Artificial intelligence for natural product drug discovery

DTF-diffusion: A 3D equivariant diffusion generation model based on ligand-target information fusion

The goal of drug discovery based on deep learning is to generate drug molecules that bind to a given target protein. Recently, the use of three-dimensional molecular structures has shown superior performance over other two-dimensional structural models. However, most of the current depth generation models are based on ligands, and in the process of molecular generation, the models only learn the independent information of ligands or targets, without considering the complex interaction information of them. In addition, chemical knowledge was not considered in the process of molecular formation, which led to generation unreasonable drug molecular structure. In order to solve above problems, this paper proposes DTF-diffusion, a 3D equivariant diffusion generation model based on ligand-target information fusion. Firstly based on the diffusion model, DTF-diffusion uses multimodal feature fusion module proposed in this paper to fuse the three-dimensional position feature information of ligand molecules and targets, and extract advanced hidden features from ligand atom information and target sequence information. Secondly, this paper designs a chemical rule discrimination module, and learns the real ligand molecular structure and the characteristic information of the generated ligand molecules through the discriminator, and then capture the chemical rules in the drug molecular structure, which effectively improve the rationality of the ligand structure generated by the model. This paper evaluates the generation performance of DTF-diffusion and other baseline methods from multiple perspectives based on the CrossDocket2020 dataset. In the quantitative estimate of drug-likeness index, DTF-diffusion is 3.85 % higher than the existing optimal model, the drug validity index increased by 4.34 %. More generation experiments have proved that DTF-diffusion has excellent performance, indicating that it has a good application prospect in the field of drug molecule generation.

Bioinformatics assisted construction of the link between biosynthetic gene clusters and secondary metabolites in fungi

Fungal secondary metabolites are considered as important resources for drug discovery. Despite various methods being employed to facilitate the discovery of new fungal secondary metabolites, the trend of identifying novel secondary metabolites from fungi is inevitably slowing down. Under laboratory conditions, the majority of biosynthetic gene clusters, which store information for secondary metabolites, remain inactive. Therefore, establishing the link between biosynthetic gene clusters and secondary metabolites would contribute to understanding the genetic logic underlying secondary metabolite biosynthesis and alleviating the current challenges in discovering novel natural products. Bioinformatics methods have garnered significant attention due to their powerful capabilities in data mining and analysis, playing a crucial role in various aspects. Thus, we have summarized successful cases since 2016 in which bioinformatics methods were utilized to establish the link between fungal biosynthetic gene clusters and secondary metabolites, focusing on their biosynthetic gene clusters and associated secondary metabolites, with the goal of aiding the field of natural product discovery.

Epigenetic targets and their inhibitors in the treatment of idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a deadly lung disease characterized by fibroblast proliferation, excessive extracellular matrix buildup, inflammation, and tissue damage, resulting in respiratory failure and death. Recent studies suggest that impaired interactions among epithelial, mesenchymal, immune, and endothelial cells play a key role in IPF development. Advances in bioinformatics have also linked epigenetics, which bridges gene expression and environmental factors, to IPF. Despite the incomplete understanding of the pathogenic mechanisms underlying IPF, recent preclinical studies have identified several novel epigenetic therapeutic targets, including DNMT, EZH2, G9a/GLP, PRMT1/7, KDM6B, HDAC, CBP/p300, BRD4, METTL3, FTO, and ALKBH5, along with potential small-molecule inhibitors relevant for its treatment. This review explores the pathogenesis of IPF, emphasizing epigenetic therapeutic targets and potential small molecule drugs. It also analyzes the structure-activity relationships of these epigenetic drugs and summarizes their biological activities. The objective is to advance the development of innovative epigenetic therapies for IPF.

Natural products that target p53 for cancer therapy

Wild-type p53 acts as a tumor suppressor, but p53 is frequently mutated and inactivated in tumor cells, promoting cancer progression, invasion, and metastasis. Thus, compounds that reactivate p53 may be leveraged for cancer treatment, and the development of drugs targeting p53 reactivation is actively progressing. Notably, natural products exhibit diverse structures and biological activities and are used as therapeutic agents for various diseases worldwide. This review discusses the natural products that inhibit p53 degradation through p53-Mdm2 interaction, promote p53 reactivation by inducing conformational changes, and exhibit p53-dependent growth inhibition.

Water in drug design: pitfalls and good practices

INTRODUCTION

Structure-based drug design relies on optimizing drug-target interactions and blocking harmful pathophysiological events at the atomic level. Such events of the human body are modulated by water acting either as a medium or an individual partner in molecular interactions. A precise understanding of the modulatory mechanisms of water is essential for a successful drug design.

AREAS COVERED

The present review discusses different topographical and networking situations that result in radically different roles of water, a root of various pitfalls of drug design. The review surveys good practices for tackling the problems of determining water structure at atomic resolution. Techniques for quantifying the effects of bulk, networking, and individual water molecules on the stability of drug-target complexes are also discussed. The article is based on a literature search using the PubMed, Web of Science, and Google Scholar databases.

EXPERT OPINION

With advances in rapid computational algorithms and a better understanding of the physicochemical machinery of complex formation, theoretical approaches have resulted in elegant and cost-effective tools that fill the knowledge gaps left by the limited experimental methods. Overcoming the technical pitfalls of drug design, water transforms from a frustrating challenge into a handy tool for fine-tuning drug-target interactions.

