Mini Reviews in Medicinal Chemistry - Online First

Marine organisms produce a diverse array of secondary metabolites with significant pharmacological potential, particularly in the development of anti-cancer, anti-microbial, anti-fungal, and anti-viral therapies. Despite challenges in isolation and cultivation, marine-derived compounds, such as Didemnin B, Psammaplin A, and Dolastatin, have shown promise in cancer treatment, while other metabolites exhibit potent activity against drug-resistant bacteria, fungi, and viruses. These compounds have excellent potential for treating various infections, for example, MRSA (eicosapentaenoic acid and fridamycin), Candida albicans (aurantoside K), and HIV-1 and HIV-2 (sulfoquinovosyl diacylglycerol). These unique compounds offer new avenues in drug discovery, addressing current limitations in traditional therapies. This review provides an overview of the pharmacological potential of marine organisms, focusing on their applications in overcoming drug resistance and developing novel treatments for cancer, infections, and viral diseases. Sustainable approaches for harvesting these compounds are essential for future research.

The drug discovery process is highly intricate and complex. Driven by unprecedented advances in AI technology, the application of artificial intelligence in drug discovery (AIDD) is showing significant growth, and AIDD has the potential to fundamentally change and revolutionize traditional drug development models in the foreseeable future. We selected 7061 literature studies on artificial intelligence used in drug discovery from the Web of Science Core Collection database from 2000 to 2024, and adopted bibliometric tools, such as Citespace, VOSviewer, and RStudio, to select nodes for knowledge graph generation and visualization analysis by using country, institution, author, and keywords. The results showed that from 2017 to 2024, the literature on the use of artificial intelligence for drug discovery exploded, with the United States having the largest number of papers, and China's number of papers growing rapidly and surpassing the United States in the last two years. Molecular docking, virtual screening, algorithm optimization, interpretable AIDD, protein language modeling, drug targets, and protein interaction prediction were found to be the research hotspots in this field in recent years. AI has been widely used in the field of drug research and development. Based on the quantitative analysis of the literature on the use of artificial intelligence in drug discovery, this paper has revealed the current research status in this field, clarified the current research hotspots and future research trends, and provided certain references for researchers to plan future research directions.

The method of discovering new drugs is costly, time-consuming, laborious, and associated with a high failure rate. Various techniques have been applied in modern drug discovery to resolve these issues and discover novel pharmacologically active agents. Natural products are one of the sources of drugs that have long been used to treat various illnesses. Kojic acid (KA) is a naturally produced bioactive chemical with a 3-hydroxy-4-pyranone skeleton made by numerous aerobic microbes, such as Aspergillus and Penicillium. KA is a potent tyrosinase inhibitor used in cosmetics to lighten skin by reducing hyperpigmentation. In this review, beyond its cosmetic applications, it exhibits versatile biological activities, including anticancer, antibacterial, antifungal, antioxidant, antiviral, anti-inflammatory, anticonvulsant, anti-Alzheimer's disease, antidiabetic, and metal-chelating properties. KA and its analogs have been reported as promising radioprotective agents capable of mitigating the harmful effects of ionizing radiation. By integrating KA with pharmacologically active scaffolds, researchers have developed potent hybrids, such as amino-chloroquinoline-KA derivatives, which demonstrate vigorous β-hematin inhibitory activity and significant efficacy against both delicate and resilient strains of P. falciparum to chloroquine. The approach taken to prepare this review article involved collecting, assessing, and synthesizing relevant literature from different databases. This review systematically explores the comprehensive therapeutic potential of KA and its derivatives, including Mannich base, thiazoles, and 1,2,3-triazoles, for various activities, with Michael Adducts and dinuclear ruthenium complexes which exhibits promising antitumor activity. Combining current knowledge will provide a comprehensive foundation for the rational design and development of clinically relevant agents based on KA pharmacophores.

In the ongoing battle between the immune system and cancer, a unique subset of T cells has quietly emerged as a key player. Tissue-Resident Memory T (TRM) cells, strategically positioned within tissues, are redefining our understanding of localized immune defense and tumor control. This review aims to bridge this knowledge gap by synthesizing current concepts of T cell immune surveillance with foundational aspects of TRM cell biology. We explored clinical evidence supporting the prognostic and therapeutic relevance of TRM cells in various cancer contexts, including their emerging roles in enhancing responses to immunotherapy. Furthermore, we discussed innovative strategies that exploit TRM-phenotype cells for patient stratification, disease staging, and therapeutic development. Key challenges such as the absence of standardized T cell nomenclature and the limited understanding of how TRM markers relate to tumor biology are critically examined. By integrating basic science and clinical observations, this review provides a comprehensive overview of the field and highlights promising avenues for future research to harness TRM cells in precision oncology.

