Artificial intelligence for natural product drug discovery

Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Preparation of imidazolium ionic liquid functionalized paper membrane for selective extraction of caffeic acid and its structural and functional analogues from Taraxaci Herba

In the search for pharmaceutically active compounds from natural products, it is crucial and challenging to develop separation or purification methods that target not only structurally similar compounds but also those with specific pharmaceutical functions. The adsorption-based method is widely employed in this field and holds potential for this application, given the diverse range of functional monomers that can be chosen based on structural or functional selectivity. In this work, an imidazolium ionic liquid (IL) modified paper membrane was synthesized via microwave reaction. Caffeic acid (CA), with potential interactions with imidazolium IL and a representative component of phenolic acids in Taraxaci Herba, was chosen as a target compound. After optimization of synthesis and extraction parameters, the resulting extraction membrane could be used to quantitatively analyze CA at ng/ml level, and to extract CA's analogues from the sample matrix. Cheminformatics confirmed the presence of structural and functional similarity among these extracted compounds. This study offers a novel approach to preparing a readily synthesized extraction membrane capable of isolating compounds with structural and functional analogies, as well as developing a membrane solid-phase extraction-based analytical method for natural products.

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.

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.

Machine learning in preclinical drug discovery

Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that can augment the traditional drug-development process. In this Perspective, we discuss the integration of algorithmic methods throughout the preclinical phases of drug discovery. Specifically, we highlight an array of ML-based efforts, across diverse disease areas, to accelerate initial hit discovery, mechanism-of-action (MOA) elucidation and chemical property optimization. With advances in the application of ML across diverse therapeutic areas, we posit that fully ML-integrated drug-discovery pipelines will define the future of drug-development programs.

FragHub: A Mass Spectral Library Data Integration Workflow

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

Occurrence of "Natural Selection" in Successful Small Molecule Drug Discovery

Published compounds from ChEMBL version 32 are used to seek evidence for the occurrence of "natural selection" in drug discovery. Three measures of natural product (NP) character were applied, to compare time- and target-matched compounds reaching the clinic (clinical compounds in phase 1-3 development and approved drugs) with background compounds (reference compounds). Pseudo-NPs (PNPs), containing NP fragments combined in ways inaccessible by nature, are increasing over time, reaching 67% of clinical compounds first disclosed since 2010. PNPs are 54% more likely to be found in post-2008 clinical versus reference compounds. The majority of target classes show increased clinical compound NP character versus their reference compounds. Only 176 NP fragments appear in >1000 clinical compounds published since 2008, yet these make up on average 63% of the clinical compound's core scaffolds. There is untapped potential awaiting exploitation, by applying nature's building blocks─"natural intelligence"─to drug design.

Molecular mechanisms and therapeutic potential of natural flavonoids in diabetic nephropathy: Modulation of intracellular developmental signaling pathways

Recognized as a common microvascular complication of diabetes mellitus (DM), diabetic nephropathy (DN) is the principal cause of chronic end-stage renal disease (ESRD). Patients with diabetes have an approximately 25% risk of developing progressive renal disease. The underlying principles of DN control targets the dual outcomes of blood glucose regulation through sodium glucose cotransporter 2 (SGLT 2) blockade and hypertension management through renin-angiotensin-aldosterone inhibition. However, these treatments are ineffective in halting disease progression to kidney failure and cardiovascular comorbidities. Recently, the dysregulation of subcellular signaling pathways has been increasingly implicated in DN pathogenesis. Natural compounds are emerging as effective and side-effect-free therapeutic agents that target intracellular pathways. This narrative review synthesizes recent insights into the dysregulation of maintenance pathways in DN, drawing from animal and human studies. To compile this review, articles reporting DN signaling pathways and their treatment with natural flavonoids were collected from PubMed, Cochrane Library Web of Science, Google Scholar and EMBASE databases since 2000. As therapeutic interventions are frequently based on the results of clinical trials, a brief analysis of data from current phase II and III clinical trials on DN is discussed.

