Machine learning approaches for elucidating the biological effects of natural products

Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis

The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the body, including the brain and spine. Developing a single drug can take several decades, making drug discovery costly and time-consuming. Machine learning algorithms like support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF) and Gaussian naive base (GNB) are fast and effective and are commonly used in drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. For the training of the models, a dataset of 307 was downloaded from BindingDB. Among 307 compounds, 85 compounds were labeled as active, having an IC50 below 58 mM, while 222 compounds were labeled inactive against thymidylate kinase, with 87.2% accuracy. The developed models were subjected to an external ZINC dataset of 136,564 compounds. Furthermore, we performed the 100-ns dynamic simulation and post trajectories analysis of compounds having good interaction and score in molecular docking. As compared to the standard reference compound, the top three hits revealed greater stability and compactness. In conclusion, our predicted hits can inhibit thymidylate kinase overexpression to combat Mycobacterium tuberculosis.Communicated by Ramaswamy H. Sarma.

Artifical intelligence: a virtual chemist for natural product drug discovery

Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.

PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning

Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a "within-one" measure that reaches 93.0% accuracy.

Identification of Active Components for Sports Supplements: Machine Learning-Driven Classification and Cell-Based Validation

The identification of active components is critical for the development of sports supplements. However, high-throughput screening of active components remains a challenge. This study sought to construct prediction models to screen active components from herbal medicines via machine learning and validate the screening by using cell-based assays. The six constructed models had an accuracy of >0.88. Twelve randomly selected active components from the screening were tested for their active potency on C2C12 cells, and 11 components induced a significant increase in myotube diameters and protein synthesis. The effect and mechanism of luteolin among the 11 active components as potential sports supplements were then investigated by using immunofluorescence staining and high-content imaging analysis. It showed that luteolin increased the skeletal muscle performance via the activation of PGC-1α and MAPK signaling pathways. Thus, high-throughput prediction models can be effectively used to screen active components as sports supplements.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.

Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms

Machine learning (ML) models have made inroads into chemical sciences, with optimization of chemical reactions and prediction of biologically active molecules being prime examples thereof. These models excel where physical experiments are expensive or time-consuming, for example, due to large scales or the need for materials that are difficult to obtain. Studies of natural products suffer from these issues─this class of small molecules is known for its wealth of structural diversity and wide-ranging biological activities, but their investigation is hindered by poor synthetic accessibility and lack of scalability. To facilitate the evaluation of these molecules, we designed ML models that predict which natural products can interact with a particular target or a relevant pathway. Here, we focused on discovering natural products that are capable of modulating the 5-lipoxygenase (5-LO) pathway that plays key roles in lipid signaling and inflammation. These computational approaches led to the identification of nine natural products that either directly inhibit the activity of the 5-LO enzyme or affect the cellular 5-LO pathway. Further investigation of one of these molecules, deltonin, led us to discover a new cell-type-selective mechanism of action. Our ML approach helped deorphanize natural products as well as shed light on their mechanisms and can be broadly applied to other use cases in chemical biology.

Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning

BACKGROUND

Astragali Radix (AR) is a widely used herbal medicine. The quality of AR is influenced by several key factors, including the production area, growth mode, species, and grade. However, the markers currently used to distinguish these factors primarily focus on secondary metabolites, and their validation on large-scale samples is lacking.

PURPOSE

This study aims to discover reliable markers and develop classification models for identifying the production area, growth mode, species, and grade of AR.

METHODS

A total of 366 batches of AR crude slices were collected from six provinces in China and divided into learning (n = 191) and validation (n = 175) sets. Three ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were developed and validated for determining 22 primary and 10 secondary metabolites in AR methanol extract. Based on the quantification data, seven machine learning algorithms, such as Nearest Neighbors and Gradient Boosted Trees, were applied to screen the potential markers and build the classification models for identifying the four factors associated with AR quality.

RESULTS

Our analysis revealed that secondary metabolites (e.g., astragaloside IV, calycosin-7-O-β-D-glucoside, and ononin) played a crucial role in evaluating AR quality, particularly in identifying the production area and species. Additionally, fatty acids (e.g., behenic acid and lignoceric acid) were vital in determining the growth mode of AR, while amino acids (e.g., alanine and phenylalanine) were helpful in distinguishing different grades. With both primary and secondary metabolites, the Nearest Neighbors algorithm-based model was constructed for identifying each factor of AR, achieving good classification accuracy (>70%) on the validation set. Furthermore, a panel of four metabolites including ononin, astragaloside II, pentadecanoic acid, and alanine, allowed for simultaneous identification of all four factors of AR, offering an accuracy of 86.9%.

