Scaffold diversity of natural products: inspiration for combinatorial library design

Pseudo-Natural Products: Expanding chemical and biological space by surpassing natural constraints

This review explores the recent advancements in the design and synthesis of pseudo-natural products (pseudo-NPs) by employing innovative principles and strategies, heralding a transformative era in chemistry and biology. Pseudo-NPs, produced through in silico fragmentation and the de novo recombination of natural product fragments, reveal compounds endowed with distinct biological activities. Their advantage lies in transcending natural product structures, fostering diverse possibilities. Research in this area over the past decade has yielded unconventional combinations of natural product fragments, leading to the identification of novel compounds possessing unique scaffolds and biological significance, thereby contributing to the discovery of new therapeutics. The pseudo-NPs exert potent biological effects through various signaling pathways. In chemical biology and medicinal chemistry, designing pseudo-NPs is an important strategy, harnessing molecular hybridization and bioinspired synthesis to generate diverse compounds with remarkable biological activities, underscoring their immense potential in drug discovery and development.

Natural and engineered xylosyl products from microbial source

Glycosylation is a prevalent post-modification found in natural products and has a significant impact on the structural diversity and activity variation of natural products. Glucosylation is the most common type of glycosylation, whereas xylosylation is relatively rare. Despite their unique chemical structures and beneficial activities, xylosylated natural products from microorganisms have received little attention. This review provides, for the first time, a comprehensive summary of 126 microbial-derived xylosylated natural products, including xylosyl-cyathane diterpenes, xylosylated triterpenes, xylosyl aromatic compounds, and others. Among these compounds, xylosyl-cyathane diterpenes represent the highest number of derivatives, followed by xylosylated triterpenes. Xylosyl compounds from bacterial sources have less defined structural profiles compared to those from fungi. The characterization of xylosyltransferase EriJ from Basidiomycota extended the structural diversity of xylosyl cyathane diterpenes. This work provides a valuable reference for the research and use of xylosyltransferase for drug discovery and synthetic chemistry. Further work is needed to explore the potential applications of microbial derived xylosyl compounds and to develop novel xylosyl transferases. With the deepening of genomic sequencing of medicinal fungi, more biosynthesis of bioactive xylosyl compounds is expected to be elucidated in the future.

Innate functions of natural products: A promising path for the identification of novel therapeutics

In the course of evolution, living organisms have become well equipped with diverse natural products that serve important functions, including defence from biotic and abiotic stress, growth regulation, reproduction, metabolism, and epigenetic regulation. It seems to be the organism's ecological niche that influences the natural product's structural and functional diversity. Indeed, natural products constitute the nuts and bolts of molecular co-evolution and ecological relationships among different life forms. Since natural products in the form of specialized secondary metabolites exhibit biological functions via interactions with specific target proteins, they can provide a simultaneous glimpse of both new therapeutics and therapeutic targets in humans as well. In this review, we have discussed the innate role of natural products in the ecosystem and how this intrinsic role provides a futuristic opportunity to identify new drugs and therapeutic targets rapidly.

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.

Explaining Blood-Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

The blood-brain barrier (BBB) represents a major obstacle to delivering drugs to the central nervous system (CNS), resulting in the lack of effective treatment for many CNS diseases including brain cancer. To accelerate CNS drug development, computational prediction models could save the time and effort needed for experimental evaluation. Here, we studied BBB permeability focusing on active transport (influx and efflux) as well as passive diffusion using previously published and self-curated data sets. We created prediction models based on physicochemical properties, molecular substructures, or their combination to understand which mechanisms contribute to BBB permeability. Our results show that features that predicted passive diffusion over membranes overlap with features that explain endothelial permeation of approved CNS-active drugs. We also identified physical properties and molecular substructures that positively or negatively predicted BBB transport. These findings provide guidance toward identifying BBB-permeable compounds by optimally matching physicochemical and molecular properties to BBB transport mechanisms.