A review on the expanding biotechnological frontier of Pedobacter

The genus Pedobacter consists of Gram-negative bacteria with a broad geographic distribution, isolated from diverse habitats, including water, soil, plants, wood, rocks and animals. However, characterization efforts have been limited to a small number of species. Likewise, in the context of natural products (NP), only a small fraction of Pedobacter -derived NPs have been characterized so far. In contrast, in silico analysis of the increasing number of available genomes in the databases, suggests a wealth of yet to be discovered compounds. Notable biotechnological applications described so far include the production of heparinases and chondroitinases for therapeutic purposes, phytases and galactosidases as aquaculture feed supplements, alginate lyases for biofuel production, and secondary metabolites such as pedopeptins and isopedopeptins with antimicrobial properties. Further research integrating synthetic biology approaches, holds great promise for unlocking the hidden potential of members of this genus, thus expanding its industrial applications.

Unlocking marine treasures: isolation and mining strategies of natural products from sponge-associated bacteria

Covering: 2019 to early 2025Marine sponges form unique ecosystems through symbiosis with diverse microbial communities, producing natural products including bioactive compounds. This review comprehensively addresses the key steps in the discovery of natural products from sponge-associated microorganisms, encompassing microbial isolation and cultivation, compound identification, and characterisation. Various cultivation methods, such as floating filter cultivation, microcapsule-based cultivation, and in situ systems, are examined to highlight their applications and strategies for overcoming limitations of conventional approaches. Additionally, the integration of genome-based methodologies and compound screening is explored to enhance the discovery of novel bioactive substances and establish a sustainable platform for natural product research. This review provides insights into the latest trends in sponge-associated microbial research and offers practical perspectives for expanding the utilization of marine biological resources.

Artificial intelligence for RNA-ligand interaction prediction: advances and prospects

Accurate prediction of RNA-ligand interactions is vital for understanding biological processes and advancing RNA-targeted drug discovery. Given their complexity, Artificial Intelligence (AI) is revolutionizing the study of RNA-ligand interactions, offering insights into the complex dynamics and therapeutic potential of RNA. In this review, we highlight advances in AI-driven RNA-ligand binding site identification, structure modeling, binding mode and binding affinity prediction, and virtual screening (VS). We also discuss challenges, such as data set scarcity and modeling the flexibility of RNA, are discussed. Future directions emphasize integrating cutting-edge AI techniques with physics-based models and expanding experimental data sets to enhance RNA-ligand interaction predictions.

Cancer chemoprevention: signaling pathways and strategic approaches

Although cancer chemopreventive agents have been confirmed to effectively protect high-risk populations from cancer invasion or recurrence, only over ten drugs have been approved by the U.S. Food and Drug Administration. Therefore, screening potent cancer chemopreventive agents is crucial to reduce the constantly increasing incidence and mortality rate of cancer. Considering the lengthy prevention process, an ideal chemopreventive agent should be nontoxic, inexpensive, and oral. Natural compounds have become a natural treasure reservoir for cancer chemoprevention because of their superior ease of availability, cost-effectiveness, and safety. The benefits of natural compounds as chemopreventive agents in cancer prevention have been confirmed in various studies. In light of this, the present review is intended to fully delineate the entire scope of cancer chemoprevention, and primarily focuses on various aspects of cancer chemoprevention based on natural compounds, specifically focusing on the mechanism of action of natural compounds in cancer prevention, and discussing in detail how they exert cancer prevention effects by affecting classical signaling pathways, immune checkpoints, and gut microbiome. We also introduce novel cancer chemoprevention strategies and summarize the role of natural compounds in improving chemotherapy regimens. Furthermore, we describe strategies for discovering anticancer compounds with low abundance and high activity, revealing the broad prospects of natural compounds in drug discovery for cancer chemoprevention. Moreover, we associate cancer chemoprevention with precision medicine, and discuss the challenges encountered in cancer chemoprevention. Finally, we emphasize the transformative potential of natural compounds in advancing the field of cancer chemoprevention and their ability to introduce more effective and less toxic preventive options for oncology.

Multiscale graph equivariant diffusion model for 3D molecule design

Three-dimensional molecular generation is critical in drug design. However, current methods often rely on point clouds or oversimplified interaction models, limiting their ability to accurately represent molecular structures. To address these challenges, this paper proposes the multiscale graph equivariant diffusion model for 3D molecule design (MD3MD). MD3MD partitions molecular conformations into multiscale graphs, assigning different weights to capture atomic interactions across scales. This framework guides the diffusion process, enabling high-quality 3D molecular generation. Experimental results demonstrate that MD3MD excels in both unconditional and conditional generation tasks, producing diverse, stable, and innovative molecules that meet specified conditions. Visualization highlights MD3MD's ability to learn domain-specific patterns and generate molecules distinct from existing datasets while maintaining distributional consistency. By effectively exploring chemical space, MD3MD surpasses previous methods in generating innovative and chemically diverse molecules, offering a notable advancement in the field of molecular design.

Empowering natural product science with AI: leveraging multimodal data and knowledge graphs

Artificial intelligence (AI) is accelerating how we conduct science, from folding proteins with AlphaFold and summarizing literature findings with large language models, to annotating genomes and prioritizing newly generated molecules for screening using specialized software. However, the application of AI to emulate human cognition in natural product research and its subsequent impact has so far been limited. One reason for this limited impact is that available natural product data is multimodal, unbalanced, unstandardized, and scattered across many data repositories. This makes natural product data challenging to use with existing deep learning architectures that consume fairly standardized, often non-relational, data. It also prevents models from learning overarching patterns in natural product science. In this Viewpoint, we address this challenge and support ongoing initiatives aimed at democratizing natural product data by collating our collective knowledge into a knowledge graph. By doing so, we believe there will be an opportunity to use such a knowledge graph to develop AI models that can truly mimic natural product scientists' decision-making.

