Multi-target drug discovery has become an essential strategy for treating complex diseases involving multiple molecular pathways. Traditional single-target approaches often fall short in addressing the multifactorial nature of conditions such as cancer and neurodegenerative disorders. With the rise in large-scale biological data and algorithmic advances, machine learning (ML) has emerged as a powerful tool to accelerate and optimize multi-target drug development. This review presents a comprehensive overview of ML techniques, including advanced deep learning (DL) approaches like attention-based models, and highlights their application in multi-target prediction, from traditional supervised learning to modern graph-based and multi-task learning frameworks. We highlight real-world applications in oncology, central nervous system disorders, and drug repurposing, showcasing the translational potential of ML in systems pharmacology. Major challenges are discussed, such as data sparsity, lack of interpretability, limited generalizability, and integration into experimental workflows. We also address ethical and regulatory considerations surrounding model transparency, fairness, and reproducibility. Looking forward, we explore promising directions such as generative modeling, federated learning, and patient-specific therapy design. Together, these advances point toward a future of precision polypharmacology driven by biologically informed and interpretable ML models. This review aims to provide researchers and practitioners with a roadmap for leveraging ML in the development of safer and more effective multi-target therapeutics.

In recent times, a number of studies have provided evidence that biotoxins present great potential as antitumor agents, such as snake venom, bee venom, some bacteria toxins and plant toxins, and thus could be used as chemotherapeutic agents against tumors. The biodiversity of venoms and toxins make them a unique source from which novel anticancer agent may be developed. Biotoxins, also known as natural toxins, include toxic substances produced by plants, animals and microorganisms. Here, we systematically list representative biological toxins that have antitumor properties, involving animal toxins, plant toxins, mycotoxins as well as bacterial toxins. In this review, we summarize the current knowledge involving biotoxins and the active compounds that have anti-cancer activity to induce cytotoxic, antitumor, immunomodulatory, and apoptotic effects in different tumor cells in vivo or in vitro. We also show insights into the molecular and functional evolution of biotoxins.

Gastrointestinal (GI) cancers impose a substantial global health burden, highlighting the necessity for deeper understanding of their intricate pathogenesis and treatment strategies. This review explores the interplay between intratumoral microbiota, tumor metabolism, and major types of GI cancers (including esophageal, gastric, liver, pancreatic, and colorectal cancers), summarizing recent studies and elucidating their clinical implications and future directions. Recent research revealed altered microbial signatures within GI tumors, impacting tumor progression, immune responses, and treatment outcomes. Dysbiosis-induced alterations in tumor metabolism, including glycolysis, fatty acid metabolism, and amino acid metabolism, play critical roles in cancer progression and therapeutic resistance. The integration of molecular mechanisms and potential biomarkers into this understanding further enhances the prognostic significance of intratumoral microbiota composition and therapeutic opportunities targeting microbiota-mediated tumor metabolism. Despite advancements, challenges remain in understanding the dynamic interactions within the tumor microenvironment (TME). Future research directions, including advanced omics technologies and prospective clinical studies, offer promising avenues for precision oncology and personalized treatment interventions in GI cancer. Overall, integrating microbiota-based approaches and molecular biomarkers into GI cancer management holds promise for improving patient outcomes and survival.

Efficient drug discovery is essential to mitigate the high attrition rates and capital-intensive nature of traditional pharmaceutical research. Deep learning (DL) has catalyzed a paradigm shift, offering unprecedented computational capabilities to accelerate this process. This review systematically summarizes advancements in DL methodologies for drug discovery. First, the critical data foundations, Molecular Representation Learning, and core architectures underpinning artificial intelligence (AI)-driven therapeutics are elucidated, with a particular emphasis on the evolution of generative AI frameworks (such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models) for chemical space exploration. Subsequently, diverse DL applications across the discovery pipeline are investigated, encompassing target identification, virtual screening, de novo molecular design, retrosynthetic analysis, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, and drug repurposing. Furthermore, persistent challenges, particularly data scarcity, model opacity, experimental validation bottlenecks, and escalating computational resource requirements, are critically evaluated. To bridge the gap between in silico predictions, chemical synthesis, and biological reality, emerging solutions such as federated learning, causality-aware Explainable AI (XAI), closed-loop autonomous laboratories, and efficiency-oriented algorithmic strategies are explored. Ultimately, this article provides strategic insights into leveraging DL, underscoring its transformative potential in driving next-generation medicinal and therapeutic innovation.