Rapid and Efficient Access to Novel Bio-Inspired 3-Dimensional Tricyclic SpiroLactams as Privileged Structures via Meyers’ Lactamization

The concept of privileged structure has been used as a fruitful approach for the discovery of novel biologically active molecules. A privileged structure is defined as a semi-rigid scaffold able to display substituents in multiple spatial directions and capable of providing potent and selective ligands for different biological targets through the modification of those substituents. On average, these backbones tend to exhibit improved drug-like properties and therefore represent attractive starting points for hit-to-lead optimization programs. This article promotes the rapid, reliable, and efficient synthesis of novel, highly 3-dimensional, and easily functionalized bio-inspired tricyclic spirolactams, as well as an analysis of their drug-like properties.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

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.

Total Syntheses and Cytotoxicity Evaluation of Coryaurone A and Representative Analogues

The first total synthesis of coryaurone A, which was originally obtained from Psoralea corylifolia L., was achieved via an efficient route with the longest linear sequence of six steps from the commercially available 6-hydroxy-2H-benzofuran-3-one in 37% overall yield. A series of representative analogues were synthesized from the same starting material in 4-7 steps with overall yields of 27-56%. The cytotoxicities of these compounds against the leukemia cell line HL-60 and the colon cancer cell line SW480 were determined. Among them, compounds 12, 14, 21, and 27 exhibited different levels of cytotoxic activity, which were greater than the positive control cisplatin.

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.

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.

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.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

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.

Precision Oncology, Signaling and Anticancer Agents in Cancer Therapeutics

BACKGROUND

The global alliance for genomics and healthcare facilities provides innovational solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention and treatment based on patients' genetic profile. Advancements in "omics" techniques, next-generation sequencing, artificial intelligence and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures.

METHOD

Data were collected from Pubmed and Google scholar using keywords: "Precision medicine", "precision medicine and cancer", "anticancer agents in precision medicine" and reviewed comprehensively.

RESULTS

Personalized therapeutics including immunotherapy, cancer vaccines, serve as a groundbreaking solution for cancer treatment. Herein, we take a measurable view of precision therapies and novel diagnostic approaches targeting cancer treatment. The contemporary applications of precision medicine have also been described along with various hurdles identified in the successful establishment of precision therapeutics.

CONCLUSION

This review highlights the key breakthroughs related to immunotherapies, targeted anticancer agents, and target interventions related to cancer signaling mechanisms. The success story of this field in context to drug resistance, safety, patient survival and in improving quality of life is yet to be elucidated. We conclude that, in the near future, the field of individualized treatments may truly revolutionize the nature of cancer patient care.

Topsentinol L Trisulfate, a Marine Natural Product That Targets Basal-like and Claudin-Low Breast Cancers

Patients diagnosed with basal-like breast cancer suffer from poor prognosis and limited treatment options. There is an urgent need to identify new targets that can benefit patients with basal-like and claudin-low (BL-CL) breast cancers. We screened fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancers. We identified a previously unreported trisulfated sterol, i.e., topsentinol L trisulfate (TLT), which exhibited increased efficacy against BL-CL breast cancers relative to luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL cell lines revealed its ability to inhibit activation of AMP-activated protein kinase (AMPK) and checkpoint kinase 1 (CHK1) and to promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL cell lines was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL breast cancers as compared with luminal/HER2+ breast cancer. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT, while glioblastoma multiforme was the least sensitive.

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.

Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com.

Large-scale sequencing efforts have uncovered a large number of secondary metabolic pathways, but the chemicals they synthesise remain unknown. Here the authors present PRISM 4, which predicts the chemical structures encoded by microbial genome sequences, including all classes of bacterial antibiotics in clinical use.

Too sweet: cheminformatics for deglycosylation in natural products

Sugar units in natural products are pharmacokinetically important but often redundant and therefore obstructing the study of the structure and function of the aglycon. Therefore, it is recommended to remove the sugars before a theoretical or experimental study of a molecule. Deglycogenases, enzymes that specialized in sugar removal from small molecules, are often used in laboratories to perform this task. However, there is no standardized computational procedure to perform this task in silico. In this work, we present a systematic approach for in silico removal of ring and linear sugars from molecular structures. Particular attention is given to molecules of biological origin and to their structural specificities. This approach is made available in two forms, through a free and open web application and as standalone open-source software.