Meta-analysis and review of in silico methods in drug discovery - part 1: technological evolution and trends from big data to chemical space

This review offers an overview of advanced in silico methods crucial for drug discovery, emphasizing their integration with data science, and investigates the effectiveness of data science, machine learning, and artificial intelligence via a thorough meta-analysis of existing technologies. This meta-analysis aims to rank these technologies based on their applications and accessibility of knowledge. Initially, a search strategy yielded 900 papers, which were then refined into two subsets: the top 300 most-cited papers since 2000 and papers selected for systematic review based on high impact. From these, 97 articles were identified for discussion, categorized by their influence on society. The focus remains on the qualitative impact of these disciplines rather than solely on metrics like new drug approvals. Ultimately, the review underscores the role of big data in enhancing our comprehension of drug candidate trajectories from development to commercialization, utilizing information stored in publicly available databases to chemical space. Graphical extrapolation of some keywords (Drug Discovery; Big Data; Database; Metadata) discussed in this article and their evolution (in terms of absolute items that are available) by time.

Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme

Machine learning has recently been introduced into the arsenal of tools that are available to computational chemists. In the past few years, we have seen an increase in the applicability of these tools on a plethora of applications, including the automated exploration of a large fraction of the chemical space, the reduction of repetitive computational tasks, the detection of outliers on large databases, and the acceleration of molecular simulations. An attractive application of machine learning in molecular electronic structure theory is the "recycling" of molecular wave functions for faster and more accurate completion of complex quantum chemical calculations. Along these lines, we have developed hybrid quantum chemical/machine learning workflows that utilize information from low-level wave functions for the accurate prediction of higher-level wave functions. The data-driven coupled-cluster (DDCC) family of methods is discussed in this article together with the importance of the inclusion of physical properties in such hybrid workflows. After a short introduction to the philosophy and the capabilities of DDCC, we present our recent progress in extending its applicability to larger and more complex molecular structures and data sets. A significant advantage offered by DDCC is its transferability, with respect to different molecular systems and different excitation levels. As we show here, predicted wave functions at the coupled-cluster singles and doubles level of theory can be used for the accurate prediction of the perturbative triples of the CCSD(T) scheme. We conclude with some personal considerations with respect to future directions related to the development of the next generation of such hybrid quantum chemical/machine learning models.

Throw out an oligopeptide to catch a protein: Deep learning and natural language processing-screened tripeptide PSP promotes Osteolectin-mediated vascularized bone regeneration

Angiogenesis is imperative for bone regeneration, yet the conventional cytokine therapies have been constrained by prohibitive costs and safety apprehensions. It is urgent to develop a safer and more efficient therapeutic alternative. Herein, utilizing the methodologies of Deep Learning (DL) and Natural Language Processing (NLP), we proposed a paradigm algorithm that amalgamates Word2vec with a TF-IDF variant, TF-IIDF, to deftly discern potential pro-angiogenic peptides from intrinsically disordered regions (IDRs) of 262 related proteins, where are fertile grounds for developing safer and highly promising bioactive peptides. After the evaluation of the candidate oligopeptides, one tripeptide, PSP, emerged as particularly notable for its exceptional ability to stimulate the vascularization of endothelial cells (ECs), enhance vascular-osteo communication, and then boost the osteogenic differentiation of bone marrow stem cells (BMSCs), evidenced in mouse critical-sized cranial model. Moreover, we found that PSP serves as a 'priming' agent, activating the body's innate ability to produce Osteolectin (Oln) - prompting ECs to release small extracellular vesicles (sEVs) enriched with Oln to facilitate bone formation. In summary, our study established a precise and efficient composite model of DL and NLP to screen bioactive peptides, opening an avenue for the development of various peptide-based therapeutic strategies applicable to a broader range of diseases.

Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation

This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations. By meticulously adjusting the cosine similarity thresholds from 0.2 to 0.5, our tailored models have consistently achieved a remarkable accuracy rate of 97 %, illustrating their effectiveness in predicting suitable medical treatments and interventions. These results not only highlight the revolutionary capabilities of NLP and ML in harnessing data-driven insights for healthcare but also lay a robust groundwork for future advancements in personalized medicine and bespoke treatment pathways. Comprehensive analysis demonstrates the scalability and adaptability of these technologies in real-world healthcare settings, potentially leading to substantial improvements in patient outcomes and operational efficiencies within the healthcare system.

MIFS: An adaptive multipath information fused self-supervised framework for drug discovery

The production of expressive molecular representations with scarce labeled data is challenging for AI-driven drug discovery. Mainstream studies often follow a pipeline that pre-trains a specific molecular encoder and then fine-tunes it. However, the significant challenges of these methods are (1) neglecting the propagation of diverse information within molecules and (2) the absence of knowledge and chemical constraints in the pre-training strategy. In this study, we propose an adaptive multipath information fused self-supervised framework (MIFS) that explores molecular representations from large-scale unlabeled data to aid drug discovery. In MIFS, we innovatively design a dedicated molecular graph encoder called Mol-EN, which implements three pathways of information propagation: atom-to-atom, chemical bond-to-atom, and group-to-atom, to comprehensively perceive and capture abundant semantic information. Furthermore, a novel adaptive pre-training strategy based on molecular scaffolds is devised to pre-train Mol-EN on 11 million unlabeled molecules. It optimizes Mol-EN by constructing a topological contrastive loss to provide additional chemical insights into molecular structures. Subsequently, the pre-trained Mol-EN is fine-tuned on 14 widespread drug discovery benchmark datasets, including molecular properties prediction, drug-target interactions, and drug-drug interactions. Notably, to further enhance chemical knowledge, we introduce an elemental knowledge graph (ElementKG) in the fine-tuning phase. Extensive experiments show that MIFS achieves competitive performance while providing plausible explanations for predictions from a chemical perspective.

