The unique characteristics of the deep space environment, microgravity, cosmic radiation, and extreme temperature fluctuations, are emerging as major driving forces for pharmaceutical innovation. These factors provide new avenues for optimizing drug formulations, improving crystal structure quality, and accelerating the discovery of therapeutic targets. Advances in deep space research not only help overcome critical bottlenecks in terrestrial drug development but also promote progress in structure-based drug design and deepen understanding of cellular stress-response mechanisms. Current progress in space-based pharmaceutical research primarily includes the study of disease mechanisms under microgravity, protein crystallization in microgravity, and drug development utilizing deep space radiation and resources. However, the operational complexity, high costs, and limited data reproducibility of space experiments remain key challenges hindering widespread application. Looking ahead, with the integration of automation, artificial intelligence analysis, and on-orbit manufacturing, deep space drug development is expected to achieve greater scalability and precision, opening a new frontier in biopharmaceutical science.
Drug Design ; Drug Development/methods* ; Humans ; Weightlessness ; Space Flight ; Artificial Intelligence ; Extraterrestrial Environment

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines' bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.
Biological Products/pharmacology* ; Artificial Intelligence ; Humans ; Drug Discovery/methods* ; Machine Learning ; Deep Learning

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Artificial intelligence (AI) researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes. Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable, thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making. This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations. The research methodology begins with the compilation of small molecule databases, followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms, capturing patterns and salient features across extensive chemical spaces. The study then examines various drug discovery downstream tasks, including drug-target interaction (DTI) prediction, drug-target affinity (DTA) prediction, drug property (DP) prediction, and drug generation, all based on learned representations. The analysis concludes by highlighting challenges and opportunities associated with machine learning (ML) methods for molecular representation and improving downstream task performance. Additionally, the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine (TCM) medicinal substances and facilitating TCM target discovery.
Artificial Intelligence ; Drug Discovery/methods* ; Humans ; Machine Learning ; Medicine, Chinese Traditional ; Small Molecule Libraries/chemistry*

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*