs based on the active components of TCM and promote the modernization and internationalization of TCM.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Medicine, Chinese Traditional ; Drug Discovery/methods* ; Animals

The study of active substances in traditional Chinese medicine(TCM) is the foundation of TCM pharmacology, TCM quality control, and new drug development, and it is also one of the most popular directions in TCM modernization research in recent years. Due to their diverse chemical compositions and complex component-effect relationships, Chinese medicines often require the comprehensive use of multidisciplinary technologies such as chemistry, biology, and information science to reveal their active substances and targets. In this paper, we review the innovative breakthroughs made in the past 30 years in the discovery strategies and technological means for the research of active substances in traditional Chinese medicine from the perspective of technological changes, and focus on the new direction of the research of active substances in traditional Chinese medicine under the paradigm shift of scientific research brought about by artificial intelligence, with the aim of promoting research in related fields to move in the direction of more accurate, efficient, and intelligent, and providing innovative ideas for the research of active substances in traditional Chinese medicine under the new situation.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Humans ; Artificial Intelligence ; Drug Discovery/methods* ; Animals

Maternal health during pregnancy has a direct impact on the risk and severity of neurodevelopmental disorders (NDDs) in the offspring, especially in the case of drug exposure. However, little progress has been made to assess the risk of drug exposure during pregnancy due to ethical constraints and drug use factors. We collected and manually curated sub-pathways and pathways (sub-/pathways) and drug information to propose an analytical framework for predicting drug candidates. This framework linked sub-/pathway activity and drug response scores derived from gene transcription data and was applied to human fetal brain development and six NDDs. Further, specific and pleiotropic sub-/pathways/drugs were identified using entropy, and sex bias was analyzed in conjunction with logistic regression and random forest models. We identified 19 disorder-associated and 256 regionally pleiotropic and specific candidate drugs that targeted risk sub-/pathways in NDDs, showing temporal or spatial changes across fetal development. Moreover, 5443 differential drug-sub-/pathways exhibited sex-biased differences after filling in the gender labels. A user-friendly NDDP visualization website ( https://ndd-lab.shinyapps.io/NDDP ) was developed to allow researchers and clinicians to access and retrieve data easily. Our framework overcame data gaps and identified numerous pleiotropic and specific candidates across six disorders and fetal developmental trajectories. This could significantly contribute to drug discovery during pregnancy and can be applied to a wide range of traits.
Humans ; Female ; Pregnancy ; Neurodevelopmental Disorders/metabolism* ; Male ; Prenatal Exposure Delayed Effects ; Fetal Development/drug effects* ; Drug Discovery/methods* ; Brain/metabolism*

Peptide-based therapies have attracted considerable interest in the treatment of cancer, diabetes, bacterial infections, and neurodegenerative diseases due to their promising therapeutic properties and enhanced safety profiles. This review provides a comprehensive overview of the major trends in peptide drug discovery and development, emphasizing preclinical strategies aimed at improving peptide stability, specificity, and pharmacokinetic properties. It assesses the current applications and challenges of peptide-based drugs in these diseases, illustrating the pharmaceutical areas where peptide-based drugs demonstrate significant potential. Furthermore, this review analyzes the obstacles that must be overcome in the future, aiming to provide valuable insights and references for the continued advancement of peptide-based drugs.
Humans ; Peptides/pharmacology* ; Animals ; Neoplasms/drug therapy* ; Drug Discovery ; Neurodegenerative Diseases/drug therapy* ; Diabetes Mellitus/drug therapy*

Natural products (NPs) have long held a significant position in various fields such as medicine, food, agriculture, and materials. The chemical space covered by NPs is extensive but often underexplored. Therefore, high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems. Mass spectrometry (MS) has emerged as a powerful platform for the annotation and discovery of NPs. MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information. Additionally, the released annotation methodologies, based on a variety of informatics tools, continuously improve the ability to annotate the structure and properties of compounds. This review examines the current mainstream databases and annotation methodologies, focusing on their advantages and limitations. Prospects for future technological advancements are then discussed in terms of novel applications and research objectives. Through a systematic overview, this review aims to provide valuable insights and a reference for MS-based NPs annotation, thereby promoting the discovery of novel natural entities.
Biological Products/chemistry* ; Mass Spectrometry/methods* ; Databases, Factual ; Drug Discovery/methods* ; Humans

The bioactivity-guided isolation of potentially active natural products has been widely utilized in pharmaceutical discovery. In this study, by screening fungal extracts against coxsackievirus B3 (CVB3), three new aspochalasins, templichalasins A‒C (1‒3), along with six known aspochalasins (4‒9) were isolated from an active extract derived from the endophytic fungus Aspergillus templicola LHWf045. Compound 1 features a unique 5/6/5/7/5 pentacyclic ring system, while compounds 2 and 3 possess unusual 5/6/6/7 tetracyclic skeletons. Their structures were characterized through extensive spectroscopic analyses, electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Additionally, we demonstrated that compound 4 can be readily converted into compounds 1‒3 under mild acidic conditions and proposed a plausible mechanism for this conversion. Bioactivity evaluation of compounds 1‒9 against CVB3 revealed the inhibitory effects of all compounds against the virus. Notably, compound 9 exhibited superior antiviral activity, surpassing the commercial drug ribavirin in selectivity index (SI) value.
Antiviral Agents/isolation & purification* ; Aspergillus/chemistry* ; Molecular Structure ; Enterovirus B, Human/drug effects* ; Endophytes/chemistry* ; Cytochalasins/isolation & purification* ; Drug Discovery ; Humans

Microorganisms, abundant in nature, are prolific producers of a diverse array of natural products (NPs) that are fundamental in the development of innovative therapeutics. Despite their significant potential, the field faces considerable challenges, including the continuous emergence of potential health threats, as well as novel pathogen strains and viruses. The advent and implementation of advanced technologies, such as culture strategies, genomics mining, and artificial intelligence (AI), are facilitating a paradigm shift in pharmaceutical research, introducing innovative methodologies and perspectives. The development and maturation of these technologies have enhanced the exploration of microbial-derived NPs, thereby advancing pharmaceutical research and development. This review synthesizes recent developments in this context, emphasizing their applications in pharmaceutical discovery and development. Through systematic analysis and synthesis, it provides objective insights into the promising prospects and future direction of this essential field.
Biological Products/chemistry* ; Drug Discovery ; Humans ; Artificial Intelligence ; Bacteria/metabolism*

Recent years have witnessed significant advances in the development of novel techniques and methodologies for identifying active ingredients in traditional Chinese medicine (TCM), substantially advancing research and development efforts. Spectrum-effect correlation analysis, affinity ultrafiltration, high-content screening (HCS) imaging, and cell membrane chromatography (CMC) have emerged as essential tools, effectively linking TCM chemical constituents to their biological effects, thereby enabling efficient active ingredient screening. Additionally, molecular interaction analysis provides deeper insights into TCM-biomolecule interaction mechanisms, enhancing understanding of its therapeutic potential. Computer-aided techniques facilitate TCM active ingredient identification, optimizing the screening process for efficiency and cost-effectiveness. Molecular probe technology, as an emerging methodology, enables precise and rapid screening for novel therapeutic drug discovery. Ongoing technological advancement in this field indicates promising future developments, potentially leading to more effective and targeted TCM-based therapies.
Drugs, Chinese Herbal/chemistry* ; Medicine, Chinese Traditional/methods* ; Humans ; Drug Discovery/methods* ; Animals

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*