Acta Pharmaceutica Sinica B

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Microneedle-based delivery for autoimmune diseases: Emerging opportunities and unresolved questions

Jiamin Wu

Acta Pharmaceutica Sinica B. 2026, 16(4): 2593-2594.
Astrocytes in predictive processing of emotion: Kir4.1 as precision modulator of social interaction

Acta Pharmaceutica Sinica B. 2026, 16(4): 2595-2597.
Author correction to “The crucial function of IDO1 in pulmonary fibrosis: from the perspective of mitochondrial fusion in lung fibroblasts and targeted molecular inhibition” [Acta Pharmaceutica Sinica B 15 (2025) 3125-3148]

Acta Pharmaceutica Sinica B. 2026, 16(4): 2598-2599.
AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip

Hongyun Yin, Zheyu Li, Zinuo Shen, Shiying Wang, Na Du, Shibo Cheng, Jie Zhou, Yutao Li, Yanwei Jia, Ying Li

Acta Pharmaceutica Sinica B. 2026, 16(3): 1175-1200.

Drug discovery remains a protracted and capital-intensive process, primarily hindered by inefficiencies in drug screening. Microfluidic technology provides a promising approach for in vitro drug screening, enabling physiologically relevant, high-throughput, and cost-effective analysis by mimicking key aspects of cellular microenvironments. The synergistic integration of artificial intelligence (AI) with microfluidics constitutes a pivotal advancement in biomedical analysis. The convergence of the two facilitates automated data analysis, complex pattern recognition, and intelligent experimental control, thereby accelerating drug screening and contributing to enhanced precision. This review systematically presents the latest advancements in AI-assisted microfluidic drug screening, organized by increasing biological complexity: from single-cell analysis (1D), multicellular arrays (2D), and spheroids (3D), to sophisticated Organ-on-a-chip (OoC, 3D+) platforms. We detail how AI algorithms promote screening throughput, sensitivity, and physiological relevance at each scale. Furthermore, we critically discuss the prevailing challenges, including those related to data, model robustness, interpretability, and system integration. Finally, we outline future directions, highlighting the potential of AI-enhanced microfluidics to further advance precision drug discovery and biomedical research. We believe this timely review will offer a useful reference for researchers working in the interdisciplinary field of AI, microfluidics, and pharmacology.
A review of recent advances in generative artificial intelligence models for biomolecular sciences

Jian Jiang, Daixin Li, Guilin Wang, Nicole Hayes, Yazhou Shi, Huahai Qiu, Bengong Zhang, Tianshou Zhou, Guo-Wei Wei

Acta Pharmaceutica Sinica B. 2026, 16(3): 1201-1218.

Generative artificial intelligence (AI) models, a class of AI techniques that learn data distributions to synthesize novel samples, have emerged as impactful tools across scientific disciplines. In recent years, these models have found extensive applications in fields such as natural language processing and biomedical sciences. Despite their growing influence, comprehensive reviews on the application of generative models in biomolecular sciences remain limited. In this review, we provide a systematic overview of recent advances in generative models applied to biomolecular sciences. We discuss several prominent generative architectures, including variational autoencoders, generative adversarial networks, and diffusion models, highlighting their applications in molecular design and bioinformatics. Additionally, we examine how these models contribute to critical challenges such as molecular property prediction and molecular generation. Finally, we discuss key challenges that remain in this field, including model interpretability, scalability, and the need for high-quality molecular datasets. We highlight emerging research directions that aim to overcome these limitations and propose strategies for improving the reliability and applicability of generative models in biomolecular problems. Through this review, our objective is to provide researchers with a comprehensive understanding of the current landscape of generative modeling in biomolecular sciences and to inspire further advancements in this interdisciplinary area.
PhenoModel: A multimodal phenotypic drug design foundation model for discovering novel potential inhibitors of multiple cancer cells

Shihang Wang, Qilei Han, Weichen Qin, Lin Wang, Junhong Yuan, Fengyu Cai, Yiqun Zhao, Pengxuan Ren, Yunze Zhang, Yilin Tang, Ruifeng Li, Zongquan Li, Wenchao Zhang, Shenghua Gao, Fang Bai

Acta Pharmaceutica Sinica B. 2026, 16(3): 1219-1232.

Phenotypic drug discovery (PDD) focuses on the observable traits or phenotype of cells or organisms in response to drug treatment, rather than relying primarily on specific molecular targets. Drugs discovered through this approach may have better therapeutic relevance, as they are tested in conditions that closely mimic human disease. In this study, we present PhenoModel, a multimodal molecular foundation model developed using our unique dual-space contrastive learning framework. This model effectively connects molecular structures with phenotypic information. PhenoModel is applicable to a range of downstream drug discovery tasks, including molecular property prediction and active molecule screening based on targets, phenotypes, and ligands. Our results demonstrate that PhenoModel outperforms baseline methods in these areas. Building from this model, PhenoScreen is developed to successfully identify several phenotypically bioactive compounds against osteosarcoma and rhabdomyosarcoma cell lines. These findings highlight the versatility of PhenoModel and its potential to accelerate drug discovery by uncovering novel therapeutic pathways and expanding the diversity of viable drug candidates.
TeroACT: A terpenoid bioactivity landscape and discovery platform

Acta Pharmaceutica Sinica B. 2026, 16(3): 1233-1249.