Synthesis of bilocularin A carbamate derivatives and their evaluation as leucine transport inhibitors in prostate cancer cells

Large-scale extraction of the leaves of the Australian rainforest tree Maytenus bilocularis followed by extensive purification studies afforded the targeted and abundant dihydro-β-agarofuran, bilocularin A, in sufficient quantities (>500 mg) for detailed semi-synthetic chemistry. Eight bilocularin A carbamate analogues were synthesised using a series of commercially available isocyanate reagents in high purity (>95%) and variable yields (9-91%). All previously undescribed analogues were spectroscopically characterised using NMR, UV, IR and MS data. One compound afforded crystalline material and subsequent single crystal X-ray analysis (Cu-Kα) confirmed the chemical structure along with the absolute configuration. All compounds were evaluated for anti-proliferative activity against the human prostate cancer cell line LNCaP; none of the compounds showed significant (>50%) growth inhibition at 20 μM. Compounds were also tested for their ability to inhibit leucine transport in LNCaP cells, and two analogues showed moderate activity with IC₅₀ values of 8.9 and 8.5 μM. This is the first reported synthesis of dihydro-β-agarofuran carbamate derivatives.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

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.

Revisiting Inflammatory Bowel Disease: Pathology, Treatments, Challenges and Emerging Therapeutics Including Drug Leads from Natural Products

Inflammatory bowel disease (IBD) is a chronic and life-long disease characterized by gastrointestinal tract inflammation. It is caused by the interplay of the host's genetic predisposition and immune responses, and various environmental factors. Despite many treatment options, there is no cure for IBD. The increasing incidence and prevalence of IBD and lack of effective long-term treatment options have resulted in a substantial economic burden to the healthcare system worldwide. Biologics targeting inflammatory cytokines initiated a shift from symptomatic control towards objective treatment goals such as mucosal healing. There are seven monoclonal antibody therapies excluding their biosimilars approved by the US Food and Drug Administration for induction and maintenance of clinical remission in IBD. Adverse side effects associated with almost all currently available drugs, especially biologics, is the main challenge in IBD management. Natural products have significant potential as therapeutic agents with an increasing role in health care. Given that natural products display great structural diversity and are relatively easy to modify chemically, they represent ideal scaffolds upon which to generate novel therapeutics. This review focuses on the pathology, currently available treatment options for IBD and associated challenges, and the roles played by natural products in health care. It discusses these natural products within the current biodiscovery research agenda, including the applications of drug discovery techniques and the search for next-generation drugs to treat a plethora of inflammatory diseases, with a major focus on IBD.

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.

Cheminformatics Analysis of Natural Product Scaffolds: Comparison of Scaffolds Produced by Animals, Plants, Fungi and Bacteria

Natural products (NPs) have evolved over a very long natural selection process to form optimal interactions with biologically relevant macromolecules. NPs are therefore a very useful source of inspiration for the design of new drugs. In the present study we report the results of a cheminformatics analysis of a large database of NP structures focusing on their scaffolds. First, general differences between NP scaffolds and scaffolds from synthetic molecules are discussed, followed by a comparison of the properties of scaffolds produced by different types of organisms. Scaffolds produced by plants are the most complex and those produced by bacteria differ in many structural features from scaffolds produced by other organisms. The results presented here may be used as a guidance in selection of scaffolds for the design of novel NP-like bioactive structures or NP-inspired libraries.

Striking essential oil: tapping into a largely unexplored source for drug discovery

Essential oils (EOs) have been used therapeutically for centuries. In recent decades, randomized controlled (clinical) trials have supported efficacy in specific therapeutic indications for a few of them. Some EOs, their components or derivatives thereof have been approved as drugs. Nevertheless, they are still considered products that are mainly used in complementary and alternative medicine. EO components occupy a special niche in chemical space, that offers unique opportunities based on their unusual physicochemical properties, because they are typically volatile and hydrophobic. Here we evaluate selected physicochemical parameters, used in conventional drug discovery, of EO components present in a range of commercially available EOs. We show that, contrary to generally held belief, most EO components meet current-day requirements of medicinal chemistry for good drug candidates. Moreover, they also offer attractive opportunities for lead optimization or even fragment-based drug discovery. Because their therapeutic potential is still under-scrutinized, we propose that this be explored more vigorously with present-day methods.