Machine Learning-Enabled Drug-Induced Toxicity Prediction

Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research. However, the optimal AI model for different types of toxicity usually varies, making it essential to conduct comparative analyses of AI methods across toxicity domains. The diverse data sources also pose challenges for researchers focusing on specific toxicity studies. In this review, 10 categories of drug-induced toxicity is examined, summarizing the characteristics and applicable ML models, including both predictive and interpretable algorithms, striking a balance between breadth and depth. Key databases and tools used in toxicity prediction are also highlighted, including toxicology, chemical, multi-omics, and benchmark databases, organized by their focus and function to clarify their roles in drug-induced toxicity prediction. Finally, strategies to turn challenges into opportunities are analyzed and discussed. This review may provide researchers with a valuable reference for understanding and utilizing the available resources to bridge prediction and mechanistic insights, and further advance the application of ML in drugs-induced toxicity prediction.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Large-scale biosynthetic analysis of human microbiomes reveals diverse protective ribosomal peptides

The human microbiome produces diverse metabolites that influence host health, yet the chemical landscape of ribosomally synthesized and post-translationally modified peptides (RiPPs)-a versatile class of bioactive compounds-remains underexplored. Here, we conduct a large-scale biosynthetic analysis of 306,481 microbial genomes from human-associated microbiomes, uncovering a broad array of yet-to-be-discovered RiPPs. These RiPPs are distributed across various body sites but show a specific enrichment in the gut and oral microbiome. Big data omics analysis reveals that numerous RiPP families are inversely related to various diseases, suggesting their potential protective effects on health. For a proof of principle study, we apply the synthetic-bioinformatic natural product (syn-BNP) approach to RiPPs and chemically synthesize nine autoinducing peptides (AIPs) for in vitro and ex vivo assay. Our findings reveal that five AIPs effectively inhibit the biofilm formation of disease-associated pathogens. Furthermore, when ex vivo testing gut microbiota from mice with inflammatory bowel disease, we observe that two AIPs can regulate the microbial community and reduce harmful species. These findings highlight the vast potential of human microbial RiPPs in regulating microbial communities and maintaining human health, emphasizing their potential for therapeutic development.

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

Drug repositioning as a promising approach for the eradication of emerging and re-emerging viral agents

The global impact of emerging and re-emerging viral agents during epidemics and pandemics leads to serious health and economic burdens. Among the major emerging or re-emerging viruses include SARS-CoV-2, Ebola virus (EBOV), Monkeypox virus (Mpox), Hepatitis viruses, Zika virus, Avian flu, Influenza virus, Chikungunya virus (CHIKV), Dengue fever virus (DENV), West Nile virus, Rhabdovirus, Sandfly fever virus, Crimean-Congo hemorrhagic fever (CCHF) virus, and Rift Valley fever virus (RVFV). A comprehensive literature search was performed to identify existing studies, clinical trials, and reviews that discuss drug repositioning strategies for the treatment of emerging and re-emerging viral infections using databases, such as PubMed, Scholar Google, Scopus, and Web of Science. By utilizing drug repositioning, pharmaceutical companies can take advantage of a cost-effective, accelerated, and effective strategy, which in turn leads to the discovery of innovative treatment options for patients. In light of antiviral drug resistance and the high costs of developing novel antivirals, drug repositioning holds great promise for more rapid substitution of approved drugs. Main repositioned drugs have included chloroquine, ivermectin, dexamethasone, Baricitinib, tocilizumab, Mab114 (Ebanga™), ZMapp (pharming), Artesunate, imiquimod, saquinavir, capmatinib, naldemedine, Trametinib, statins, celecoxib, naproxen, metformin, ruxolitinib, nitazoxanide, gemcitabine, Dorzolamide, Midodrine, Diltiazem, zinc acetate, suramin, 5-fluorouracil, quinine, minocycline, trifluoperazine, paracetamol, berbamine, Nifedipine, and chlorpromazine. This succinct review will delve into the topic of repositioned drugs that have been utilized to combat emerging and re-emerging viral pathogens.

Weight Differences-Based Multi-level Signal Profiling for Homogeneous and Ultrasensitive Intelligent Bioassays

Current high-sensitivity immunoassay protocols often involve complex signal generation designs or rely on sophisticated signal-loading and readout devices, making it challenging to strike a balance between sensitivity and ease of use. In this study, we propose a homogeneous-based intelligent analysis strategy called Mata, which uses weight analysis to quantify basic immune signals through signal subunits. We perform nanomagnetic labeling of target capture events on micrometer-scale polystyrene subunits, enabling magnetically regulated kinetic signal expression. Signal subunits are classified through the multi-level signal classifier in synergy with the developed signal weight analysis and deep learning recognition models. Subsequently, the basic immune signals are quantified to achieve ultra-high sensitivity. Mata achieves a detection of 0.61 pg/mL in 20 min for interleukin-6 detection, demonstrating sensitivity comparable to conventional digital immunoassays and over 22-fold that of chemiluminescence immunoassay and reducing detection time by more than 70%. The entire process relies on a homogeneous reaction and can be performed using standard bright-field optical imaging. This intelligent analysis strategy balances high sensitivity and convenient operation and has few hardware requirements, presenting a promising high-sensitivity analysis solution with wide accessibility.

From Traditional Efficacy to Drug Design: A Review of Astragali Radix

Astragali Radix (AR), a traditional Chinese herbal medicine, is derived from the dried roots of Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao (A. membranaceus var. mongholicus, AMM) or Astragalus membranaceus (Fisch.) Bge (A. membranaceus, AM). According to traditional Chinese medicine (TCM) theory, AR is believed to tonify qi, elevate yang, consolidate the body's surface to reduce sweating, promote diuresis and reduce swelling, generate body fluids, and nourish the blood. It has been widely used to treat general weakness and chronic illnesses and to improve overall vitality. Extensive research has identified various medicinal properties of AR, including anti-tumor, antioxidant, cardiovascular-protective, immunomodulatory, anti-inflammatory, anti-diabetic, and neuroprotective effects. With advancements in technology, methods such as computer-aided drug design (CADD) and artificial intelligence (AI) are increasingly being applied to the development of TCM. This review summarizes the progress of research on AR over the past decades, providing a comprehensive overview of its traditional efficacy, botanical characteristics, drug design and distribution, chemical constituents, and phytochemistry. This review aims to enhance researchers' understanding of AR and its pharmaceutical potential, thereby facilitating further development and utilization.