AI in analytical chemistry: Advancements, challenges, and future directions

This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.

HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules

Self-assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self-assembling peptides from scratch. This model explores the self-assembly properties by molecular structure, leveraging 1,377 self-assembling non-peptidal small molecules to navigate chemical space and improve structural diversity. Utilizing HydrogelFinder, 111 peptide candidates are generated and synthesized 17 peptides, subsequently experimentally validating the self-assembly and biophysical characteristics of nine peptides ranging from 1-10 amino acids-all achieved within a 19-day workflow. Notably, the two de novo-designed self-assembling peptides demonstrated low cytotoxicity and biocompatibility, as confirmed by live/dead assays. This work highlights the capacity of HydrogelFinder to diversify the design of self-assembling peptides through non-peptidal small molecules, offering a powerful toolkit and paradigm for future peptide discovery endeavors.

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction

Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying O ( n 3 ) matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity O ( n 2 ) . Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model's fully classical O ( n 3 ) analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

Design, synthesis, and biological evaluation of novel sesquiterpene lactone derivatives as PKM2 activators with potent anti-ulcerative colitis activities

Pyruvate kinase isoform 2 (PKM2) is closely related to the regulation of Th17/Treg balance, which is considered to be an effective strategy for UC therapy. Parthenolide (PTL), a natural product, only possesses moderate PKM2-activating activity. Thus, five series of PTL derivatives are designed and synthesized to improve PKM2-activated activities and anti-UC abilities. Through detailed structure optimization, B4 demonstrates potent T-cell anti-proliferation activity (IC50 = 0.43 μM) and excellent PKM2-activated ability (AC50 = 0.144 μM). Subsequently, through mass spectrometry analysis, B4 is identified to interact with Cys423 of PKM2 via covalent-bond. Molecular docking and molecular dynamic simulation results reveal that the trifluoromethoxy of B4 forms a stronger hydrophobic interaction with Ala401, Pro402, and Ile403. In addition, B4 has a significant effect only on Th17 cell differentiation, thereby regulating the Th17/Treg balance. The effect of B4 on Th17/Treg imbalance can be attributed to inhibition of PKM2 dimer translocation and suppression of glucose metabolism. Finally, B4 can notably ameliorate the symptoms of dextran sulfate sodium (DSS)-induced colitis in mouse model in vivo. Thus, B4 is confirmed as a potent PKM2 activator, and has the potential to develop as a novel anti-UC agent.

AI-based prediction of protein-ligand binding affinity and discovery of potential natural product inhibitors against ERK2

Determination of protein-ligand binding affinity (PLA) is a key technological tool in hit discovery and lead optimization, which is critical to the drug development process. PLA can be determined directly by experimental methods, but it is time-consuming and costly. In recent years, deep learning has been widely applied to PLA prediction, the key of which lies in the comprehensive and accurate representation of proteins and ligands. In this study, we proposed a multi-modal deep learning model based on the early fusion strategy, called DeepLIP, to improve PLA prediction by integrating multi-level information, and further used it for virtual screening of extracellular signal-regulated protein kinase 2 (ERK2), an ideal target for cancer treatment. Experimental results from model evaluation showed that DeepLIP achieved superior performance compared to state-of-the-art methods on the widely used benchmark dataset. In addition, by combining previously developed machine learning models and molecular dynamics simulation, we screened three novel hits from a drug-like natural product library. These compounds not only had favorable physicochemical properties, but also bound stably to the target protein. We believe they have the potential to serve as starting molecules for the development of ERK2 inhibitors.

A guide to artificial intelligence for cancer researchers

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.