Principle and design of pseudo-natural products

Natural products (NPs) are a significant source of inspiration towards the discovery of new bioactive compounds based on novel molecular scaffolds. However, there are currently only a small number of guiding synthetic strategies available to generate novel NP-inspired scaffolds, limiting both the number and types of compounds accessible. In this Perspective, we discuss a design approach for the preparation of biologically relevant small-molecule libraries, harnessing the unprecedented combination of NP-derived fragments as an overarching strategy for the synthesis of new bioactive compounds. These novel 'pseudo-natural product' classes retain the biological relevance of NPs, yet exhibit structures and bioactivities not accessible to nature or through the use of existing design strategies. We also analyse selected pseudo-NP libraries using chemoinformatic tools, to assess their molecular shape diversity and properties. To facilitate the exploration of biologically relevant chemical space, we identify design principles and connectivity patterns that would provide access to unprecedented pseudo-NP classes, offering new opportunities for bioactive small-molecule discovery.

Exploring the Multi-Target Neuroprotective Chemical Space of Benzofuran Scaffolds: A New Strategy in Drug Development for Alzheimer's Disease

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disorder that slowly destroys memory. The precise mechanism of AD is still not entirely understood and remains under discussion; it is believed to be a multifactorial disease in which a number of mechanisms are involved in its pathogenesis. Worldwide, near 37 million people suffer from the effects of AD. As a cause of death for elderly, it is predicted that AD will rank third in the coming years, just behind cancer and heart disease. Unfortunately, AD remains an incurable condition. Despite the devastating problems associated with AD, there are only four FDA approved drugs for palliative treatment of this pathology. Hence, renewed scientific efforts are required not only to uncover more insights into the AD process but also to develop more efficient pharmacological tools against this disease. Due to the complexity and multiple mechanisms at play in the progression of AD, the development of drugs by rational design is extremely difficult. The existing drugs to fight against Alzheimer's have had limited success, mainly due to their ability to modulate only one of the mechanisms involved in AD. As opposed to single-targeted strategies, the identification of small molecules able to affect multiple pathways involved in Alzheimer's is a promising strategy to develop more efficient medicines against this disease. Central to existing efforts to develop pharmaceuticals controlling AD is the discovery of new chemicals displaying strong neuroactivity. Benzofurans are privileged oxygen containing heterocycles that have a strong neuroprotective behavior, inhibiting several of the important events involved in the AD process. In this review, an approach is presented that relies on expanding the neuroprotective chemical space of benzofuran scaffolds by accessing them from Andean-Patagonian fungi and synthetic sources (chemical libraries). The exploration of the neuroprotective chemical space of these scaffolds has the potential to allow the discovery of substitution patterns that display multi-target neuroactivity against multiple events involved in AD. This benzofuran chemical framework will establish a multi-target chemical space that could set the basis for the development of super drugs against AD.

Fragment-based screening with natural products for novel anti-parasitic disease drug discovery

Introduction: Fragment-based drug discovery can identify relatively simple compounds with low binding affinity due to fewer binding interactions with protein targets. FBDD reduces the library size and provides simpler starting points for subsequent chemical optimization of initial hits. A much greater proportion of chemical space can be sampled in fragment-based screening compared to larger molecules with typical molecular weights (MWs) of 250-500 g mol-1 used in high-throughput screening (HTS) libraries. Areas covered: The authors cover the role of natural products in fragment-based drug discovery against parasitic disease targets. They review the approaches to develop fragment-based libraries either using natural products or natural product-like compounds. The authors present approaches to fragment-based drug discovery against parasitic diseases and compare these libraries with the 3D attributes of natural products. Expert opinion: To effectively use the three-dimensional properties and the chemical diversity of natural products in fragment-based drug discovery against parasitic diseases, there needs to be a mind-shift. Library design, in the medicinal chemistry area, has acknowledged that escaping flat-land is very important to increase the chances of clinical success. Attempts to increase sp3 richness in fragment libraries are acknowledged. Sufficient low molecular weight natural products are known to create true natural product fragment libraries.

