Characterization of the Chemical Space of Known and Readily Obtainable Natural Products

ShennongAlpha: an AI-driven sharing and collaboration platform for intelligent curation, acquisition, and translation of natural medicinal material knowledge

Natural Medicinal Materials (NMMs) have a long history of global clinical applications and a wealth of records and knowledge. Although NMMs are a major source for drug discovery and clinical application, the utilization and sharing of NMM knowledge face crucial challenges, including the standardized description of critical information, efficient curation and acquisition, and language barriers. To address these, we developed ShennongAlpha, an artificial intelligence (AI)-driven sharing and collaboration platform for intelligent knowledge curation, acquisition, and translation. For standardized knowledge curation, the platform introduced a Systematic Nomenclature to enable accurate differentiation and identification of NMMs. More than fourteen thousand Chinese NMMs have been curated into the platform along with their knowledge. Furthermore, the platform pioneered chat-based knowledge acquisition, standardized machine translation, and collaborative knowledge updating. Together, our study represents the first major advance in leveraging AI to empower NMM knowledge sharing, which not only marks a novel application of AI for science, but also will significantly benefit the global biomedical, pharmaceutical, physician, and patient communities.

Chemoinformatic Characterization of NAPROC-13: A Database for Natural Product 13C NMR Dereplication

Natural products (NPs) are secondary metabolites of natural origin with broad applications across various human activities, particularly the discovery of bioactive compounds. Structural elucidation of new NPs entails significant cost and effort. On the other hand, the dereplication of known compounds is crucial for the early exclusion of irrelevant compounds in contemporary pharmaceutical research. NAPROC-13 stands out as a publicly accessible database, providing structural and 13C NMR spectroscopic information for over 25 000 compounds, rendering it a pivotal resource in natural product (NP) research, favoring open science. This study seeks to quantitatively analyze the chemical content, structural diversity, and chemical space coverage of NPs within NAPROC-13, compared to FDA-approved drugs and a very diverse subset of NPs, UNPD-A. Findings indicated that NPs in NAPROC-13 exhibit properties comparable to those in UNPD-A, albeit showcasing a notably diverse array of structural content, scaffolds, ring systems of pharmaceutical interest, and molecular fragments. NAPROC-13 covers a specific region of the chemical multiverse (a generalization of the chemical space from different chemical representations) regarding physicochemical properties and a region as broad as UNPD-A in terms of the structural features represented by fingerprints.

Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process

Natural products (NPs) or their derivatives represent a large proportion of drugs that successfully progress through clinical trials to approval. This study explores the presence of NPs in both early- and late-stage drug discovery to determine their success rate, and the factors or features of natural products that contribute to such success. As a proxy for early drug development stages, we analyzed patent applications over several decades, finding a consistent proportion of NP, NP-derived, and synthetic-compound-based patent documents, with the latter group outnumbering NP and NP-derived ones (approximately 77% vs 23%). We next assessed clinical trial data, where we observed a steady increase in NP and NP-derived compounds from clinical trial phases I to III (from approximately 35% in phase I to 45% in phase III), with an inverse trend observed in synthetics (from approximately 65% in phase I to 55% in phase III). Finally, in vitro and in silico toxicity studies revealed that NPs and their derivatives were less toxic alternatives to their synthetic counterparts. These discoveries offer valuable insights for successful NP-based drug development, highlighting the potential benefits of prioritizing NPs and their derivatives as starting points.

Introducing the NUATEI Consortium: A Mexican Research Program for the Identification of Natural and Synthetic Antimicrobial Compounds for Prevalent Infectious Diseases

The need for new drugs to treat human infections is a global health concern. Diseases like tuberculosis, trypanosomiasis, amoebiasis, and AIDS remain significant problems, especially in developing countries like Mexico. Despite existing treatments, issues such as resistance and adverse effects drive the search for new alternatives. Herein, we introduce the NUATEI research consortium, made up of experts from the Institute of Biomedical Research at UNAM, who identify and obtain natural and synthetic compounds and test their effects against human pathogens using in vitro and in vivo models. The consortium has evaluated hundreds of natural extracts and compounds against the pathogens causing tuberculosis, trypanosomiasis, amoebiasis, and AIDS, rendering promising results, including a patent with potential for preclinical studies. This paper presents the rationale behind the formation of this consortium, as well as its objectives and strategies, emphasizing the importance of natural and synthetic products as sources of antimicrobial compounds and the relevance of the diseases studied. Finally, we briefly describe the methods of the evaluation of the compounds in each biological model and the main achievements. The potential of the consortium to screen numerous compounds and identify new therapeutic agents is highlighted, demonstrating its significant contribution to addressing these infectious diseases.

Expanding chemistry through in vitro and in vivo biocatalysis

Living systems contain a vast network of metabolic reactions, providing a wealth of enzymes and cells as potential biocatalysts for chemical processes. The properties of protein and cell biocatalysts-high selectivity, the ability to control reaction sequence and operation in environmentally benign conditions-offer approaches to produce molecules at high efficiency while lowering the cost and environmental impact of industrial chemistry. Furthermore, biocatalysis offers the opportunity to generate chemical structures and functions that may be inaccessible to chemical synthesis. Here we consider developments in enzymes, biosynthetic pathways and cellular engineering that enable their use in catalysis for new chemistry and beyond.

Updating and profiling the natural product-likeness of Latin American compound libraries

Compound databases of natural products play a crucial role in drug discovery and development projects and have implications in other areas, such as food chemical research, ecology and metabolomics. Recently, we put together the first version of the Latin American Natural Product database (LANaPDB) as a collective effort of researchers from six countries to ensemble a public and representative library of natural products in a geographical region with a large biodiversity. The present work aims to conduct a comparative and extensive profiling of the natural product-likeness of an updated version of LANaPDB and the individual ten compound databases that form part of LANaPDB. The natural product-likeness profile of the Latin American compound databases is contrasted with the profile of other major natural product databases in the public domain and a set of small-molecule drugs approved for clinical use. As part of the extensive characterization, we employed several chemoinformatics metrics of natural product likeness. The results of this study will capture the attention of the global community engaged in natural product databases, not only in Latin America but across the world.

PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

Structure and Synthesis of Vindolicine and Derivatives

This article describes the reaction of vindoline with formaldehyde and trimethyl orthoformate to prepare vindolicine, tris-vindolicinyl methane and higher molecular weight homologues. The synthesis of 10-formyl vindoline as an intermediate allowed further exploration of its chemistry, in particular the reaction with acetone which yielded a symmetrical dimer, which was further reacted with vindoline to give molecules containing three and four vindoline units. These molecules were characterized by NMR and for some of them (vindolicine, 10-formyl vindoline, 10-(1'-(but-1'-en-3'-one))-vindoline) by X-ray crystallography. Depending on the substitution and on the absence of axes of symmetry, the NMR spectra displayed non-equivalent spin systems for the vindoline moieties. The dimer formed from the double condensation of 10-formyl vindoline with acetone showed cytotoxic activity in the micromolar range.

Progress in Lewis-Acid-Templated Diels-Alder Reactions

The synthesis of natural products with complicated architectures often requires the use of segments with functional groups that can be structurally transformed with the desired stereogenic centers. Bicyclic 𝛾-lactones have great potential as a suitable segment for natural product synthesis. However, the stereoselective construction of such functionalized bicyclic 𝛾-lactones is not as straightforward as one might expect. The template-mediated Diels-Alder reaction is one of the most powerful and versatile methods for providing bicyclic 𝛾-lactones with high regioselectivity and stereoselectivity. In this reaction, the diene is linked to the dienophile by a temporary tether, allowing the reaction to proceed efficiently, yielding a product that can be used for natural product synthesis. This review describes some important instances of the template-mediated Diels-Alder reaction and its application to the synthesis of biologically active compounds.

Structural insights into the diverse prenylating capabilities of DMATS prenyltransferases

Covering: 2009 up to August 2023Prenyltransferases (PTs) are involved in the primary and the secondary metabolism of plants, bacteria, and fungi, and they are key enzymes in the biosynthesis of many clinically relevant natural products (NPs). The continued biochemical and structural characterization of the soluble dimethylallyl tryptophan synthase (DMATS) PTs over the past two decades have revealed the significant promise that these enzymes hold as biocatalysts for the chemoenzymatic synthesis of novel drug leads. This is a comprehensive review of DMATSs describing the structure-function relationships that have shaped the mechanistic underpinnings of these enzymes, as well as the application of this knowledge to the engineering of DMATSs. We summarize the key findings and lessons learned from these studies over the past 14 years (2009-2023). In addition, we identify current gaps in our understanding of these fascinating enzymes.

Natural Products Dereplication: Databases and Analytical Methods

The development of efficient methods for dereplication has been critical in the re-emergence of the research in natural products as a source of drug leads. Current dereplication workflows rapidly identify already known bioactive secondary metabolites in the early stages of any drug discovery screening campaign based on natural extracts or enriched fractions. Two main factors have driven the evolution of natural products dereplication over the last decades. First, the availability of both commercial and public large databases of natural products containing the key annotations against which the biological and chemical data derived from the studied sample are searched for. Second, the considerable improvement achieved in analytical technologies (including instrumentation and software tools) employed to obtain robust and precise chemical information (particularly spectroscopic signatures) on the compounds present in the bioactive natural product samples. This chapter describes the main methods of dereplication, which rely on the combined use of large natural product databases and spectral libraries, alongside the information obtained from chromatographic, UV-Vis, MS, and NMR spectroscopic analyses of the samples of interest.

Juniperus oxycedrus L. ssp. Essential Oil Microneedles: A Promising Antimicrobial and Wound Healing Activity

The use of essential oil (EO) in treating infected wounds is still challenging. A lot of effort has been made to make such an application more convenient. Recently, microneedles (MNDs) have been considered as a smart dermal delivery system to overcome the poor absorption and distribution, low bioavailability, and skin penetration of some drugs. The aim of our study is to evaluate the wound healing activity of juniper-EO-loaded MNDs (EO MNDs) against wounds with bacterial and fungal infection. The Polyvinylpyrrolidone (PVP) MNDs were prepared using the gel-filled mold technique and loaded with juniper EO. In vivo models were created and wounds on rats were infected with two clinically isolated bacterial strains Pseudomonas aeruginosa and Staphylococcus aureus. Furthermore, Candida albicans was used to mimic fungal infection and juniper EO MNDs were tested. The obtained results showed an improvement in wound healing which started from the third day after application of the juniper EO MNDs, and at the sixth day post-infection, the treated wounds were significantly smaller than untreated wounds. A complete healing was shown by the 12th day after infection. Furthermore, our cytotoxicity results showed a cytotoxic effect of juniper EO MNDs on epithelial cells, which explained the faster wound healing in rats. Our study showed that juniper EO MNDs represent a novel strategy in EO delivery with minimal invasion. Juniper EO MNDs demonstrated significant antimicrobial activity against both the bacterial strains Pseudomonas aeruginosa and Staphylococcus aureus and against one fungal strain, Candida albicans. Finally, application of juniper EO MNDs exerted promising activity in the treatment and healing of wound infection.