Compendium of Metabolomic and Genomic Datasets for Cyanobacteria: Mined the Gap

Cyanobacterial blooms, producing toxic secondary metabolites, are becoming increasingly common phenomena in the face of rising global temperatures. They are the world's most abundant photosynthetic organisms, largely owing their success to a range of highly diverse and complex natural products possessing a broad spectrum of different bioactivities. Over 2600 compounds have been isolated from cyanobacteria thus far, and their characterisation has revealed unusual and useful chemistries and motifs including alkynes, halogens, and non-canonical amino acids. Genome sequencing of cyanobacteria lags behind natural product isolation, with only 19% of cyanobacterial natural products associated with a sequenced organism. Recent advances in meta(genomics) provide promise to narrow this gap and has also facilitated the uprise of combined genomic and metabolomic approaches, heralding a new era of discovery of novel compounds. Analyses of the datasets described within this manuscript reveal the asynchrony of current genomic and metabolomic data, highlight the chemical diversity of cyanobacterial natural products. Linked to this manuscript, we make these manually curated datasets freely accessible for the public to facilitate further research in this important area.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

The protective effect of natural medicines in rheumatoid arthritis via inhibit angiogenesis

Rheumatoid arthritis is a chronic immunological disease leading to the progressive bone and joint destruction. Angiogenesis, accompanied by synovial hyperplasia and inflammation underlies joint destruction. Delaying or even blocking synovial angiogenesis has emerged as an important target of RA treatment. Natural medicines has a long history of treating RA, and numerous reports have suggested that natural medicines have a strong inhibitory activity on synovial angiogenesis, thereby improving the progression of RA. Natural medicines could regulate the following signaling pathways: HIF/VEGF/ANG, PI3K/Akt pathway, MAPKs pathway, NF-κB pathway, PPARγ pathway, JAK2/STAT3 pathway, etc., thereby inhibiting angiogenesis. Tripterygium wilfordii Hook. f. (TwHF), sinomenine, and total glucoside of Paeonia lactiflora Pall. Are currently the most representative of all natural products worthy of development and utilization. In this paper, the main factors affecting angiogenesis were discussed and different types of natural medicines that inhibit angiogenesis were systematically summarized. Their specific anti-angiogenesis mechanisms are also reviewed which aiming to provide new perspective and options for the management of RA by targeting angiogenesis.

Halicin: A New Horizon in Antibacterial Therapy against Veterinary Pathogens

It is crucial to discover novel antimicrobial drugs to combat resistance. This study investigated the antibacterial properties of halicin (SU3327), an AI-identified anti-diabetic drug, against 13 kinds of common clinical pathogens of animal origin, including multidrug-resistant strains. Employing minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assessments, halicin demonstrated a broad-spectrum antibacterial effect. Time-killing assays revealed its concentration-dependent bactericidal activity against Escherichia coli ATCC 25922 (E. coli ATCC 25922), Staphylococcus aureus ATCC 29213 (S. aureus ATCC 29213), and Actinobacillus pleuropneumoniae S6 (APP S6) after 4 h of treatment at concentrations above the MIC. Halicin exhibited longer post-antibiotic effects (PAEs) and sub-MIC effects (PA-SMEs) for E. coli 25922, S. aureus 29213, and APP S6 compared to ceftiofur and ciprofloxacin, the commonly used veterinary antimicrobial agents, indicating sustained antibacterial action. Additionally, the results of consecutive passaging experiments over 40 d at sub-inhibitory concentrations showed that bacteria exhibited difficulty in developing resistance to halicin. Toxicology studies confirmed that halicin exhibited low acute toxicity, being non-mutagenic, non-reproductive-toxic, and non-genotoxic. Blood biochemical results suggested that halicin has no significant impact on hematological parameters, liver function, and kidney function. Furthermore, halicin effectively treated respiratory A. pleuropneumoniae infections in murine models. These results underscore the potential of halicin as a new antibacterial agent with applications against clinically relevant pathogens in veterinary medicine.

Morphological profiling for drug discovery in the era of deep learning

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer

Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure-property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.