Oleanolic Acid Derivatives as Potential Inhibitors of HIV-1 Protease

Pentacyclic triterpenes, such as oleanolic acid (I), are promising scaffolds for diversification through the use of combinatorial methods to obtain derivatives that improve their biological properties, increasing their bioavailability and enhancing their therapeutic efficacy. The purpose of this study was to evaluate the influence that derivatives of oleanolic acid, conjugated with one or two amino acids and an acyl group, might exert on HIV-1 protease inhibition. The in vitro studies conducted suggested that the presence of a carboxyacyl group generally improves the inhibition of HIV-1 protease, especially when a phthaloyl group is present, with IC₅₀ concentration values below 5 μM. The gain in activity of three 3-phthaloyl derivatives, with sub-micromolar IC₅₀ values, was between 60- and 100-fold more active than oleanolic acid. A molecular docking study has also been performed to elucidate the mode of binding to the protease by these oleanolic acid derivatives. In general, the derivatives that exhibited the highest inhibitory activity of HIV-1 protease also showed the highest binding energies in docking simulations. The overall results suggest that the coupling of one or two amino acids and a phthaloyl group to oleanolic acid improves HIV-1 protease inhibition, implying that these triterpene derivatives may be promising antiviral agents against HIV.

Analysis of the Acid/Base Profile of Natural Products from Different Sources

For many years drug discovery and other areas in chemistry have successfully relied on natural products. Recent advances in computational methods have made possible to study the chemical space of natural products from different sources. Ionizable acidic and basic functional groups heavily influence physicochemical properties and thus a molecule's absorption, distribution, metabolism, excretion, and toxicity characteristics as well as their affinity for biological targets. This work reports the generation and critical comparison of the acid/base profiles of ten chemical databases including seven natural products sets from different origins, a set of semisynthetic compounds, a collection of approved drugs, and a compendium of food chemicals. Similarities were found in the proportion of the main charge state categories among the natural products databases with few differences in their pK_a distributions. Clear differences were observed between natural products and the approved drugs and semi-synthetic natural products databases, whereas natural products share some trends with the food chemical database. We noted that the natural products collections comprise around 45 % of neutral compounds. The proportion of single acids was approximately twice that found for FDA drugs, and they demonstrated a similar distribution of pK_a values. In contrast to drugs, only 5 % of compounds among the natural products sets had a single basic group. Likewise, simple ampholytes were less prevalent in the natural products databases relative to drugs.

Structural simplification: an efficient strategy in lead optimization

The trend toward designing large hydrophobic molecules for lead optimization is often associated with poor drug-likeness and high attrition rates in drug discovery and development. Structural simplification is a powerful strategy for improving the efficiency and success rate of drug design by avoiding "molecular obesity". The structural simplification of large or complex lead compounds by truncating unnecessary groups can not only improve their synthetic accessibility but also improve their pharmacokinetic profiles, reduce side effects and so on. This review will summarize the application of structural simplification in lead optimization. Numerous case studies, particularly those involving successful examples leading to marketed drugs or drug-like candidates, will be introduced and analyzed to illustrate the design strategies and guidelines for structural simplification.