Challenges in natural product-based drug discovery assisted with in silico-based methods

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

A chemoinformatic analysis on natural glycosides with respect to biological origin and structural class

Covering: up to 202216.19% of reported natural products (NPs) in the Dictionary of Natural Products (DNP) are glycosides. As one of the most important NPs' structural modifications, glycosylation can change the NPs' polarity, making the aglycones more amphipathic. However, until now, little is known about the general distribution profile of the natural glycosides in different biological sources or structural types. The reason, structural or species preferences of the natural glycosylation remain unclear. In this highlight, chemoinformatic methods were employed to analyze the natural glycosides from DNP, the most comprehensively annotated NP database. We found that the glycosylation ratios of NPs from plants, bacteria, animals and fungi decrease successively, which are 24.99%, 20.84%, 8.40% and 4.48%, respectively. Echinoderm-derived NPs (56.11%) are the most frequently glycosylated, while those produced by molluscs (1.55%), vertebrates (2.19%) and Rhodophyta (3.00%) are the opposite. Among the diverse structural types, a large proportion of steroids (45.19%), tannins (44.78%) and flavonoids (39.21%) are glycosides, yet aminoacids and peptides (5.16%), alkaloids (5.66%) are comparatively less glycosylated. Even within the same biological source or structural type, their glycosylation rates fluctuate drastically between sub- or cross-categories. The substitute patterns of flavonoid and terpenoid glycosides and the most frequently glycosylated scaffolds were identified. NPs with different glycosylation levels occupy different chemical spaces of physicochemical property and scaffold. These findings could help us to interpret the preference of NPs' glycosylation and investigate how NP glycosylation could aid NP-based drug discovery.

Toward α-1,3/4 fucosyltransferases targeted drug discovery: In silico uncovering of promising natural inhibitors of fucosyltransferase 6

Sialyl Lewis X (sLex ) antigen is a fucosylated cell-surface glycan that is normally involved in cell-cell interactions. The enhanced expression of sLex on cell surface glycans, which is attributed to the upregulation of fucosyltransferase 6 (FUT6), has been implicated in facilitating metastasis in human colorectal, lung, prostate, and oral cancers. The role that the upregulated FUT6 plays in the progression of tumor to malignancy, with reduced survival rates, makes it a potential target for anticancer drugs. Unfortunately, the lack of experimental structures for FUT6 has hampered the design and development of its inhibitors. In this study, we used in silico techniques to identify potential FUT6 inhibitors. We first modeled the three-dimensional structure of human FUT6 using AlphaFold. Then, we screened the natural compound libraries from the COCONUT database to sort out potential natural products (NPs) with best affinity toward the FUT6 model. As a result of these simulations, we identified three NPs for which we predicted binding affinities and interaction patterns quite similar to those we calculated for two experimentally tested FUT6 inhibitors, that is, fucose mimetic-1 and a GDP-triazole derived compound. We also performed molecular dynamics (MD) simulations for the FUT6 complexes with identified NPs, to investigate their stability. Analysis of the MD simulations showed that the identified NPs establish stable contacts with FUT6 under dynamics conditions. On these grounds, the three screened compounds appear as promising natural alternatives to experimentally tested FUT6 synthetic inhibitors, with expected comparable binding affinity. This envisages good prospects for future experimental validation toward FUT6 inhibition.

Bioactive Natural Product Discovery via Deuterium Adduct Bioactivity Screening

The discovery of bioactive natural products lies at the forefront of human medicine. The continued discovery of these molecules is imperative in the fight against infection and disease. While natural products have historically dominated the drug market, discovery in recent years has slowed significantly, partly due to limitations in current discovery methodologies. This work demonstrates a new workflow, deuterium adduct bioactivity screening (DABS), which pairs untargeted isotope labeling with whole cell binding assays for bioactive natural product discovery. DABS was validated and led to the discovery of a new isoprenyl guanidine alkaloid, zillamycin, which showed anti-cancer and anti-microbial activities. DABS thus represents a new workflow to accelerate discovery of natural products with a wide range of bioactive potentials.

"Magic Chloro": Profound Effects of the Chlorine Atom in Drug Discovery

Chlorine is one of the most common atoms present in small-molecule drugs beyond carbon, hydrogen, nitrogen, and oxygen. There are currently more than 250 FDA-approved chlorine-containing drugs, yet the beneficial effect of the chloro substituent has not yet been reviewed. The seemingly simple substitution of a hydrogen atom (R = H) with a chlorine atom (R = Cl) can result in remarkable improvements in potency of up to 100,000-fold and can lead to profound effects on pharmacokinetic parameters including clearance, half-life, and drug exposure in vivo. Following the literature terminology of the "magic methyl effect" in drugs, the term "magic chloro effect" has been coined herein. Although reports of 500-fold or 1000-fold potency improvements are often serendipitous discoveries that can be considered "magical" rather than planned, hypotheses made to explain the magic chloro effect can lead to lessons that accelerate the cycle of drug discovery.

Chemoinformatics approach to design and develop vanillin analogs as COX-1 inhibitor

Background: Coronary Heart Disease (CHD), commonly known as the silent killer, impacted the severity of COVID-19 patients during the pandemic era. Thrombosis or blood clots create the buildup of plaque on the coronary artery walls of the heart, which leads to coronary heart disease. Cyclooxygenase 1 (COX-1) is involved in the production of prostacyclin by systemic arteries; hence, inhibiting the COX-1 enzyme can prevent platelet reactivity mediated by prostacyclin. To obtain good health and well-being, the research of discovery of new drugs for anti-thrombotic still continue. Objective: This study aims to predict the potential of 17 compounds owned by the vanillin analog to COX-1 receptor using in silico. Methods: This research employed a molecular docking analysis using Toshiba hardware and AutoDock Tools version 1.5.7, ChemDraw Professional 16.0, Discovery Studio, UCSF Chimera software, SWISSADME and pKCSM, a native ligand from COX- 1 (PDB ID: 1CQE) was validated. Results: The validation result indicated that the RMSD was <2 Å. The 4-formyl-2-methoxyphenyl benzoate compound had the lowest binding energy in COX-1 inhibition with a value of -7.70 Å. All vanillin derivatives show good intestinal absorption, and the predicted toxicity indicated that they were non-hepatotoxic. All these compounds have the potential to be effective antithrombotic treatments when consumed orally. Conclusion: In comparison to other vanillin derivative compounds, 4-formyl-2-methoxyphenyl benzoate has the lowest binding energy value; hence, this analog can continue to be synthesized and its potential as an antithrombotic agent might be confirmed by in vivo studies.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.

Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)

The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.

Chemical Multiverse: An Expanded View of Chemical Space

Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community's attention. The large- and ultra-large chemical spaces are associated with the significant increase in the number of compounds that can potentially be made and exist and the increasing number of experimental and calculated descriptors, that are emerging that encode the molecular structure and/or property aspects of the molecules. Due to the importance and continued evolution of compound libraries, herein, we discuss definitions proposed in the literature for chemical space and emphasize the convenience, discussed in the literature to use complementary descriptors to obtain a comprehensive view of the chemical space of compound data sets. In this regard, we introduce the term chemical multiverse to refer to the comprehensive analysis of compound data sets through several chemical spaces, each defined by a different set of chemical representations. The chemical multiverse is contrasted with a related idea: consensus chemical space.

Computational Biology of BRCA2 in Male Breast Cancer, through Prediction of Probable nsSNPs, and Hit Identification

Male breast cancer (MBC) is a relatively rare disease, but emerging data recommend the development of novel therapeutics considering its alarming threats. Compared to female breast cancer (FBC), MBC is reportedly associated with inferior outcomes (poor survival) owing to their late diagnosis and lack of adequate treatment. Treatment typically correlates with FBC, involving surgical removal of the breast tissue along with chemo/hormonal/radiation therapy, the tamoxifen being a standard adjuvant. Considering the distinct immunophenotypic (implying different pathogenesis and progression) differences from FBC, the identification of diagnostics, prognostics, and therapeutics for MBC is highly desirable. In this context, we have analyzed the most deleterious nsSNPs of BRCA2, a human tumor suppressor gene constituting the potential biomarker for tumors including MBC, to predict the structural changes associated with the mutants hampering the normal protein-protein and protein-ligand interactions, resulting in MBC progression. Among 27 nsSNPs confined to 21 rsIDs pertaining to MBC, the 19 nsSNPs constituting 14 rsIDs have been predicted as highly deleterious. We believe that these nsSNPs could serve as potential biomarkers for diagnostic and prognostic purposes and could be the pivotal target for MBC drug discovery. Subsequently, the study highlights the exploration of the key nsSNPs (of BRCA2 associated with the MBC) and its applications toward the identification of therapeutic hit TIP006136 following the homology modeling, virtual screening of 5284 phytochemicals retrieved from the TIPdb (a database of phytochemicals from indigenous plants in Taiwan) database, molecular docking (against native and mutant BRCA2), dynamic simulations (against native and mutant BRCA2), density functional theory (DFT), and molecular electrostatic potential. To the best of our knowledge, this is the first report to use diverse computational modules to investigate the important nsSNPs of BRCA2 related to MBC, implying that TIP006136 could be a potential hit and must be studied further (in vitro and in vivo) to establish its anticancer property and efficacy against MBC.

Ring systems in natural products: structural diversity, physicochemical properties, and coverage by synthetic compounds

Covering: up to 2021The structural core of most small-molecule drugs is formed by a ring system, often derived from natural products. However, despite the importance of natural product ring systems in bioactive small molecules, there is still a lack of a comprehensive overview and understanding of natural product ring systems and how their full potential can be harnessed in drug discovery and related fields. Herein, we present a comprehensive cheminformatic analysis of the structural and physicochemical properties of 38 662 natural product ring systems, and the coverage of natural product ring systems by readily purchasable, synthetic compounds that are commonly explored in virtual screening and high-throughput screening. The analysis stands out by the use of comprehensive, curated data sets, the careful consideration of stereochemical information, and a robust analysis of the 3D molecular shape and electrostatic properties of ring systems. Among the key findings of this study are the facts that only about 2% of the ring systems observed in NPs are present in approved drugs but that approximately one in two NP ring systems are represented by ring systems with identical or related 3D shape and electrostatic properties in compounds that are typically used in (high-throughput) screening.

Marine-derived microbes and molecules for drug discovery

Increasing attention has been paid to marine-derived biomolecules as sources of therapeutics for autoimmune diseases. Nagasaki Prefecture has many islands and is surrounded by seas, straits, gulfs, bays, and coves, giving it the second longest coastline in Japan after Hokkaido. We have collected more than 20,000 marine microbes and have been preparing an original marine microbial extract library, which contains small and mid-size biomolecules that may penetrate cell membranes and interfere with the intracellular protein–protein interaction involved in the development of autoinflammatory diseases such as familial Mediterranean fever. In addition, we have been developing an indoor shark farming system to prepare shark nanobodies that could be developed as potential therapeutic agents for autoimmune diseases. Sharks produce heavy-chain antibodies, called immunoglobulin new antigen receptors (IgNARs), consisting of one variable domain (V_NAR) and five constant domains (C_NAR); of these, V_NAR can recognize a variety of foreign antigens. A V_NAR single domain fragment, called a nanobody, can be expressed in Escherichia coli and has the properties of an ideal therapeutic candidate for autoimmune diseases. Shark nanobodies contain complementarity-determining regions that are formed through the somatic rearrangement of variable, diversity, and joining segments, with the segment end trimming and the N- and P-additions, as found in the variable domains of mammalian antibodies. The affinity and diversity of shark nanobodies are thus expected to be comparable to those of mammalian antibodies. In addition, shark nanobodies are physically robust and can be prepared inexpensively; as such, they may lead to the development of highly specific, stable, effective, and inexpensive biotherapeutics in the future. In this review, we first summarize the history of the development of conventional small molecule drugs and monoclonal antibody therapeutics for autoimmune diseases, and then introduce our drug discovery system at Nagasaki University, including the preparation of an original marine microbial extract library and the development of shark nanobodies.