Combinatorial biosynthesis for the engineering of novel fungal natural products

Natural products are small molecules synthesized by fungi, bacteria and plants, which historically have had a profound effect on human health and quality of life. These natural products have evolved over millions of years resulting in specific biological functions that may be of interest for pharmaceutical, agricultural, or nutraceutical use. Often natural products need to be structurally modified to make them suitable for specific applications. Combinatorial biosynthesis is a method to alter the composition of enzymes needed to synthesize a specific natural product resulting in structurally diversified molecules. In this review we discuss different approaches for combinatorial biosynthesis of natural products via engineering fungal enzymes and biosynthetic pathways. We highlight the biosynthetic knowledge gained from these studies and provide examples of new-to-nature bioactive molecules, including molecules synthesized using combinations of fungal and non-fungal enzymes.

Contribution of Sub-Saharan African medicinal plants to cancer research: Scientific basis 2013-2023

Cancer incidence and mortality rates are increasing worldwide. Cancer treatment remains a real challenge for African countries, especially in sub-Saharan Africa where funding and resources are very limited. High costs, side effects and drug resistance associated with cancer treatment have encouraged scientists to invest in research into new herbal cancer drugs. In order to identify potential anticancer plants for drug development, this review aims to collect and summarize anticancer activities (in vitro/in vivo) and molecular mechanisms of sub-Saharan African medicinal plant extracts against cancer cell lines. Scientific databases such as ScienceDirect, Google Scholar and PubMed were used to search for research articles published from January 2013 to May 2023 on anticancer medicinal plants in sub-Saharan Africa. The data were analyzed to highlight the cytotoxicity and molecular mechanisms of action of these listed plants. A total of 85 research papers covering 204 medicinal plant species were selected for this review. These plants come from 57 families, the most dominant being the plants of the family Amaryllidaceae (16), Fabaceae (14), Annonaceae (10), Asteraceae (10). Plant extracts exert their anticancer activity mainly by inducing apoptosis and stopping the cell cycle of cancer cells. Several plant extracts from sub-Saharan Africa therefore have strong potential for the search for original anticancer phytochemicals. Chemoproteomics, multi-omics, genetic editing technology (CRISPR/Cas9), combined therapies and artificial intelligence tools are cutting edge emerging technologies that facilitate the discovery and structural understanding of anticancer molecules of medicinal plants, reveal their direct targets, explore their therapeutic uses and molecular bases.

Advancing diabetes treatment: the role of mesenchymal stem cells in islet transplantation

Diabetes mellitus, a prevalent global health challenge, significantly impacts societal and economic well-being. Islet transplantation is increasingly recognized as a viable treatment for type 1 diabetes that aims to restore endogenous insulin production and mitigate complications associated with exogenous insulin dependence. We review the role of mesenchymal stem cells (MSCs) in enhancing the efficacy of islet transplantation. MSCs, characterized by their immunomodulatory properties and differentiation potential, are increasingly seen as valuable in enhancing islet graft survival, reducing immune-mediated rejection, and supporting angiogenesis and tissue repair. The utilization of MSC-derived extracellular vesicles further exemplifies innovative approaches to improve transplantation outcomes. However, challenges such as MSC heterogeneity and the optimization of therapeutic applications persist. Advanced methodologies, including artificial intelligence (AI) and single-cell RNA sequencing (scRNA-seq), are highlighted as potential technologies for addressing these challenges, potentially steering MSC therapy toward more effective, personalized treatment modalities for diabetes. This review revealed that MSCs are important for advancing diabetes treatment strategies, particularly through islet transplantation. This highlights the importance of MSCs in the field of regenerative medicine, acknowledging both their potential and the challenges that must be navigated to fully realize their therapeutic promise.