Natural Products Targeting Tau Protein Phosphorylation: A Promising Therapeutic Avenue for Alzheimer's Disease

Alzheimer's disease is a progressive neurodegenerative disorder characterized by tau protein hyperphosphorylation and neurofibrillary tangle formation, which are central to its pathogenesis. This review focuses on the therapeutic potential of natural products in targeting tau phosphorylation, a key factor in Alzheimer's disease progression. It comprehensively summarizes current research on various natural compounds, including flavonoids, alkaloids, saponins, polysaccharides, phenols, phenylpropanoids, and terpenoids, highlighting their multitarget mechanisms, such as modulating kinases and phosphatases. The ability of these compounds to mitigate oxidative stress, inflammation, and tau pathology while enhancing cognitive function underscores their value as potential anti-Alzheimer's disease therapeutics. By integrating recent advances in extraction methods, pharmacological studies, and artificial intelligence-driven screening technologies, this review provides a valuable reference for future research and development of natural product-based interventions for Alzheimer's disease.

Deep learning models in classifying primary bone tumors and bone infections based on radiographs

Primary bone tumors (PBTs) present significant diagnostic challenges due to their heterogeneous nature and similarities with bone infections. This study aimed to develop an ensemble deep learning framework that integrates multicenter radiographs and extensive clinical features to accurately differentiate between PBTs and bone infections. We compared the performance of the ensemble model with four imaging models based solely on radiographs utilizing EfficientNet B3, EfficientNet B4, Vision Transformer, and Swin Transformers. The patients were split into external dataset (N = 423) and internal dataset [including training (N = 1044), test (N = 354), and validation set (N = 171)]. The ensemble model outperformed imaging models, achieving areas under the curve (AUCs) of 0.948 and 0.963 on internal and external sets, respectively, with accuracies of 0.881 and 0.895. Its performance surpassed junior and mid-level radiologists and was comparable to senior radiologists (accuracy: 83.6%). These findings underscore the potential of deep learning in enhancing diagnostic precision for PBTs and bone infections (Research Registration Unique Identifying Number (UIN): researchregistry10483 and with details are available at https://www.researchregistry.com/register-now#home/registrationdetails/6693845995ba110026aeb754/ ).

How AI can help us beat AMR

Antimicrobial resistance (AMR) is an urgent public health threat. Advancements in artificial intelligence (AI) and increases in computational power have resulted in the adoption of AI for biological tasks. This review explores the application of AI in bacterial infection diagnostics, AMR surveillance, and antibiotic discovery. We summarize contemporary AI models applied to each of these domains, important considerations when applying AI across diverse tasks, and current limitations in the field.

Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping

Pharmacophores are abstractions of essential chemical interaction patterns, holding an irreplaceable position in drug discovery. Despite the availability of many pharmacophore tools, the adoption of deep learning for pharmacophore-guided drug discovery remains relatively rare. We herein propose a knowledge-guided diffusion framework for 'on-the-fly' 3D ligand-pharmacophore mapping, named DiffPhore. It leverages ligand-pharmacophore matching knowledge to guide ligand conformation generation, meanwhile utilizing calibrated sampling to mitigate the exposure bias of the iterative conformation search process. By training on two self-established datasets of 3D ligand-pharmacophore pairs, DiffPhore achieves state-of-the-art performance in predicting ligand binding conformations, surpassing traditional pharmacophore tools and several advanced docking methods. It also manifests superior virtual screening power for lead discovery and target fishing. Using DiffPhore, we successfully identify structurally distinct inhibitors for human glutaminyl cyclases, and their binding modes are further validated through co-crystallographic analysis. We believe this work will advance the AI-enabled pharmacophore-guided drug discovery techniques.

Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery

Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contain all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active natural compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that Meta-DEP can mined active compound from traditional Chinese medicine with integrating disease transcriptomic data. Overall, the model developed in this study provides new impetus for driving the natural compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP .

Harnessing machine learning for rational drug design

A crucial part of biomedical research is drug discovery, which aims to find and create innovative medical treatments for a range of illnesses. However, there are intrinsic obstacles to the traditional approach of discovering novel medications, including high prices, lengthy turnaround times, and poor clinical trial success rates. In recent times, the use of designing algorithms for machine learning has become a groundbreaking way to improve and optimise many stages of medication development. An outline of the quickly developing area of machine learning algorithms for drug discovery is given in this review, emphasising how revolutionary treatment development might be. To effectively get a novel medication into the market, modern medicinal development often involves many interconnected stages. The use of computational tools has become more and more crucial in reducing the time and cost involved in the investigation and creation of new medications. Our latest efforts to combine molecular modelling as well as machine learning to create the computational resources for designing modulators utilising a sensible design influenced by the pocket process that targets protein-protein interactions via AlphaSpace are reviewed in this Perspective. A significant shift in pharmaceutical research has occurred with the introduction of AI in drug discovery, which combines cutting-edge computer techniques with conventional scientific investigation to address enduring problems. By highlighting significant advancements and methodologies, this review paper elucidates the many applications of AI throughout several stages of drug discovery.

Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Computational advances in biosynthetic gene cluster discovery and prediction

Biosynthetic gene clusters (BGCs) are groups of clustered genes found in bacteria, fungi, and some plants and animals that are crucial for synthesizing secondary metabolites. In recent years, genome mining of BGCs has emerged as a prominent research focus, particularly in natural product discovery and drug development. Compared to traditional experimental methods, applying computational techniques has significantly enhanced the efficiency of BGC identification and annotation, thereby facilitating the discovery of novel metabolites. The advent of artificial intelligence, particularly machine learning models and more advanced deep learning algorithms, has significantly enhanced both the speed and precision of BGC mining. This review offers a comprehensive introduction to currently developed BGC databases and prediction tools, highlighting the potential of machine learning technologies in BGC mining. Additionally, it summarizes the challenges computational methods face in this area and discusses future research directions.

Hallmarks of artificial intelligence contributions to precision oncology

The integration of artificial intelligence (AI) into oncology promises to revolutionize cancer care. In this Review, we discuss ten AI hallmarks in precision oncology, organized into three groups: (1) cancer prevention and diagnosis, encompassing cancer screening, detection and profiling; (2) optimizing current treatments, including patient outcome prediction, treatment planning and monitoring, clinical trial design and matching, and developing response biomarkers; and (3) advancing new treatments by identifying treatment combinations, discovering cancer vulnerabilities and designing drugs. We also survey AI applications in interventional clinical trials and address key challenges to broader clinical adoption of AI: data quality and quantity, model accuracy, clinical relevance and patient benefit, proposing actionable solutions for each.

Practical Applications of Artificial Intelligence Diagnostic Systems in Fundus Retinal Disease Screening

Purpose

This study aims to evaluate the performance of a deep learning-based artificial intelligence (AI) diagnostic system in the analysis of retinal diseases, assessing its consistency with expert diagnoses and its overall utility in screening applications.

Methods

A total of 3076 patients attending our hospital underwent comprehensive ophthalmic examinations. Initial assessments were performed using the AI, the Comprehensive AI Retinal Expert (CARE) system, followed by thorough manual reviews to establish final diagnoses. A comparative analysis was conducted between the AI-generated results and the evaluations by senior ophthalmologists to assess the diagnostic reliability and feasibility of the AI system in the context of ophthalmic screening.

Results

: The AI diagnostic system demonstrated a sensitivity of 94.12% and specificity of 98.60% for diabetic retinopathy (DR); 89.50% sensitivity and 98.33% specificity for age-related macular degeneration (AMD); 91.55% sensitivity and 97.40% specificity for suspected glaucoma; 90.77% sensitivity and 99.10% specificity for pathological myopia; 81.58% sensitivity and 99.49% specificity for retinal vein occlusion (RVO); 88.64% sensitivity and 99.18% specificity for retinal detachment; 83.33% sensitivity and 99.80% specificity for macular hole; 82.26% sensitivity and 99.23% specificity for epiretinal membrane; 94.55% sensitivity and 97.82% specificity for hypertensive retinopathy; 83.33% sensitivity and 99.74% specificity for myelinated fibers; and 75.00% sensitivity and 99.95% specificity for retinitis pigmentosa. Additionally, the system exhibited notable performance in screening for other prevalent conditions, including DR, suspected glaucoma, suspected glaucoma, pathological myopia, and hypertensive retinopathy.

Conclusions

: The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices. Its implementation is particularly beneficial for grassroots and community healthcare settings, facilitating initial diagnostic efforts and enhancing the efficacy of tiered ophthalmic care, with important implications for broader clinical adoption.

Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics

The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of researchers to explore novel natural products and wastes significant amounts of time and resources. This study introduces a novel machine learning framework that can effectively characterize the bioactivity of natural products by leveraging liquid chromatography tandem mass spectrometry and untargeted metabolomics analysis. This accelerates natural product drug discovery by addressing the challenge of dereplicating previously discovered bioactive compounds. Utilizing the SIRIUS 5 metabolomics software suite and in-silico-generated fragmentation spectra, we have trained a ML model capable of predicting a compound's drug class. This approach enables the rapid identification of bioactive scaffolds from LC-MS/MS data, even without reference experimental spectra. The model was trained on a diverse set of molecular fingerprints generated by SIRIUS 5 to effectively classify compounds based on their core pharmacophores. Our model robustly classified 21 diverse bioactive drug classes, achieving accuracies greater than 93% on experimental spectra. This study underscores the potential of ML combined with MFPs to dereplicate bioactive natural products based on pharmacophore, streamlining the discovery process and expediting improved methods of isolating novel antibacterial and antifungal agents.

Targeted genome mining with GATOR-GC maps the evolutionary landscape of biosynthetic diversity

Gene clusters, groups of physically adjacent genes that work collectively, are pivotal to bacterial fitness and valuable in biotechnology and medicine. While various genome mining tools can identify and characterize gene clusters, they often overlook their evolutionary diversity, a crucial factor in revealing novel cluster functions and applications. To address this gap, we developed GATOR-GC, a targeted genome mining tool that enables comprehensive and flexible exploration of gene clusters in a single execution. We show that GATOR-GC identified a diversity of over 4 million gene clusters similar to experimentally validated biosynthetic gene clusters (BGCs) that other tools fail to detect. To highlight the utility of GATOR-GC, we identified previously uncharacterized co-occurring conserved genes potentially involved in mycosporine-like amino acid biosynthesis and mapped the taxonomic and evolutionary patterns of genomic islands that modify DNA with 7-deazapurines. Additionally, with its proximity-weighted similarity scoring, GATOR-GC successfully differentiated BGCs of the FK-family of metabolites (e.g., rapamycin, FK506/520) according to their chemistries. We anticipate GATOR-GC will be a valuable tool to assess gene cluster diversity for targeted, exploratory, and flexible genome mining. GATOR-GC is available at https://github.com/chevrettelab/gator-gc.