CONCLUSION

Our findings highlight the potential of integrating large-scale targeted metabolomics and machine learning approaches to accurately identify the quality-associated factors of AR. This study opens up possibilities for enhancing the evaluation of other herbal medicines through similar methodologies, and further exploration in this area is warranted.

Recent advances towards natural plants as potential inhibitors of SARS-Cov-2 targets

CONTEXT

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still ongoing and currently the most striking epidemic disease. With the rapid global spread of SARS-CoV-2 variants, new antivirals are urgently needed to avert a more serious crisis. Inhibitors from traditional medicines or natural plants have shown promising results to fight COVID-19 with different mechanisms of action.

OBJECTIVES

To provide comprehensive and promising approaches to the medical community in the fight against this epidemic by reviewing potential plant-derived anti-SARS-CoV-2 inhibitors.

METHODS

Structural databases such as TCMSP (http://lsp.nwu.edu.cn/tcmsp.php), TCM Database @ Taiwan (http://tcm.cmu.edu.tw/), BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/) and TCMID (http://www.megabionet.org/tcmid/), as well as PubMed, Sci Finder, Research Gate, Science Direct, CNKI, Web of Science and Google Scholar were searched for relevant articles on TCMs and natural products against SARS-CoV-2.

RESULTS

Seven traditional Chinese medicines formulas have unique advantages in regulating the immune system for treating COVID-19. The plant-derived natural compounds as anti-SARS-CoV-2 inhibitors were identified based on 5 SARS-CoV-2 key proteins, namely, angiotensin-converting enzyme 2 (ACE2), 3 C-like protease (3CLpro), papain-like protease (PLpro), spike (S) protein, and nucleocapsid (N) protein.

CONCLUSIONS

A variety of natural products, such as flavonoids, terpenoids, phenols, and alkaloids, were identified, which could be used as potential SASR-Cov-2 inhibitors. These shed new light on the efficient discovery of SASR-Cov-2 inhibitors from natural products.

Machine learning-assisted structure annotation of natural products based on MS and NMR data

Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Artesunate, a new antimalarial clinical drug, exhibits potent anti-AML activity by targeting the ROS/Bim and TFRC/Fe2+ pathways

BACKGROUND AND PURPOSE

Artesunate, approved by the Food and Drug Administration in 2020 as a new treatment for severe malaria, also shows anti-tumour activity against acute myeloid leukaemia (AML). However, the underlying molecular mechanism(s) of artesunate-induced apoptosis and differentiation of AML is not clearly elucidated.

EXPERIMENTAL APPROACH

The biological effects of artesunate on AML were explored in vitro, using cells from AML patients and leukaemia cell lines, and in vivo, using female C57BL/6 or nude nu/nu BALB/c mice. Underlying mechanisms in vitro were examined with the Trypan blue dye exclusion assay, western blotting and flow cytometry. Effects of artesunate in C57BL/6 mice intravenously injected with murine AML cells (C1498-GFP) were assessed by numbers of AML cells and by survival.

KEY RESULTS

In vitro, artesunate promoted apoptosis and differentiation in both leukaemia cell lines and patient-derived primary leukaemia cells. Mechanistically, artesunate promoted cell apoptosis by triggering reactive oxygen species (ROS) production and increasing expression of the pro-apoptotic protein Bim. Interestingly, transferrin receptor 1 (TFRC)-mediated regulation of intracellular iron homeostasis also played an essential role in AML cell differentiation induced by artesunate. In vivo, artesunate slowed AML progression and prolonged survival in a mouse leukaemia model. Notably, artesunate displayed no apparent toxicity towards healthy haematopoietic stem cells, bone marrow mononuclear cells or experimental animals.

CONCLUSION AND IMPLICATIONS

Artesunate is a safe agent with significant anti-leukaemia effects in mice and may serve as a promising chemotherapeutic strategy for patients with AML, based on two different mechanisms, targeting the ROS/Bim and the TFRC/Fe²⁺ pathways.