Evaluation of the Antimicrobial and Antivirulent Potential of Essential Oils Isolated from Juniperus oxycedrus L. ssp. macrocarpa Aerial Parts

As a consequence of the worsening situation with multidrug-resistant (MDR) pathogens and a disparity in the commercialization of novel antimicrobial agents, scientists have been prompted to seek out new compounds with antimicrobial activity from a wide range of sources, including medicinal plants. In the present study, the antibacterial, antifungal, anti-virulence, and resistance-modulating properties of the essential oil from the Sardinian endemic Juniperus oxycedrus L. ssp. macrocarpa aerial parts were evaluated. The GC/MS analysis showed that the main compounds in the oil were α-pinene (56.63 ± 0.24%), limonene (14.66 ± 0.11%), and β-pinene (13.42 ± 0.09%). The essential oil showed potent antibacterial activity against Gram-positive bacteria (0.25-2 v/v%) and Salmonella spp. (4 v/v%). The strongest fungicidal activity was recorded against Candida auris sessile cells (median FICI was 0.088) but not against C. albicans biofilms (median FICI was 1). The oil showed potent efflux pump inhibitory properties in the case of Staphylococcus aureus and Escherichia coli. The therapeutic potential of Juniperus may be promising for future more extensive research and in vivo tests to develop new drugs against antibiotic and antifungal resistance.

A Brief Review of Machine Learning-Based Bioactive Compound Research

Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces, such as those in natural products (NPs). This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds. It also provides an overview of recent research trends in machine learning-based prediction and the evaluation of bioactive compounds by listing real-world examples along with various input data. In addition, several ML-based approaches to identify specific bioactive compounds for cardiovascular and metabolic diseases are described. Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related research.

Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier

CYP3A4 is the main human enzyme responsible for phase I metabolism of dietary compounds, prescribed drugs and xenobiotics, steroid hormones, and bile acids. The inhibition of CYP3A4 activity might impair physiological mechanisms, including the endocrine system and response to drug admission. Here, we aimed to discover new CYP3A4 inhibitors from food and dietary supplements. A deep-learning model was built that classifies compounds as either an inhibitor or noninhibitor, with a high specificity of 0.997. We used this classifier to virtually screen ∼60,000 dietary compounds. Of the 115 identified potential inhibitors, only 31 were previously suggested. Many herbals, as predicted here, might cause impaired metabolism of drugs, and endogenous hormones and bile acids. Additionally, by applying Lipinski's rules of five, 17 compounds were also classified as potential intestine local inhibitors. New CYP3A4 inhibitors predicted by the model, bilobetin and picropodophyllin, were assayed in vitro.

Chemical Evolution of Natural Product Structure

Natural products are the result of Nature’s exploration of biologically relevant chemical space through evolution and an invaluable source of bioactive small molecules for chemical biology and medicinal chemistry. Novel concepts for the discovery of new bioactive compound classes based on natural product structure may enable exploration of wider biologically relevant chemical space. The pseudo-natural product concept merges the relevance of natural product structure with efficient exploration of chemical space by means of fragment-based compound development to inspire the discovery of new bioactive chemical matter through de novo combination of natural product fragments in unprecedented arrangements. The novel scaffolds retain the biological relevance of natural products but are not obtainable through known biosynthetic pathways which can lead to new chemotypes that may have unexpected or unprecedented bioactivities. Herein, we cover the workflow of pseudo-natural product design and development, highlight recent examples, and discuss a cheminformatic analysis in which a significant portion of biologically active synthetic compounds were found to be pseudo-natural products. We compare the concept to natural evolution and discuss pseudo-natural products as the human-made equivalent, i.e. the chemical evolution of natural product structure.

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.

Structure and Biosynthesis of Myxofacyclines: Unique Myxobacterial Polyketides Featuring Varing and Rare Heterocycles[] *

A metabolome-guided screening approach in the novel myxobacterium Corallococcus sp. MCy9072 resulted in the isolation of the unprecedented natural product myxofacycline A, which features a rare isoxazole substructure. Identification and genomic investigation of additional producers alongside targeted gene inactivation experiments and heterologous expression of the corresponding biosynthetic gene cluster in the host Myxococcus xanthus DK1622 confirmed a noncanonical megaenzyme complex as the biosynthetic origin of myxofacycline A. Induced expression of the respective genes led to significantly increased production titers enabling the identification of six further members of the myxofacycline natural product family. Whereas myxofacyclines A-D display an isoxazole substructure, intriguingly myxofacyclines E and F were found to contain 4-pyrimidinole, a heterocycle unprecedented in natural products. Lastly, myxofacycline G features another rare 1,2-dihydropyrol-3-one moiety. In addition to a full structure elucidation, we report the underlying biosynthetic machinery and present a rationale for the formation of all myxofacyclines. Unexpectedly, an extraordinary polyketide synthase-nonribosomal peptide synthetase hybrid was found to produce all three types of heterocycle in these natural products.

NP Navigator: A New Look at the Natural Product Chemical Space

Natural products (NPs), being evolutionary selected over millions of years to bind to biological macromolecules, remained an important source of inspiration for medicinal chemists even after the advent of efficient drug discovery technologies such as combinatorial chemistry and high-throughput screening. Thus, there is a strong demand for efficient and user-friendly computational tools that allow to analyze large libraries of NPs. In this context, we introduce NP Navigator - a freely available intuitive online tool for visualization and navigation through the chemical space of NPs and NP-like molecules. It is based on the hierarchical ensemble of generative topographic maps, featuring NPs from the COlleCtion of Open NatUral producTs (COCONUT), bioactive compounds from ChEMBL and commercially available molecules from ZINC. NP Navigator allows to efficiently analyze different aspects of NPs - chemotype distribution, physicochemical properties, biological activity and commercial availability of NPs. The latter concerns not only purchasable NPs but also their close analogs that can be considered as synthetic mimetics of NPs or pseudo-NPs.