Peripheral Evolution of Tanshinone IIA and Cryptotanshinone for Discovery of a Potent and Specific NLRP3 Inflammasome Inhibitor

Natural products (NPs) continue to serve as an invaluable source in drug discovery, and peripheral evolution of NPs is a highly efficient evolution strategy. Herein, we describe a unified "methyl to amide" peripheral evolution of Tanshinone IIA and Cryptotanshinone for discovery of NLRP3 inflammasome inhibitors. There were 54 compounds designed and prepared, while the chemoinformatic analysis revealed that these evolved NP analogues occupy a unique chemical space. Biological evaluation identified 5m as an NLRP3 inflammasome inhibitor, and 5m could directly bind to the NACHT domain of the NLRP3 protein and block the interaction of NLRP3 and ASC, thus suppressing ASC oligomerization and NLRP3 inflammasome assembly. Molecular dynamic stimulations revealed that the amide moiety played a vital role in the binding mode. Moreover, 5m exhibited therapeutical efficacy in sepsis and the NASH mouse model. In conclusion, this protocol provides a new vision of NPs' peripheral evolution and a novel NLRP3 inflammasome inhibitor.

Cardiorenal syndrome: clinical diagnosis, molecular mechanisms and therapeutic strategies

As the heart and kidneys are closely connected by the circulatory system, primary dysfunction of either organ usually leads to secondary dysfunction or damage to the other organ. These interactions play a major role in the pathogenesis of a clinical entity named cardiorenal syndrome (CRS). The pathophysiology of CRS is complicated and involves multiple body systems. In early studies, CRS was classified into five subtypes according to the organs associated with the vicious cycle and the acuteness and chronicity of CRS. Increasing evidence shows that CRS is associated with a variety of pathological mechanisms, such as haemodynamics, neurohormonal changes, hypervolemia, hypertension, hyperuraemia and hyperuricaemia. In this review, we summarize the classification and currently available diagnostic biomarkers of CRS. We highlight the recently revealed molecular pathogenesis of CRS, such as oxidative stress and inflammation, hyperactive renin‒angiotensin‒aldosterone system, maladaptive Wnt/β-catenin signalling pathway and profibrotic TGF‒β1/Smad signalling pathway, as well as other pathogeneses, such as dysbiosis of the gut microbiota and dysregulation of noncoding RNAs. Targeting these CRS-associated signalling pathways has new therapeutic potential for treating CRS. In addition, various chemical drugs, natural products, complementary therapies, blockers, and agonists that protect against CRS are summarized. Since the molecular mechanisms of CRS remain to be elucidated, no single intervention has been shown to be effective in treating CRS. Pharmacologic therapies designed to block CRS are urgently needed. This review presents a critical therapeutic avenue for targeting CRS and concurrently illuminates challenges and opportunities for discovering novel treatment strategies for CRS.

Recent advances in genome mining and synthetic biology for discovery and biosynthesis of natural products

Natural products have long served as critical raw materials in chemical and pharmaceutical manufacturing, primarily which can provide superior scaffolds or intermediates for drug discovery and development. Over the last century, natural products have contributed to more than a third of therapeutic drug production. However, traditional methods of producing drugs from natural products have become less efficient and more expensive over the past few decades. The combined utilization of genome mining and synthetic biology based on genome sequencing, bioinformatics tools, big data analytics, genetic engineering, metabolic engineering, and systems biology promises to counter this trend. Here, we reviewed recent (2020-2023) examples of genome mining and synthetic biology used to resolve challenges in the production of natural products, such as less variety, poor efficiency, and low yield. Additionally, the emerging efficient tools, design principles, and building strategies of synthetic biology and its application prospects in NPs synthesis have also been discussed.

Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis

Objective

To systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field.

Methods

A comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain.

Results

A collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold's protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening.

Conclusion

This bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations.

The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Microbial secondary metabolites: advancements to accelerate discovery towards application

Microbial secondary metabolites not only have key roles in microbial processes and relationships but are also valued in various sectors of today's economy, especially in human health and agriculture. The advent of genome sequencing has revealed a previously untapped reservoir of biosynthetic capacity for secondary metabolites indicating that there are new biochemistries, roles and applications of these molecules to be discovered. New predictive tools for biosynthetic gene clusters (BGCs) and their associated pathways have provided insights into this new diversity. Advanced molecular and synthetic biology tools and workflows including cell-based and cell-free expression facilitate the study of previously uncharacterized BGCs, accelerating the discovery of new metabolites and broadening our understanding of biosynthetic enzymology and the regulation of BGCs. These are complemented by new developments in metabolite detection and identification technologies, all of which are important for unlocking new chemistries that are encoded by BGCs. This renaissance of secondary metabolite research and development is catalysing toolbox development to power the bioeconomy.

Mining Silent Biosynthetic Gene Clusters for Natural Products in Filamentous Fungi

Filamentous fungi are of great interest due to their powerful metabolic capabilities and potential to produce abundant various secondary metabolites as natural products (NPs), some of which have been developed into pharmaceuticals. Furthermore, high-throughput genome sequencing has revealed tremendous cryptic NPs underexplored. Based on the development of in silico genome mining, various techniques have been introduced to rationally modify filamentous fungi, awakening the silent biosynthetic gene clusters (BGCs) and visualizing the NPs originally cryptic. This review summarizes the key strategies and research advances in the activation of cryptic BGCs in filamentous fungi, including endogenous non-targeted activation, single-target activation and heterologous expression. It also provides a critical evaluation of these strategies and offers perspectives on the current state of research.