A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

As a type of stress hormone, glucocorticoids (GCs) affect numerous physiological pathways by binding to the glucocorticoid receptor (GR) and regulating the transcription of various genes. However, when GCs are dysregulated, the resulting hypercortisolism may contribute to various metabolic disorders, including obesity. Thus, attempts have been made to discover potent GR antagonists that can reverse excess-GC-related metabolic diseases. Phytochemicals are a collection of valuable bioactive compounds that are known for their wide variety of chemotypes. Recently, various computational methods have been developed to obtain active phytochemicals that can modulate desired target proteins. In this study, we developed a workflow comprising two consecutive quantitative structure-activity relationship-based machine learning models to discover novel GR-antagonizing phytochemicals. These two models collectively identified 65 phytochemicals that bind to and antagonize GR. Of these, nine commercially available phytochemicals were validated for GR-antagonist and anti-obesity activities. In particular, we confirmed that demethylzeylasteral, a phytochemical of the Tripterygium wilfordii Radix, exhibits potent anti-obesity activity in vitro through GR antagonism.

Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach

Introduction

Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of personalized medicine, the operation mechanism is largely unknown. To gain a better understanding, we investigated the compound information that composes Sasang type-specific personalized herbal medicines on both multivariate and univariate levels.

Methods

Five machine learning classifiers including extremely randomized trees (ERT) were trained to investigate whether the Sasang type can be explained by compound information at the multivariate level. Hierarchical clustering was conducted to determine whether compounds are processed distributedly or specifically. Taxonomic and biosynthetic analyses were conducted on these compounds. A univariate level statistical test was conducted to provide more robust Sasang type-specific compound information.

Results

Using the trained ERT classifier, sixty important compounds were extracted. The sixty compounds were clustered into three groups, corresponding to each Sasang type-prominent compounds, suggesting that most compounds have specific preference for the Sasang type. Structural and biosynthetic characteristics of these Sasang type-prominent compounds were determined based on taxonomy and pathway analyses. Fourteen compounds showed statistically significant relevance with the Sasang type. Additionally, we predicted the Sasang type of unknown herbs, which were confirmed by their biological effects in functional assays.

Conclusion

This study investigated the personalized herbal medicines of the SCM using compound information. This study provided information on the chemical characteristics of the compounds that are essential for classifying the Sasang type of medicinal herbs, as well as predictions regarding the Sasang type of the commonly used but unidentified medicinal herbs.

Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts

Natural extracts have been and continue to be used to treat a wide range of medical conditions, from infectious diseases to cancer, based on their convenience and therapeutic potential. Natural products derived from microbes, plants, and animals offer a broad variety of molecules and chemical compounds. Natural products are not only one of the most important sources for innovative drug development for animal and human health, but they are also an inspiration for synthetic biology and chemistry scientists towards the discovery of new bioactive compounds and pharmaceuticals. This is particularly relevant in the current context, where antimicrobial resistance has risen as a global health problem. Thus, efforts are being directed toward studying natural compounds' chemical composition and bioactive potential to generate drugs with better efficacy and lower toxicity than existing molecules. Currently, a wide range of methodologies are used to analyze the in vitro activity of natural extracts to determine their suitability as antimicrobial agents. Despite traditional technologies being the most employed, technological advances have contributed to the implementation of methods able to circumvent issues related to analysis capacity, time, sensitivity, and reproducibility. This review produces an updated analysis of the conventional and current methods to evaluate the antimicrobial activity of natural compounds.

Proteomic Analysis of Human Breast Cancer MCF-7 Cells to Identify Cellular Targets of the Anticancer Pigment OR3 from Streptomyces coelicolor JUACT03

Search for ideal compounds with known pathways of anticancer mechanism is still a priority research focus for cancer, as it continues to be a major health challenge across the globe. Hence, in the present study, anticancer potential of a yellow pigment fraction, OR3, isolated from Streptomyces coelicolor JUACT03 was assessed on the breast cancer cell line MCF-7. TLC-fractionated OR3 pigment was subjected to HPLC and GC-MS analysis for characterization and identification of the bioactive component. MCF-7 cells were treated with IC₅₀ concentration of OR3 and the molecular alterations were analyzed using mass spectrometry-based quantitative proteomic analysis. Bioinformatics tools such as STRING analysis and Ingenuity Pathway Analysis were performed to analyze proteomics data and to identify dysregulated signaling pathways. As per our obtained data, OR3 treatment decreased cell proliferation and induced apoptotic cell death due to significant dysregulation of protein expressions in MCF-7 cells. Altered expression included the ribosomal, mRNA processing and vesicle-mediated transport proteins as a result of OR3 treatment. Downregulation of MAPK proteins, NFkB, and estradiol signaling was identified in OR3-treated MCF-7 cells. Mainly eIF2, mTOR, and eIF4 signaling pathways were altered in OR3-treated cells. GC-MS data indicated the presence of novel compounds in OR3 fraction. It can be concluded that OR3 exhibits potent anticancer activity on the breast cancer cells mainly through altering the expression and affecting the signaling proteins which are involved in different cell proliferation/apoptotic pathways thereby causing inhibition of cancer cell proliferation, survival and metastasis.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Role of N6-Methyladenosine Methylation Regulators in the Drug Therapy of Digestive System Tumours