Generation of a Small Library of Natural Products Designed to Cover Chemical Space Inexpensively

Natural products space includes at least 200,000 compounds and the structures of most of these compounds are available in digital format. Previous analyses showed (i) that although they were capable of taking up synthetic pharmaceutical drugs, such exogenous molecules were likely the chief ‘natural’ substrates in the evolution of the transporters used to gain cellular entry by pharmaceutical drugs, and (ii) that a relatively simple but rapid clustering algorithm could produce clusters from which individual elements might serve to form a representative library covering natural products space. This exploited the fact that the larger clusters were likely to be formed early in evolution (and hence to have been accompanied by suitable transporters), so that very small clusters, including singletons, could be ignored. In the latter work, we assumed that the molecule chosen might be that in the middle of the cluster. However, this ignored two other criteria, namely the commercial availability and the financial cost of the individual elements of these clusters. We here develop a small representative library in which we to seek to satisfy the somewhat competing criteria of coverage (‘representativeness’), availability and cost. It is intended that the library chosen might serve as a testbed of molecules that may or may not be substrates for known or orphan drug transporters. A supplementary spreadsheet provides details, and their availability via a particular supplier.

A Systematic Cheminformatics Analysis of Functional Groups Occurring in Natural Products

The two most striking features that discriminate natural products from synthetic molecules are their characteristic scaffolds and unique functional groups (FGs). In this study we systematically investigate the distribution of FGs in natural products from a cheminformatics perspective by comparing FG frequencies in natural products with those found in average synthetic molecules. We thereby aim for the identification of FGs that are characteristic for molecules produced by living organisms. In our analysis we also include information about the natural origins of the structures investigated, allowing us to link the occurrence of specific FGs to the individual producing species. Our findings have the potential for being applied in a medicinal chemistry context concerning the synthesis of natural product-like libraries and natural product-inspired fragment collections. The results may be used also to support compound derivatization strategies and the design of "non-natural" natural products.

Natural products as modulators of the cyclic-AMP pathway: evaluation and synthesis of lead compounds

It is now well recognized that the normal cellular response in mammalian cells is critically regulated by the cyclic-AMP (cAMP) pathway through the appropriate balance of adenylyl cyclase (AC) and phosphodiesterase-4 (PDE4) activities. Dysfunctions in the cAMP pathway have major implications in various diseases like CNS disorders, inflammation and cardiac syndromes and, hence, the modulation of cAMP signalling through appropriate intervention of AC/PDE4 activities has emerged as a promising new drug discovery strategy of current interest. In this context, synthetic small molecules have had limited success so far and therefore parallel efforts on natural product leads have been actively pursued. The early promise of using the diterpene forskolin and its semi-synthetic analogs as AC activators has given way to new leads in the last decade from novel natural products like the marine sesterterpenoids alotaketals and ansellones and the 9,9'-diarylfluorenone cored selaginpulvilins, etc. and their synthesis has drawn much attention. This review captures these contemporary developments, particularly total synthesis campaigns and structure-guided analog design in the context of AC and PDE-4 modulating attributes and the scope for future possibilities.

NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules

Natural products (NPs) remain the most prolific resource for the development of small-molecule drugs. Here we report a new machine learning approach that allows the identification of natural products with high accuracy. The method also generates similarity maps, which highlight atoms that contribute significantly to the classification of small molecules as a natural product or synthetic molecule. The method can hence be utilized to (i) identify natural products in large molecular libraries, (ii) quantify the natural product-likeness of small molecules, and (iii) visualize atoms in small molecules that are characteristic of natural products or synthetic molecules. The models are based on random forest classifiers trained on data sets consisting of more than 265,000 to 322,000 natural products and synthetic molecules. Two-dimensional molecular descriptors, MACCS keys and Morgan2 fingerprints were explored. On an independent test set the models reached areas under the receiver operating characteristic curve (AUC) of 0.997 and Matthews correlation coefficients (MCCs) of 0.954 and higher. The method was further tested on data from the Dictionary of Natural Products, ChEMBL and other resources. The best-performing models are accessible as a free web service at http://npscout.zbh.uni-hamburg.de/npscout.

Approaches for the isolation and identification of hydrophilic, light-sensitive, volatile and minor natural products

Water-soluble, volatile, minor and photosensitive natural products are yet poorly known, and this review discusses the literature reporting the isolation strategies for some of these metabolites.

The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design

Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.