Promising tools for future drug discovery and development in antiarrhythmic therapy

Arrhythmia refers to irregularities in the rate and rhythm of the heart, with symptoms spanning from mild palpitations to life-threatening arrhythmias and sudden cardiac death. The complex molecular nature of arrhythmias complicates the selection of appropriate treatment. Current therapies involve the use of antiarrhythmic drugs (class I-IV) with limited efficacy and dangerous side effects and implantable pacemakers and cardioverter-defibrillators with hardware-related complications and inappropriate shocks. The number of novel antiarrhythmic drugs in the development pipeline has decreased substantially during the last decade and underscores uncertainties regarding future developments in this field. Consequently, arrhythmia treatment poses significant challenges, prompting the need for alternative approaches. Remarkably, innovative drug discovery and development technologies show promise in helping advance antiarrhythmic therapies. In this article, we review unique characteristics and the transformative potential of emerging technologies that offer unprecedented opportunities for transitioning from traditional antiarrhythmics to next-generation therapies. We assess stem cell technology, emphasizing the utility of innovative cell profiling using multiomics, high-throughput screening, and advanced computational modeling in developing treatments tailored precisely to individual genetic and physiological profiles. We offer insights into gene therapy, peptide, and peptibody approaches for drug delivery. We finally discuss potential strengths and weaknesses of such techniques in reducing adverse effects and enhancing overall treatment outcomes, leading to more effective, specific, and safer therapies. Altogether, this comprehensive overview introduces innovative avenues for personalized rhythm therapy, with particular emphasis on drug discovery, aiming to advance the arrhythmia treatment landscape and the prevention of sudden cardiac death. SIGNIFICANCE STATEMENT: Arrhythmias and sudden cardiac death account for 15%-20% of deaths worldwide. However, current antiarrhythmic therapies are ineffective and have dangerous side effects. Here, we review the field of arrhythmia treatment underscoring the slow progress in advancing the cardiac rhythm therapy pipeline and the uncertainties regarding evolution of this field. We provide information on how emerging technological and experimental tools can help accelerate progress and address the limitations of antiarrhythmic drug discovery.

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

Natural Products in the Prevention of Degenerative Bone and Joint Diseases: Mechanisms Based on the Regulation of Ferroptosis

Degenerative bone and joint diseases (DBJDs), characterized by osteoporosis, osteoarthritis, and chronic inflammation of surrounding soft tissues, are systemic conditions primarily affecting the skeletal system. Ferroptosis, a programmed cell death pathway distinct from apoptosis, autophagy, and necroptosis. Accumulating evidence suggests that ferroptosis is intricately linked to the pathogenesis of DBJDs, and targeting its regulation could be beneficial in managing these conditions. Natural products, known for their anti-inflammatory and antioxidant properties, have shown unique advantages in preventing DBJDs, potentially through modulating ferroptosis. This article provides an overview of the latest research on ferroptosis, with a focus on its role in the pathogenesis of DBJDs and the therapeutic potential of natural products targeting this cell death pathway, offering novel insights for the prevention and treatment of DBJDs.

The Theranostic Genome

Theranostic drugs represent an emerging path to deliver on the promise of precision medicine. However, bottlenecks remain in characterizing theranostic targets, identifying theranostic lead compounds, and tailoring theranostic drugs. To overcome these bottlenecks, we present the Theranostic Genome, the part of the human genome whose expression can be utilized to combine therapeutic and diagnostic applications. Using a deep learning-based hybrid human-AI pipeline that cross-references PubMed, the Gene Expression Omnibus, DisGeNET, The Cancer Genome Atlas and the NIH Molecular Imaging and Contrast Agent Database, we bridge individual genes in human cancers with respective theranostic compounds. Cross-referencing the Theranostic Genome with RNAseq data from over 17'000 human tissues identifies theranostic targets and lead compounds for various human cancers, and allows tailoring targeted theranostics to relevant cancer subpopulations. We expect the Theranostic Genome to facilitate the development of new targeted theranostics to better diagnose, understand, treat, and monitor a variety of human cancers.

Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study

In virtual drug screening, consensus docking is a standard in-silico approach consisting of a combined result from optimized docking experiments, a minimum of two results combination. Therefore, consensus docking is subjected to a lower success rate than the best docking method due to its mathematical nature, an unavoidable limitation. This study aims to overcome this drawback via random forest, an ensemble machine learning model. First, in vitro beta-lactamase inhibitory screening was performed using an in-house chemical library. The in vitro results were later used as a validation. Consequently, we optimized docking protocols for AutoDock Vina and DOCK6 programs. With an appropriate scoring function, we found that DOCK6 could identify up to 70% of all active molecules, double the inappropriate. Further consensus analysis reduced the success rate to 50%. Simultaneously, a false positive rate was down to 16%, which was experimentally favorable for a drug search. Finally, we trained two quantitative structure-activity relationship (QSAR) models using logistic regression as a reference model and a random forest as a test model. After combining consensus docking results, random forest-based QSAR outperformed a logistic regression by restoring the success rate to 70% and maintaining a low false positive rate of around 21%. In conclusion, this study demonstrated the benefit of using a random forest (machine learning)-based QSAR model to overcome a standard consensus docking limitation in beta-lactamase inhibitor search as a proof-of-concept.

Scaling up for success: from bioactive natural products to new medicines

Covering 1986 to presentNatural product drug discovery at Novartis has a long and successful history of delivering life saving medicines to millions of patients. In this viewpoint, we are presenting the tools we use and challenges we face as we advance natural products from early research into development and beyond. We are leveraging our collection of 90 000 microbial strains and 20 000 isolated natural products to find new medications in an interdisciplinary approach that requires expertise in microbiology, computational biology, synthetic biology, chemistry, and process development. Technological advances, particularly in genome engineering and data science have transformed our field, accelerating discovery and facilitating sustainable compound supply. Emerging new modalities such as antibody drug conjugates, radioligand therapies and xRNA-based medications offer new opportunities for natural product-derived drugs. By taking advantage of these new modalities and the most recent research technologies, natural products will significantly contribute to the medicines of the future.