Acta Pharmaceutica Sinica B

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.

Metal-polyphenol network-based multifunctional materials for tumor therapy: Intrinsic properties, advances, and applications

Acta Pharmaceutica Sinica B. 2026, 16(3): 1421-1448.

Metal-polyphenol networks (MPNs), a novel class of nano-biomaterials, have recently emerged as promising candidates for tumor diagnosis and therapy due to their unique chemical tunability, excellent biocompatibility, and synergistic multifunctionality. Notably, MPNs can be synthesized via one-step or multi-step approaches, allowing precise control over their morphology, size, and drug-loading capacity. The versatility of MPNs is further demonstrated by their ability to integrate multiple therapeutic modalities, including chemotherapy, photothermal therapy, photodynamic therapy, and chemical dynamic therapy. Furthermore, through surface modification with targeted molecules, MPNs enable tumor-specific targeting while facilitating real-time therapeutic monitoring via multimodal imaging. Additionally, MPNs exhibit excellent biocompatibility and superior biodegradability, making them highly suitable for biomedical applications. This review systematically explores MPN synthesis strategies and physicochemical properties. It then comprehensively analyzes MPN-based biomaterials and their tumor therapeutic mechanisms. Furthermore, we evaluate the challenges in MPN clinical translation and propose future perspectives for precise tumor treatment using MPN-based platforms. Ultimately, this review highlights the transformative potential of MPNs in advancing tumor theranostics and lays the foundation for their future clinical applications.

A review of recent advances in generative artificial intelligence models for biomolecular sciences

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.

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.

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.

Obesity–cancer axis crosstalk: Molecular insights and therapeutic approaches

Ahmed Rakib, Md Abdullah Al Mamun, Mousumi Mandal, Priti Sinha, Udai P. Singh

Acta Pharmaceutica Sinica B. 2025, 15(6): 2930-2944.

Now recognized as a global health crisis, obesity has been linked to an increased risk of many types of cancer, including those of the breast, colon, rectum, uterus, gallbladder, and ovary. Obesity and cancer share several characteristics at the cellular, molecular, and epigenetic levels. Obesity is characterized by chronic inflammation of the adipose tissue (AT), resulting in genotoxic stress that further induces metabolic complications and contributes to the initiation and progression of cancer. The excessive accumulation of AT provides adipokines and lipids to engage tumor cells with stromal and immune cells to infiltrate carcinomas and secrete a plethora of cytokines, chemokines, and growth factors within the tumor microenvironment (TME) that contribute to carcinogenesis. Obesity also alters the metabolic reprogramming of immune cells, including macrophages, neutrophils, and T cells, thereby providing a suitable environment for the growth and progression of cancer. Obesity-associated metabolic dysregulation also perturbs the gut microbiome, which produces metabolites that can further increase the risk of cancer progression. This review will discuss links between obesity and cancer progression, including several crucial pathways that bridge the crosstalk between obesity-associated changes in AT inflammation, immune cells, adipokines, chemokines, and tumor cells to support cancer progression. We will also discuss our insights into the mechanisms by which obesity-driven factors influence metabolic reprogramming and touch base on how obesity mediates microbiome dysbiosis to alter metabolite and affect cancer progression. Altogether, this review highlights the crossroads of the obesity–cancer axis, describes its salient features, and presents possible therapeutic approaches for obesity-related cancers.

Functional aptamer evolution-enabled elucidation of a melanoma migration-related bioactive epitope

Hong Xuan, Siqi Bian, Qinguo Liu, Jun Li, Shaojin Li, Sharpkate Shaker, Haiyan Cao, Tongxuan Wei, Panzhu Yao, Yifan Chen, Xiyang Liu, Ruidong Xue, Youbo Zhang, Liqin Zhang

Acta Pharmaceutica Sinica B. 2025, 15(6): 3196-3209.

Metastasis is the leading cause of death from cutaneous melanoma. Identifying metastasis-related targets and developing corresponding therapeutic strategies are major areas of focus. While functional genomics strategies provide powerful tools for target discovery, investigations at the protein level can directly decode the bioactive epitopes on functional proteins. Aptamers present a promising avenue as they can explore membrane proteomes and have the potential to interfere with cell function. Herein, we developed a target and epitope discovery platform, termed functional aptamer evolution-enabled target identification (FAETI), by integrating affinity aptamer acquisition with phenotype screening and target protein identification. Utilizing the aptamer XH3C, which was screened for its migration-inhibitory function, we identified the Chondroitin Sulfate Proteoglycan 4 (CSPG4), as a potential target involved in melanoma migration. Further evidence demonstrated that XH3C induces cytoskeletal rearrangement by blocking the interaction between the bioactive epitope of CSPG4 and integrin α4. Taken together, our study demonstrates the robustness of aptamer-based molecular tools for target and epitope discovery. Additionally, XH3C is an affinity and functional molecule that selectively binds to a unique epitope on CSPG4, enabling the development of innovative therapeutic strategies.

Editor Profiles: Guest Editors of Special Column on Nanomedicines for Tumor Microenvironment Modulation

Acta Pharmaceutica Sinica B. 2025, 15(6): 2815-2815.

PRIME, a temperature-guided language model revolutionizes protein engineering

Yuanxi Yu, Qianhui Wang, Yike Zou

Acta Pharmaceutica Sinica B. 2025, 15(6): 3343-3345.

Alginate lyase immobilized Chlamydomonas algae microrobots: minimally invasive therapy for biofilm penetration and eradication

Xiaoting Zhang, Huaan Li, Lu Liu, Yanzhen Song, Lishan Zhang, Jiajun Miao, Jiamiao Jiang, Hao Tian, Chang Liu, Fei Peng, Yingfeng Tu

Acta Pharmaceutica Sinica B. 2025, 15(6): 3259-3272.

Bacterial biofilms can make traditional antibiotics impenetrable and even promote the development of antibiotic-resistant strains. Therefore, non-antibiotic strategies to effectively penetrate and eradicate the formed biofilms are urgently needed. Here, we demonstrate the development of self-propelled biohybrid microrobots that can enhance the degradation and penetration effects for Pseudomonas aeruginosa biofilms in minimally invasive strategy. The biohybrid microrobots (CR@Alg) are constructed by surface modification of Chlamydomonas reinhardtii (CR) microalgae with alginate lyase (Alg) via biological orthogonal reaction. By degrading the biofilm components, the number of CR@Alg microrobots with fast-moving capability penetrating the biofilm increases by around 2.4-fold compared to that of microalgae. Massive reactive oxygen species are subsequently generated under laser irradiation due to the presence of chlorophyll, inherent photosensitizers of microalgae, thus triggering photodynamic therapy (PDT) to combat bacteria. Our algae-based microrobots with superior biocompatibility eliminate biofilm-infections efficiently and tend to suppress the inflammatory response in vivo, showing huge promise for the active treatment of biofilm-associated infections.