Raging the War Against Inflammation With Natural Products

Over the last few decade Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are the drugs of choice for treating numerous inflammatory diseases including rheumatoid arthritis. The NSAIDs produces anti-inflammatory activity via inhibiting cyclooxygenase enzyme, responsible for the conversation of arachidonic acid to prostaglandins. Likewise, cyclooxegenase-2 inhibitors (COX-2) selectively inhibit the COX-2 enzyme and produces significant anti-inflammatory, analgesic, and anti-pyretic activity without producing COX-1 associated gastrointestinal and renal side effects. In last two decades numerous selective COX-2 inhibitors (COXIBs) have been developed and approved for various inflammatory conditions. However, data from clinical trials have suggested that the prolong use of COX-2 inhibitors are also associated with life threatening cardiovascular side effects including ischemic heart failure and myocardial infection. In these scenario secondary metabolites from natural product offers a great hope for the development of novel anti-inflammatory compounds. Although majority of the natural product based compounds exhibit more selectively toward COX-1. However, the data suggest that slight structural modification can be helpful in developing COX-2 selective secondary metabolites with comparative efficacy and limited side effects. This review is an effort to highlight the secondary metabolites from terrestrial and marine source with significant COX-2 and COX-2 mediated PGE2 inhibitory activity, since it is anticipated that isolates with ability to inhibit COX-2 mediated PGE2 production would be useful in suppressing the inflammation and its classical sign and symptoms. Moreover, this review has highlighted the potential lead compounds including berberine, kaurenoic acid, α-cyperone, curcumin, and zedoarondiol for further development with the help of structure-activity relationship (SAR) studies and their current status.

Novel selenadiazole derivatives as selective antitumor and radical scavenging agents

Twenty-seven novel benzo[c][1,2,5]selenadiazole-5-carboxylic acid (BSCA) derivatives were designed and synthesized. Anti-proliferative activity of these structures was tested in vitro against a panel of five human cancer cell lines, including prostate (PC-3), colon (HT-29), leukemia (CCRF-CEM), lung (HTB-54) and breast (MCF-7). Four compounds (5, 6, 7 and 19) showed potent inhibitory activity with GI₅₀ values below 10 μM in at least one of the cancer cell lines. The selectivity of these compounds was further examined in two non-malignant cell lines derived from breast (184B5) and lung (BEAS-2B). Compound 7 exhibited promising anti-proliferative activity (GI₅₀ = 3.7 μM) in MCF-7 cells, together with high selectivity index (SI > 27.1). The induction of cell death by compound 7 was independent of the apoptotic process and it did not affect cell cycle progression either. Likewise, radical scavenging properties of the new selenadiazole derivatives were confirmed by testing their ability to scavenge DPPH radicals. Four compounds (1, 2, 8 and 9) showed potent radical scavenging activity, compound 9 being the most effective. Overall, while compound 7 was identified as the most cell growth inhibitory agent and selectively toxic to cancer cells, compound 9 proved to be the most potent antioxidant among the selenadiazole derivatives synthesized. This series of compounds can serve as an excellent scaffold to achieve new and potent antioxidant compounds useful for several diseases, i.e. cancer, neurodegenerative, heart diseases and leishmaniasis, considering the high radical scavenging activity and low toxicity showed by most of the compounds.

Why Are the Majority of Active Compounds in the CNS Domain Natural Products? A Critical Analysis

Small-molecule natural products (NPs) have a long and successful track record of providing first-in-class drugs and pharmacophore (scaffolds) in all therapeutic areas, serving as a bridge between modern and traditional medicine. This trajectory has been remarkably successful in three key areas of modern therapeutics: cancers, infections, and CNS diseases. Beginning with the discovery of morphine 200 years ago, natural products have remained the primary source of new drugs/scaffolds for CNS diseases. In this perspective, we address the question: why are the majority of active compounds in the CNS domain natural products? Our analysis indicates that ∼84% approved drugs for CNS diseases are NPs or NP-inspired, and interestingly, 20 natural products provided more than 400 clinically approved CNS drugs. We have discussed unique physicochemical properties of NPs and NP-inspired vis-à-vis synthetic drugs, isoform selectivity, and evolutionary relationship, providing a rationale for increasing focus on natural product driven discovery for next-generation drugs for neurodegenerative diseases.