Terpenoids exhibit diverse biological activities and thus have a wide range of pharmacological applications. In modern drug discovery, data-driven deep models play a crucial role in facilitating efficient feature representation and knowledge inference. To explore the uncharted bioactivity space of terpenoids, the construction of a multi-dimensional relational terpenoid database is essential for mapping terpenoid-bioactivity profiles. In this study, we first constructed a large-scale biological knowledge graph by integrating various data types, including terpenoid compounds, protein targets, cellular targets, genes, diseases, and their interrelationships. Subsequently, we developed a network-based disease prediction model, as well as optimized multiple compound-protein interaction prediction tools to extend the framework for activity research. These resources have been deployed on a user-friendly web platform (TeroACT) accessible at: http://terokit.qmclab.com/teroact/. Using in silico models within the TeroACT platform, we screened multiple terpenoid molecules for anti-melanoma activity. In vitro and in vivo animal models further validated the anti-migration and anti-proliferative effects of mollugin and columbianadin in melanoma. Additionally, integrated computational screening and experimental approaches identified numerous terpenoids with anti-inflammatory properties. In this sense, TeroACT fills the gap in terpenoid bioactivity study by providing a comprehensive data resource and AI-driven drug discovery tools.
Liver-pancreas communication in disease and drug development

Yan Jia, Qingtian Zhu, Qiong Pan, Guotao Lu, Jia Xiao

Acta Pharmaceutica Sinica B. 2026, 16(3): 1250-1271.

The liver and pancreas are metabolically intertwined organs whose bidirectional communication is critical for maintaining systemic homeostasis. Dysregulation of this inter-organ crosstalk is a central driver in the pathology of a growing list of prevalent diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD), liver cancer, acute and chronic pancreatitis, and various forms of diabetes. Given the substantial global health burden of these conditions and the lack of effective, Food and Drug Administration (FDA)-approved pharmacological interventions for those diseases, understanding the intricate mechanisms is an urgent and timely endeavor. This review provides a comprehensive synthesis of recent advancements in deciphering the molecular basis of liver-pancreas communication. We explore the multifaceted signaling networks involved, including the roles of liver-derived hepatokines, pancreas-derived hormones, extracellular vesicles, and metabolic exchanges. This axis is further integrated within broader systemic networks involving the gut, neuronal system, adipose tissue, and skeletal muscle. While clinical trials targeting this communication show promise (e.g., FGF21- and bile acid-related drugs), significant challenges remain, particularly the lack of FDA-approved pharmacological treatments for alcohol-associated liver disease (ALD), and acute/chronic pancreatitis. Future research must elucidate specific signaling pathways, identify novel pancreas-derived factors, and develop innovative therapeutic strategies. In particular, small molecule drug discovery based on polypharmacology, for these complex metabolic and organ-specific diseases.
Targeting immunosuppressive network in glioblastoma: Emerging strategies to overcome immunodeficiency and enhance therapeutic efficacy

Songlei Zhou, Xiaokun Zhang, Jiayi Lin, Fenfen Ma, Hongzhuan Chen, Jun Chen, Xin Luan

Acta Pharmaceutica Sinica B. 2026, 16(3): 1272-1291.

Glioblastoma (GBM), the most aggressive primary brain tumor, remains a formidable therapeutic challenge, with a median survival under 15 months. Despite the current standard of care-comprising maximal safe surgical resection, radiotherapy, and temozolomide chemotherapy-patient outcomes have seen minimal improvement over the past two decades. A key barrier to effective treatment is GBM's robust and multifaceted immunosuppressive network, which critically undermines antitumor immunity. While much of the research has focused on the local immunosuppressive tumor microenvironment, systemic immunosuppression represents an equally important yet often underappreciated obstacle, significantly impairing host immune competence. Effective immunotherapy relies on an intact and functional immune system capable of mounting durable T cell-mediated responses. However, GBM induces profound systemic immune dysfunction, manifested by severe lymphopenia and depletion of effector immune cells, which further limits immune-mediated tumor control. Therefore, a comprehensive understanding of both systemic and local immunosuppressive mechanisms is essential for the rational design of effective immunotherapeutic strategies. In this review, we examine the unique physiological features of the brain, dissect the immunosuppressive landscape of GBM at both local and systemic levels, and highlight recent insights into the underlying mechanisms. We also discuss current immunotherapeutic modalities, and emerging drug delivery strategies aimed at overcoming immunosuppression to improve therapeutic efficacy.
Targeting RNA binding proteins with small-molecule inhibitors: Advances, challenges, and therapeutic opportunities

Acta Pharmaceutica Sinica B. 2026, 16(3): 1292-1335.

RNA-binding proteins (RBPs) constitute central regulators of post-transcriptional gene expression and have been increasingly recognized as critical contributors to the pathogenesis of cancer, neurodegenerative disorders, and autoimmune diseases. However, in contrast to well-established drug targets such as kinases and G protein-coupled receptors, RBPs remain largely underexploited owing to their intrinsic structural heterogeneity, dynamic RNA interactions, and paucity of canonical ligand-binding pockets. In this review, we synthesize current knowledge on the roles of RBPs in disease, outline recent advances in the design of small-molecule modulators, and highlight innovative applications of high-throughput screening and chemical biology approaches for target identification and validation. We further discuss emerging concepts and challenges in translating RBP modulators into therapeutics, providing a forward-looking perspective on how these efforts may reshape small-molecule drug discovery in this evolving field.

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