Effectiveness of molecular fingerprints for exploring the chemical space of natural products

Natural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the best performance on the natural product chemical space. We considered 20 molecular fingerprints from four different sources, which we then benchmarked on over 100,000 unique natural products from the COCONUT (COlleCtion of Open Natural prodUcTs) and CMNPD (Comprehensive Marine Natural Products Database) databases. Our analysis focused on the correlation between different fingerprints and their classification performance on 12 bioactivity prediction datasets. Our results show that different encodings can provide fundamentally different views of the natural product chemical space, leading to substantial differences in pairwise similarity and performance. While Extended Connectivity Fingerprints are the de-facto option to encoding drug-like compounds, other fingerprints resulted to match or outperform them for bioactivity prediction of natural products. These results highlight the need to evaluate multiple fingerprinting algorithms for optimal performance and suggest new areas of research. Finally, we provide an open-source Python package for computing all molecular fingerprints considered in the study, as well as data and scripts necessary to reproduce the results, at https://github.com/dahvida/NP_Fingerprints .

The antibiotic crisis: On the search for novel antibiotics and resistance mechanisms

In the relentless battle for human health, the proliferation of antibiotic-resistant bacteria has emerged as an impending catastrophe of unprecedented magnitude, potentially driving humanity towards the brink of an unparalleled healthcare crisis. The unyielding advance of antibiotic resistance looms as the foremost threat of the 21st century in clinical, agricultural and environmental arenas. Antibiotic resistance is projected to be the genesis of the next global pandemic, with grim estimations of tens of millions of lives lost annually by 2050. Amidst this impending calamity, our capacity to unearth novel antibiotics has languished, with the past four decades marred by a disheartening 'antibiotic discovery void'. With nearly 80% of our current antibiotics originating from natural or semi-synthetic sources, our responsibility is to cast our investigative nets into uncharted ecological niches teeming with microbial strife, the so-called 'microbial oases of interactions'. Within these oases of interactions, where microorganisms intensively compete for space and nutrients, a dynamic and ever-evolving microbial 'arms race' is constantly in place. Such a continuous cycle of adaptation and counter-adaptation is a fundamental aspect of microbial ecology and evolution, as well as the secrets to unique, undiscovered antibiotics, our last bastion against the relentless tide of resistance. In this context, it is imperative to invest in research to explore the competitive realms, like the plant rhizosphere, biological soil crusts, deep sea hydrothermal vents, marine snow and the most modern plastisphere, in which competitive interactions are at the base of the microorganisms' struggle for survival and dominance in their ecosystems: identify novel antibiotic by targeting microbial oases of interactions could represent a 'missing piece of the puzzle' in our fight against antibiotic resistance.

Natural products in osteoarthritis treatment: bridging basic research to clinical applications

Osteoarthritis (OA) is the most prevalent degenerative musculoskeletal disease, severely impacting the function of patients and potentially leading to disability, especially among the elderly population. Natural products (NPs), obtained from components or metabolites of plants, animals, microorganisms etc., have gained significant attention as important conservative treatments for various diseases. Recently, NPs have been well studied in preclinical and clinical researches, showing promising potential in the treatment of OA. In this review, we summed up the main signaling pathways affected by NPs in OA treatment, including NF-κB, MAPKs, PI3K/AKT, SIRT1, and other pathways, which are related to inflammation, anabolism and catabolism, and cell death. In addition, we described the therapeutic effects of NPs in different OA animal models and the current clinical studies in OA patients. At last, we discussed the potential research directions including in-depth analysis of the mechanisms and new application strategies of NPs for the OA treatment, so as to promote the basic research and clinical transformation in the future. We hope that this review may allow us to get a better understanding about the potential bioeffects and mechanisms of NPs in OA therapy, and ultimately improve the effectiveness of NPs-based clinical conservative treatment for OA patients.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Predicting success in Cu-catalyzed C-N coupling reactions using data science

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

Complete genome sequence of Brachybacterium sp. GU-2 (Actinomycetota), isolated from the massive coral Porites lobata

Brachybacterium sp. GU-2 was isolated from the hard coral Porites lobata found in Apra Harbor, Guam, Micronesia. This genome sequence will be beneficial to understand the role of actinomycetes in coral holobionts. The Brachybacterium genome contains several gene clusters for bioactive compounds, including antibiotics.