Digestive system tumours, including stomach, colon, esophagus, liver and pancreatic tumours, are serious diseases affecting human health. Although surgical treatment and postoperative chemoradiotherapy effectively improve patient survival, current diagnostic and therapeutic strategies for digestive system tumours lack sensitivity and specificity. Moreover, the tumour's tolerance to drug therapy is enhanced owing to tumour cell heterogeneity. Thus, primary or acquired treatment resistance is currently the main hindrance to chemotherapy efficiency. N6-methyladenosine (m6A) has various biological functions in RNA modification. m6A modification, a key regulator of transcription expression, regulates RNA metabolism and biological processes through the interaction of m6A methyltransferase ("writers") and demethylase ("erasers") with the binding protein decoding m6A methylation ("readers"). Additionally, m6A modification regulates the occurrence and development of tumours and is a potential driving factor of tumour drug resistance. This review systematically summarises the regulatory mechanisms of m6A modification in the drug therapy of digestive system malignancies. Furthermore, it clarifies the related mechanisms and therapeutic prospects of m6A modification in the resistence of digestive system malignancies to drug therapy.

InflamNat: web-based database and predictor of anti-inflammatory natural products

Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. The experimental results showed that the proposed predictive tools achieved an AUC value of 0.842 and 0.872 in the prediction of anti-inflammatory activity and compound-target interactions, respectively.

Machine Learning in Antibacterial Drug Design

Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling

Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 μM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery.

Virtual screening of phytochemicals from Indian medicinal plants against the endonuclease domain of SFTS virus L polymerase

Severe fever with thrombocytopenia syndrome virus (SFTSV) causes a highly infectious disease with reported mortality in the range 2.8% to 47%. The replication and transcription of the SFTSV genome is performed by L polymerase, which has both an RNA dependent RNA polymerase domain and an N-terminal endonuclease (endoN) domain. Due to its crucial role in the cap-snatching mechanism required for initiation of viral RNA transcription, the endoN domain is an ideal antiviral drug target. In this virtual screening study for the identification of potential inhibitors of the endoN domain of SFTSV L polymerase, we have used molecular docking and molecular dynamics (MD) simulation to explore the natural product space of 14 011 phytochemicals from Indian medicinal plants. After generating a heterogeneous ensemble of endoN domain structures reflecting conformational diversity of the corresponding active site using MD simulations, ensemble docking of the phytochemicals was performed against the endoN domain structures. Apart from the ligand binding energy from docking, our virtual screening workflow imposes additional filters such as drug-likeness, non-covalent interactions with key active site residues, toxicity and chemical similarity with other hits, to identify top 5 potential phytochemical inhibitors of endoN domain of SFTSV L polymerase. Further, the stability of the protein–ligand docked complexes for the top 5 potential inhibitors was analyzed using MD simulations. The potential phytochemical inhibitors, predicted in this study using contemporary computational methods, are expected to serve as lead molecules in future experimental studies towards development of antiviral drugs against SFTSV.

Virtual screening of a large phytochemical library from Indian medicinal plants to identify potential endonuclease inhibitors against emerging virus SFTSV.

m6A modification: recent advances, anticancer targeted drug discovery and beyond

Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets. Both traditional medicine-related approaches and modern drug discovery platforms have been used in an attempt to develop m6A-targeted drugs. Here, we provide an update of the latest findings on m6A modification and the critical roles of m6A modification in cancer progression, and we summarize rational sources for the discovery of m6A-targeted anticancer agents from traditional medicines and computer-based chemosynthetic compounds. This review highlights the potential agents targeting m6A modification for cancer treatment and proposes the advantage of artificial intelligence (AI) in the discovery of m6A-targeting anticancer drugs.

Graphical abstract

Three stages of m6A-targeting anticancer drug discovery: traditional medicine-based natural products, modern chemical modification or synthesis, and artificial intelligence (AI)-assisted approaches for the future.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation‐based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.

