With advances of drug design and preparation technology, the development of long-acting drugs has become an important research focus in precision medicine and chronic disease management. These drugs are designed to improve the patients' compliance and quality of life by achieving prolonged maintenance of an effective drug concentration in the body with a reduced dosing frequency. Small molecule drugs, monoclonal antibodies and nucleic acid drugs all have their own difficulties in achieving long actions, which can be especially challenging for the latter two because of their structural complexity. This review provides an overview of the strategies for designing long-acting small molecule drugs, monoclonal antibodies, and nucleic acid drugs.
Humans ; Drug Design ; Antibodies, Monoclonal/chemistry* ; Nucleic Acids ; Precision Medicine ; Delayed-Action Preparations

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*

Zebrafish larvae are useful for identifying chemicals against lateral line (LL) hair cell (HC) damage and this type of chemical screen mainly focuses on searching for protectors against cell death. To expand the candidate pool of HC protectors, a self-built acoustic escape response (AER)-detecting system was developed to apply both low-frequency near-field sound transmission and AER image acquisition/processing modules. The device quickly confirmed the changed LL HC functions caused by most known ototoxins, protectors, and neural transmission modifiers, or knockdown of LL HC-expressing genes. With ten devices wired in tandem, five 'hit' chemicals were identified from 124 cyclin-dependent kinase inhibitors to partially restore cisplatin-damaged AER in less than a day. AS2863619, ribociclib, and SU9516 among the hits, protected the HCs in the mouse cochlea. Therefore, using free-swimming larval zebrafish, the self-made AER-detecting device can efficiently identify compounds that are protective against HC damage, including cell death and loss-of-function.
Animals ; Zebrafish ; Hair Cells, Auditory/physiology* ; Lateral Line System/cytology* ; Escape Reaction/physiology* ; Larva ; Mice ; Cisplatin/toxicity* ; Drug Evaluation, Preclinical/methods*

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

Saponins associated with Panax notoginseng (P. notoginseng) demonstrate significant therapeutic efficacy across multiple diseases. However, certain high-yield saponins face limited clinical applications due to their reduced pharmacological efficacy. This study synthesized and evaluated 36 saponin-1,2,3-triazole derivatives of ginsenosides Rg1/Rb1 and notoginsenoside R1 for anti-adipogenesis activity in vitro. The research revealed that the ginsenosides Rg1-1,2,3-triazole derivative a17 demonstrates superior adipogenesis inhibitory effects. Structure-activity relationships (SARs) analysis indicates that incorporating an amidyl-substituted 1,2,3-triazole into the saponin side chain via Click reaction enhances anti-adipogenesis activity. Additionally, several other derivatives exhibit general adipogenesis inhibition. Compound a17 demonstrated enhanced potency compared to the parent ginsenoside Rg1. Mechanistic investigations revealed that a17 exhibits dose-dependent inhibition of adipogenesis in vitro, accompanied by decreased expression of preadipocytes. Peroxisome proliferator-activated receptor γ (PPARγ), fatty acid synthase (FAS), and fatty acid binding protein 4 (FABP4) adipogenesis regulators. These findings establish the ginsenoside Rg1-1,2,3-triazole derivative a17 as a promising adipocyte differentiation inhibitor and potential therapeutic agent for obesity and associated metabolic disorders. This research provides a foundation for developing effective therapeutic approaches for various metabolic syndromes.
Adipogenesis/drug effects* ; Triazoles/chemical synthesis* ; Ginsenosides/chemical synthesis* ; Saponins/chemical synthesis* ; Animals ; Mice ; Structure-Activity Relationship ; PPAR gamma/genetics* ; 3T3-L1 Cells ; Adipocytes/metabolism* ; Panax notoginseng/chemistry* ; Drug Design ; Molecular Structure ; Humans ; Cell Differentiation/drug effects* ; Fatty Acid-Binding Proteins/genetics*

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*