Protein druggability assessment for natural products using in silico simulation: A case study with estrogen receptor and the flavonoid genistein

Traditional herbal medicine (THM) comprises a vast number of natural compounds. Most of them are metabolized into different structures after administration, which makes the clarification of THM's mode of action more complicated. To evaluate the biological activities of those components and metabolites, in silico simulation technology is helpful. We focused on mixed-solvent molecular dynamics (MD) simulation for druggability assessment of natural products. Mixed-solvent MD is an in silico simulation method for the exploration of ligand-binding sites on target proteins, which uses water and an organic molecule mixture. The selection of organic small molecules is an important factor for predicting the characteristics of natural products. In this study, we used the known crystal structure of estrogen receptors with genistein as a test case and explored fragments reflecting the characteristics of natural products. We found that structures with a 4-pyrone structure are more often included in the natural products database compared with the DrugBank database, and we selectively detected the known-binding sites of estrogen receptor α and β. The results indicate that the 4-pyrone structure might be promising for predicting the protein druggability of flavonoids. Additionally, mixed-solvent MD simulation discriminates the selectivity of genistein between estrogen receptor β and α, indicating that the simulation can be evaluated using indices that differ from those of traditional ligand docking. Although this approach is still in its early stages, it has the potential to provide valuable information for understanding the diverse biological activities of natural products.

SANCDB: an update on South African natural compounds and their readily available analogs

BACKGROUND

South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/ ) is the sole and a fully referenced database of natural chemical compounds of South African biodiversity. It is freely available, and since its inception in 2015, the database has become an important resource to several studies. Its content has been: used as training data for machine learning models; incorporated to larger databases; and utilized in drug discovery studies for hit identifications.

DESCRIPTION

Here, we report the updated version of SANCDB. The new version includes 412 additional compounds that have been reported since 2015, giving a total of 1012 compounds in the database. Further, although natural products (NPs) are an important source of unique scaffolds, they have a major drawback due to their complex structure resulting in low synthetic feasibility in the laboratory. With this in mind, SANCDB is, now, updated to provide direct links to commercially available analogs from two major chemical databases namely Mcule and MolPort. To our knowledge, this feature is not available in other NP databases. Additionally, for easier access to information by users, the database and website interface were updated. The compounds are now downloadable in many different chemical formats.

CONCLUSIONS

The drug discovery process relies heavily on NPs due to their unique chemical organization. This has inspired the establishment of numerous NP chemical databases. With the emergence of newer chemoinformatic technologies, existing chemical databases require constant updates to facilitate information accessibility and integration by users. Besides increasing the NPs compound content, the updated SANCDB allows users to access the individual compounds (if available) or their analogs from commercial databases seamlessly.

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.

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

The question of molecular similarity is core in cheminformatics and is usually assessed via a pairwise comparison based on vectors of properties or molecular fingerprints. We recently exploited variational autoencoders to embed 6M molecules in a chemical space, such that their (Euclidean) distance within the latent space so formed could be assessed within the framework of the entire molecular set. However, the standard objective function used did not seek to manipulate the latent space so as to cluster the molecules based on any perceived similarity. Using a set of some 160,000 molecules of biological relevance, we here bring together three modern elements of deep learning to create a novel and disentangled latent space, viz transformers, contrastive learning, and an embedded autoencoder. The effective dimensionality of the latent space was varied such that clear separation of individual types of molecules could be observed within individual dimensions of the latent space. The capacity of the network was such that many dimensions were not populated at all. As before, we assessed the utility of the representation by comparing clozapine with its near neighbors, and we also did the same for various antibiotics related to flucloxacillin. Transformers, especially when as here coupled with contrastive learning, effectively provide one-shot learning and lead to a successful and disentangled representation of molecular latent spaces that at once uses the entire training set in their construction while allowing "similar" molecules to cluster together in an effective and interpretable way.

Modeling Pharmacokinetic Natural Product-Drug Interactions for Decision-Making: A NaPDI Center Recommended Approach

The popularity of botanical and other purported medicinal natural products (NPs) continues to grow, especially among patients with chronic illnesses and patients managed on complex prescription drug regimens. With few exceptions, the risk of a given NP to precipitate a clinically significant pharmacokinetic NP-drug interaction (NPDI) remains understudied or unknown. Application of static or dynamic mathematical models to predict and/or simulate NPDIs can provide critical information about the potential clinical significance of these complex interactions. However, methods used to conduct such predictions or simulations are highly variable. Additionally, published reports using mathematical models to interrogate NPDIs are not always sufficiently detailed to ensure reproducibility. Consequently, guidelines are needed to inform the conduct and reporting of these modeling efforts. This recommended approach from the Center of Excellence for Natural Product Drug Interaction Research describes a systematic method for using mathematical models to interpret the interaction risk of NPs as precipitants of potential clinically significant pharmacokinetic NPDIs. A framework for developing and applying pharmacokinetic NPDI models is presented with the aim of promoting accuracy, reproducibility, and generalizability in the literature. SIGNIFICANCE STATEMENT: Many natural products (NPs) contain phytoconstituents that can increase or decrease systemic or tissue exposure to, and potentially the efficacy of, a pharmaceutical drug; however, no regulatory agency guidelines exist to assist in predicting the risk of these complex interactions. This recommended approach from a multi-institutional consortium designated by National Institutes of Health as the Center of Excellence for Natural Product Drug Interaction Research provides a framework for modeling pharmacokinetic NP-drug interactions.