Marine natural products targeting the eukaryotic cell membrane

The cell membrane, with high fluidity and alternative curvatures, maintains the robust integrity to distinguish inner and outer space of cells or organelles. Lipids are the main components of the cell membrane, but their functions are largely unknown. Even the visualization of lipids is not straightforward since modification of lipids often hampers its correct physical properties. Many natural products target cell membranes, some of which are used as pharmaceuticals and/or research tools. They show specific recognition on lipids, and thus exhibit desired pharmacological effects and unique biological phenotypes. This review is a catalog of marine natural products that target eukaryotic cell membranes. Chemical structures, biological activities, and molecular mechanisms are summarized. I hope that this review will be helpful for readers to notice the potential of marine natural products in the exploration of the function of lipids and the druggability of eukaryotic cell membranes.

Burkholderia in the genomic era: from taxonomy to the discovery of new antimicrobial secondary metabolites

Species of Burkholderia are highly versatile being found not only abundantly in soil, but also as plants and animals' commensals or pathogens. Their complex multireplicon genomes harbour an impressive number of polyketide synthase (PKS) and nonribosomal peptide-synthetase (NRPS) genes coding for the production of antimicrobial secondary metabolites (SMs), which have been successfully deciphered by genome-guided tools. Moreover, genome metrics supported the split of this genus into Burkholderia sensu stricto (s.s.) and five new other genera. Here, we show that the successful antimicrobial SMs producers belong to Burkholderia s.s. Additionally, we reviewed the occurrence, bioactivities, modes of action, structural, and biosynthetic information of thirty-eight Burkholderia antimicrobial SMs shedding light on their diversity, complexity, and uniqueness as well as the importance of genome-guided strategies to facilitate their discovery. Several Burkholderia NRPS and PKS display unusual features, which are reflected in their structural diversity, important bioactivities, and varied modes of action. Up to now, it is possible to observe a general tendency of Burkholderia SMs being more active against fungi. Although the modes of action and biosynthetic gene clusters of many SMs remain unknown, we highlight the potential of Burkholderia SMs as alternatives to fight against new diseases and antibiotic resistance.

Predicting biochemical and physiological effects of natural products from molecular structures using machine learning

Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

Natural Products in Cancer Therapy: Past, Present and Future

Natural products, with remarkable chemical diversity, have been extensively investigated for their anticancer potential for more than a half-century. The collective efforts of the community have achieved the tremendous advancements, bringing natural products to clinical use and discovering new therapeutic opportunities, yet the challenges remain ahead. With remarkable changes in the landscape of cancer therapy and growing role of cutting-edge technologies, we may have come to a crossroads to revisit the strategies to understand nature products and to explore their therapeutic utility. This review summarizes the key advancements in nature product-centered cancer research and calls for the implementation of systematic approaches, new pharmacological models, and exploration of emerging directions to revitalize natural products search in cancer therapy.

The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability

Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.

Bacterial pathogens: threat or treat (a review on bioactive natural products from bacterial pathogens)

Covering: up to the second quarter of 2020 Threat or treat? While pathogenic bacteria pose significant threats, they also represent a huge reservoir of potential pharmaceuticals to treat various diseases. The alarming antimicrobial resistance crisis and the dwindling clinical pipeline urgently call for the discovery and development of new antibiotics. Pathogenic bacteria have an enormous potential for natural products drug discovery, yet they remained untapped and understudied. Herein, we review the specialised metabolites isolated from entomopathogenic, phytopathogenic, and human pathogenic bacteria with antibacterial and antifungal activities, highlighting those currently in pre-clinical trials or with potential for drug development. Selected unusual biosynthetic pathways, the key roles they play (where known) in various ecological niches are described. We also provide an overview of the mode of action (molecular target), activity, and minimum inhibitory concentration (MIC) towards bacteria and fungi. The exploitation of pathogenic bacteria as a rich source of antimicrobials, combined with the recent advances in genomics and natural products research methodology, could pave the way for a new golden age of antibiotic discovery. This review should serve as a compendium to communities of medicinal chemists, organic chemists, natural product chemists, biochemists, clinical researchers, and many others interested in the subject.

A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds

Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost. To overcome these limitations, several in silico methods have been proposed for conducting large-scale analysis, but they are still limited in terms of dealing with incomplete and heterogeneous natural compound data. Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data. The rationale behind this approach is that deep learning can effectively utilize heterogeneous features to alleviate incomplete information. Based on latent knowledge, molecular interactions, and chemical property features, we generated 686 dimensional features for 4,507 natural compounds and 2,882 approved and investigational drugs. The deep learning model was trained using the generated features and verified drug indication information. When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.

Cheminformatics to Characterize Pharmacologically Active Natural Products

Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.