Since its discovery in 1979, the tumor suppressor p53 has been widely studied and expressed in various organisms, including yeast. Yeast has proven to be a very informative model and an effective system for studying the roles and functions of this protein and gene. This review is a compilation of our team's studies involving p53 expression in yeast. These researches investigated certain aspects, essentially the apoptotic function of p53. Our main contribution to the study and understanding of the p53 gene in the yeast context is the confirmation of a negative effect of p53 on cell growth in both Saccharomyces cerevisiae and Pichia pastoris strains, which ultimately led to apoptotic cell death. This involves a high dose of p53 and the NLS signal, which enables p53 to target both the mitochondria and the nucleus. Prior to that, obtaining the whole protein required a cDNA without its UTR. Thus, a yeast model was developed, allowing verification of p53 activity. Cancer mutants and their revertants could thereby be assessed. This has evolved into a real antioxidant/anti-apoptotic molecular screening mechanism. Two primary applications were achieved: testing the co-expression with the thioredoxin 2 gene (TRX2) and assessing the impact of Nigella sativa seed extracts. Furthermore, the high yield of yeast P53 production allowed its use in serological cancer diagnosis.

Fragment-based drug discovery (FBDD) has emerged as a transformative strategy in modern medicinal chemistry, offering a rational and efficient alternative to traditional high-throughput screening (HTS). By utilizing small, low-molecular-weight fragments with moderate binding affinity, FBDD enables systematic optimization into potent lead compounds with improved physicochemical properties. Its modular and ligand-centric nature has proven particularly advantageous in accelerating early-stage drug discovery. The COVID-19 pandemic highlighted the adaptability of FBDD, as fragment screening and computational modeling rapidly identified inhibitors of the SARS-CoV-2 main protease (M^pro). Integration with artificial intelligence (AI) and cloud-based platforms further enhanced the speed and global accessibility of fragment campaigns, setting a precedent for collaborative, open-science initiatives. Beyond infectious diseases, FBDD has demonstrated significant promise in oncology, antibacterial therapy, and neurodegenerative disorders, reflecting its versatility across diverse therapeutic landscapes. Recent technological advances have expanded the scope of FBDD. High-resolution cryo-electron microscopy and AI-driven structural prediction now enable the exploration of previously inaccessible or dynamic protein targets. Emerging modalities, such as PROTACs and RNA-targeted therapeutics, also intersect with fragment-based strategies, opening avenues for addressing so-called “undruggable” proteins. Despite persistent challenges, including the need for sensitive biophysical methods and sophisticated infrastructure, the approach continues to evolve. Looking ahead, the convergence of FBDD with machine learning, open-access fragment libraries, and global research collaboration positions it as a scalable, adaptive platform for drug discovery. As future health threats demand rapid innovation, FBDD is poised to remain a cornerstone of both academic and industrial research pipelines.

Breast cancer (BC) remains a leading cause of mortality worldwide, with treatment complicated by tumor heterogeneity, drug resistance, and therapy-related toxicities. Despite advances in chemotherapy, immunotherapy, and surgery, these challenges continue to limit therapeutic outcomes. Among emerging strategies, prodrugs have shown promise. These pharmacologically inactive compounds are designed to undergo enzymatic or chemical conversion in the body, releasing the active drug selectively in target tissues, thereby improving drug delivery, enhancing efficacy, and reducing systemic toxicity. Prodrug strategies targeting specific molecular features of tumors, such as the tumor microenvironment (TME), offer potential solutions to issues like poor drug solubility and bioavailability. Combining prodrugs with other therapeutic modalities, including immunotherapy and precision medicine, is actively being investigated to overcome drug resistance and enhance treatment response. Nevertheless, challenges remain, including the complexity of designing prodrugs that can be efficiently activated within the TME, as well as scalability and manufacturing costs. Future research leveraging nanotechnology, personalized medicine, and artificial intelligence-driven drug discovery is expected to drive innovations in prodrug-based therapies. Integrating these approaches may enable more effective and individualized treatments for BC, particularly in cases refractory to conventional therapies. This review highlights the current status, challenges, and future directions of prodrug development in breast cancer therapy, underscoring their potential to transform the treatment landscape.