Machine Learning Methods to Predict the Terrestrial and Marine Origin of Natural Products

In recent years there has been a growing interest in studying the differences between the chemical and biological space represented by natural products (NPs) of terrestrial and marine origin. In order to learn more about these two chemical spaces, marine natural products (MNPs) and terrestrial natural products (TNPs), a machine learning (ML) approach was developed in the current work to predict three classes, MNPs, TNPs and a third class of NPs that appear in both the terrestrial and marine environments. In total 22,398 NPs were retrieved from the Reaxys® database, from those 10,790 molecules are recorded as MNPs, 10,857 as TNPs, and 761 NPs appear registered as both MNPs and TNPs. Several ML algorithms such as Random Forest, Support Vector Machines, and deep learning Multilayer Perceptron networks have been benchmarked. The best performance was achieved with a consensus classification model, which predicted the external test set with an overall predictive accuracy up to 81 %. As far as we know this approach has never been intended and therefore allow to be used to better understand the chemical space defined by MNPs, TNPs or both, but also in virtual screening to define the applicability domain of QSAR models of MNPs and TNPs.

StreptomeDB 3.0: an updated compendium of streptomycetes natural products

Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

Structural Similarities between Some Common Fluorophores Used in Biology, Marketed Drugs, Endogenous Metabolites, and Natural Products

It is known that at least some fluorophores can act as 'surrogate' substrates for solute carriers (SLCs) involved in pharmaceutical drug uptake, and this promiscuity is taken to reflect at least a certain structural similarity. As part of a comprehensive study seeking the 'natural' substrates of 'orphan' transporters that also serve to take up pharmaceutical drugs into cells, we have noted that many drugs bear structural similarities to natural products. A cursory inspection of common fluorophores indicates that they too are surprisingly 'drug-like', and they also enter at least some cells. Some are also known to be substrates of efflux transporters. Consequently, we sought to assess the structural similarity of common fluorophores to marketed drugs, endogenous mammalian metabolites, and natural products. We used a set of some 150 fluorophores along with standard fingerprinting methods and the Tanimoto similarity metric. Results: The great majority of fluorophores tested exhibited significant similarity (Tanimoto similarity > 0.75) to at least one drug, as judged via descriptor properties (especially their aromaticity, for identifiable reasons that we explain), by molecular fingerprints, by visual inspection, and via the "quantitative estimate of drug likeness" technique. It is concluded that this set of fluorophores does overlap with a significant part of both the drug space and natural products space. Consequently, fluorophores do indeed offer a much wider opportunity than had possibly been realised to be used as surrogate uptake molecules in the competitive or trans-stimulation assay of membrane transporter activities.

Fragment Library of Natural Products and Compound Databases for Drug Discovery

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.

Natural Products Impacting DNA Methyltransferases and Histone Deacetylases

Epigenetics refers to heritable changes in gene expression and chromatin structure without change in a DNA sequence. Several epigenetic modifications and respective regulators have been reported. These include DNA methylation, chromatin remodeling, histone post-translational modifications, and non-coding RNAs. Emerging evidence has revealed that epigenetic dysregulations are involved in a wide range of diseases including cancers. Therefore, the reversible nature of epigenetic modifications concerning activation or inhibition of enzymes involved could be promising targets and useful tools for the elucidation of cellular and biological phenomena. In this review, emphasis is laid on natural products that inhibit DNA methyltransferases (DNMTs) and histone deacetylases (HDACs) making them promising candidates for the development of lead structures for anticancer-drugs targeting epigenetic modifications. However, most of the natural products targeting HDAC and/or DNMT lack isoform selectivity, which is important for determining their potential use as therapeutic agents. Nevertheless, the structures presented in this review offer the well-founded basis that screening and chemical modifications of natural products will in future provide not only leads to the identification of more specific inhibitors with fewer side effects, but also important features for the elucidation of HDAC and DNMT function with respect to cancer treatment.

Case studies on computer-based identification of natural products as lead molecules

The development and application of computer-aided drug design/discovery (CADD) techniques (such as structured-base virtual screening, ligand-based virtual screening and neural networks approaches) are on the point of disintermediation in the pharmaceutical drug discovery processes. The application of these CADD methods are standing out positively as compared to other experimental approaches in the identification of hits. In order to venture into new chemical spaces, research groups are exploring natural products (NPs) for the search and identification of new hits and more efficient leads as well as the repurposing of approved NPs. The chemical space of NPs is continuously increasing as a result of millions of years of evolution of species and these data are mainly stored in the form of databases providing access to scientists around the world to conduct studies using them. Investigation of these NP databases with the help of CADD methodologies in combination with experimental validation techniques is essential to identify and propose new drug molecules. In this chapter, we highlight the importance of the chemical diversity of NPs as a source for potential drugs as well as some of the success stories of NP-derived candidates against important therapeutic targets. The focus is on studies that applied a healthy dose of the emerging CADD methodologies (structure-based, ligand-based and machine learning).

Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019

This review is an updated and expanded version of the five prior reviews that were published in this journal in 1997, 2003, 2007, 2012, and 2016. For all approved therapeutic agents, the time frame has been extended to cover the almost 39 years from the first of January 1981 to the 30th of September 2019 for all diseases worldwide and from ∼1946 (earliest so far identified) to the 30th of September 2019 for all approved antitumor drugs worldwide. As in earlier reviews, only the first approval of any drug is counted, irrespective of how many "biosimilars" or added approvals were subsequently identified. As in the 2012 and 2016 reviews, we have continued to utilize our secondary subdivision of a "natural product mimic", or "NM", to join the original primary divisions, and the designation "natural product botanical", or "NB", to cover those botanical "defined mixtures" now recognized as drug entities by the FDA (and similar organizations). From the data presented in this review, the utilization of natural products and/or synthetic variations using their novel structures, in order to discover and develop the final drug entity, is still alive and well. For example, in the area of cancer, over the time frame from 1946 to 1980, of the 75 small molecules, 40, or 53.3%, are N or ND. In the 1981 to date time frame the equivalent figures for the N* compounds of the 185 small molecules are 62, or 33.5%, though to these can be added the 58 S* and S*/NMs, bringing the figure to 64.9%. In other areas, the influence of natural product structures is quite marked with, as expected from prior information, the anti-infective area being dependent on natural products and their structures, though as can be seen in the review there are still disease areas (shown in Table 2) for which there are no drugs derived from natural products. Although combinatorial chemistry techniques have succeeded as methods of optimizing structures and have been used very successfully in the optimization of many recently approved agents, we are still able to identify only two de novo combinatorial compounds (one of which is a little speculative) approved as drugs in this 39-year time frame, though there is also one drug that was developed using the "fragment-binding methodology" and approved in 2012. We have also added a discussion of candidate drug entities currently in clinical trials as "warheads" and some very interesting preliminary reports on sources of novel antibiotics from Nature due to the absolute requirement for new agents to combat plasmid-borne resistance genes now in the general populace. We continue to draw the attention of readers to the recognition that a significant number of natural product drugs/leads are actually produced by microbes and/or microbial interactions with the "host from whence it was isolated"; thus we consider that this area of natural product research should be expanded significantly.

Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach

Chinese herbal medicine (CHM) addresses complex diseases through polypharmacological interactions. However, systematic studies of herbal medicine pharmacology remain challenging due to the complexity of CHM ingredients and their interactions with various targets. In this study, we aim to address this challenge with computational approaches. We investigated the herb-target-disease associations of 197 commonly prescribed CHMs using the similarity ensemble approach and DisGeNET database. We demonstrated that this method can be applied to associate herbs with their putative targets. In the case study of three well-known herbs, Radix Glycyrrhizae, Flos Lonicerae, and Rhizoma Coptidis, approximately 70% of the predicted targets were supported by scientific literature. By linking 406 targets to 2439 annotated diseases, we further analyzed the pharmacological functions of 197 herbs. Finally, we proposed a strategy of target-oriented herbal formula design and illustrated the target profiles for four common chronic diseases, namely, Alzheimer's disease, depressive disorder, hypertensive disease, and non-insulin-dependent diabetes mellitus. This computational approach holds great potential in the target identification of herbs, understanding the molecular mechanisms of CHM, and designing novel herbal formulas.

Natural products against acute respiratory infections: Strategies and lessons learned

ETHNOPHARMACOLOGICAL RELEVANCE

A wide variety of traditional herbal remedies have been used throughout history for the treatment of symptoms related to acute respiratory infections (ARIs).

AIM OF THE REVIEW

The present work provides a timely overview of natural products affecting the most common pathogens involved in ARIs, in particular influenza viruses and rhinoviruses as well as bacteria involved in co-infections, their molecular targets, their role in drug discovery, and the current portfolio of available naturally derived anti-ARI drugs.

MATERIALS AND METHODS

Literature of the last ten years was evaluated for natural products active against influenza viruses and rhinoviruses. The collected bioactive agents were further investigated for reported activities against ARI-relevant bacteria, and analysed for the chemical space they cover in relation to currently known natural products and approved drugs.

RESULTS

An overview of (i) natural compounds active in target-based and/or phenotypic assays relevant to ARIs, (ii) extracts, and (iii) in vivo data are provided, offering not only a starting point for further in-depth phytochemical and antimicrobial studies, but also revealing insights into the most relevant anti-ARI scaffolds and compound classes. Investigations of the chemical space of bioactive natural products based on principal component analysis show that many of these compounds are drug-like. However, some bioactive natural products are substantially larger and have more polar groups than most approved drugs. A workflow with various strategies for the discovery of novel antiviral agents is suggested, thereby evaluating the merit of in silico techniques, the use of complementary assays, and the relevance of ethnopharmacological knowledge on the exploration of the therapeutic potential of natural products.

CONCLUSIONS

The longstanding ethnopharmacological tradition of natural remedies against ARIs highlights their therapeutic impact and remains a highly valuable selection criterion for natural materials to be investigated in the search for novel anti-ARI acting concepts. We observe a tendency towards assaying for broad-spectrum antivirals and antibacterials mainly discovered in interdisciplinary academic settings, and ascertain a clear demand for more translational studies to strengthen efforts for the development of effective and safe therapeutic agents for patients suffering from ARIs.

Expanding the chemical diversity through microorganisms co-culture: Current status and outlook

Natural products (NPs) are considered as a cornerstone for the generation of bioactive leads in drug discovery programs. However, one of the major limitations of NP drug discovery program is "rediscovery" of known compounds, thereby hindering the rate of drug discovery efficiency. Therefore, in recent years, to overcome these limitations, a great deal of attention has been drawn towards understanding the role of microorganisms' co-culture in inducing novel chemical entities. Such induction could be related to activation of genes which might be silent or expressed at very low levels (below detection limit) in pure-strain cultures under normal laboratory conditions. In this review, chemical diversity of compounds isolated from microbial co-cultures, is discussed. For this purpose, chemodiversity has been represented as a chemical-structure network based on the "Tanimoto Structural Similarity Index". This highlights the huge structural diversity induced by microbial co-culture. In addition, the current trends in microbial co-culture research are highlighted. Finally, the current challenges (1 - induction monitoring, 2 - reproducibility, 3 - growth time effect and 4 - up-scaling for isolation purposes) are discussed. The information in this review will support researchers to design microbial co-culture strategies for future research efforts. In addition, guidelines for co-culture induction reporting are also provided to strengthen future reporting in this NP field.