Research progress of plant-derived natural products in thyroid carcinoma

Thyroid carcinoma (TC) is a prevalent malignancy of the endocrine system, with a notable rise in its detection rate in recent decades. The primary therapeutic approaches for TC now encompass thyroidectomy and radioactive iodine therapy, yielding favorable prognoses for the majority of patients. TC survivors may necessitate ongoing surveillance, remedial treatment, and thyroid hormone supplementation, while also enduring the adverse consequences of thyroid hormone fluctuations, surgical complications, or side effects linked to radioactive iodine administration, and encountering enduring physical, psychosocial, and economic hardships. In vitro and in vivo studies of natural products against TC are demonstrating the potential of these natural products as alternatives to the treatment of thyroid cancer. This therapy may offer greater convenience, affordability, and acceptability than traditional therapies. In the early screening of natural products, we mainly use a combination of database prediction and literature search. The pharmacological effects on TC of selected natural products (quercetin, genistein, apigenin, luteolin, chrysin, myricetin, resveratrol, curcumin and nobiletin), which hold promise for therapeutic applications in TC, are reviewed in detail in this article through most of the cell-level evidence, animal-level evidence, and a small amount of human-level evidence. In addition, this article explores possible issues, such as bioavailability, drug safety.

Harnessing regulatory networks in Actinobacteria for natural product discovery

Microbes typically live in complex habitats where they need to rapidly adapt to continuously changing growth conditions. To do so, they produce an astonishing array of natural products with diverse structures and functions. Actinobacteria stand out for their prolific production of bioactive molecules, including antibiotics, anticancer agents, antifungals, and immunosuppressants. Attention has been directed especially towards the identification of the compounds they produce and the mining of the large diversity of biosynthetic gene clusters (BGCs) in their genomes. However, the current return on investment in random screening for bioactive compounds is low, while it is hard to predict which of the millions of BGCs should be prioritized. Moreover, many of the BGCs for yet undiscovered natural products are silent or cryptic under laboratory growth conditions. To identify ways to prioritize and activate these BGCs, knowledge regarding the way their expression is controlled is crucial. Intricate regulatory networks control global gene expression in Actinobacteria, governed by a staggering number of up to 1000 transcription factors per strain. This review highlights recent advances in experimental and computational methods for characterizing and predicting transcription factor binding sites and their applications to guide natural product discovery. We propose that regulation-guided genome mining approaches will open new avenues toward eliciting the expression of BGCs, as well as prioritizing subsets of BGCs for expression using synthetic biology approaches.

ONE-SENTENCE SUMMARY

This review provides insights into advances in experimental and computational methods aimed at predicting transcription factor binding sites and their applications to guide natural product discovery.

Unveiling the Pool of Metallophores in Native Environments and Correlation with Their Potential Producers

For many organisms, metallophores are essential biogenic ligands that ensure metal scavenging and acquisition from their environment. Their identification is challenging in highly organic matter rich environments like peatlands due to low solubilization and metal scarcity and high matrix complexity. In contrast to common approaches based on sample modification by spiking of metal isotope tags, we have developed a two-dimensional (2D) Solid-phase extraction-Liquid chromatography-mass spectrometry (SPE-LC-MS) approach for the highly sensitive (LOD 40 fmol per g of soil), high-resolution direct detection and identification of metallophores in both their noncomplexed (apo) and metal-complexed forms in native environments. The characterization of peat collected in the Bernadouze (France) peatland resulted in the identification of 53 metallophores by a database mass-based search, 36 among which are bacterial. Furthermore, the detection of the characteristic (natural) metal isotope patterns in MS resulted in the detection of both Fe and Cu potential complexes. A taxonomic-based inference method was implemented based on literature and public database (antiSMASH database version 3.0) searches, enabling to associate over 40% of the identified bacterial metallophores with potential producers. In some cases, low completeness with the MIBiG reference BCG might be indicative of alternative producers in the ecosystem. Thus, coupling of metallophore detection and producers' inference could pave a new way to investigate poorly documented environment searching for new metallophores and their producers yet unknown.