NPCARE: database of natural products and fractional extracts for cancer regulation

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Synthesis and anticancer activity of podophyllotoxin derivatives with nitrogen-containing heterocycles

Three series of podophyllotoxin derivatives with various nitrogen-containing heterocycles were designed and synthesized. The antitumor activity of these podophyllotoxin derivatives was evaluated in vitro against a panel of human tumor cell lines. The results showed that podophyllotoxin-imidazolium salts and podophyllotoxin-1,2,4-triazolium salts a1-a20 exhibited excellent cytotoxic activity. Among them, a6 was the most potent cytotoxic compound with IC₅₀ values of 0.04-0.29 μM. Podophyllotoxin-1,2,3-triazole derivatives b1-b5 displayed medium cytotoxic activity, and podophyllotoxin-amine compounds c1-c3 has good cytotoxic activity with IC₅₀ value of 0.04-0.58 μM. Furthermore, cell cycle and apoptosis experiments of compound a6 were carried out and the results exhibited that a6 could induce G2/M cell cycle arrest and apoptosis in HCT-116 cells.

The LOTUS initiative for open knowledge management in natural products research

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation

Cancer is one of the most deadly diseases that annually kills millions of people worldwide. The investigation on anticancer medicines has never ceased to seek better and more adaptive agents with fewer side effects. Besides chemically synthetic anticancer compounds, natural products are scientifically proved as a highly potential alternative source for anticancer drug discovery. Along with experimental approaches being used to find anticancer drug candidates, computational approaches have been developed to virtually screen for potential anticancer compounds. In this study, we construct an ensemble computational framework, called iANP-EC, using machine learning approaches incorporated with evolutionary computation. Four learning algorithms (k-NN, SVM, RF, and XGB) and four molecular representation schemes are used to build a set of classifiers, among which the top-four best-performing classifiers are selected to form an ensemble classifier. Particle swarm optimization (PSO) is used to optimise the weights used to combined the four top classifiers. The models are developed by a set of curated 997 compounds which are collected from the NPACT and CancerHSP databases. The results show that iANP-EC is a stable, robust, and effective framework that achieves an AUC-ROC value of 0.9193 and an AUC-PR value of 0.8366. The comparative analysis of molecular substructures between natural anticarcinogens and nonanticarcinogens partially unveils several key substructures that drive anticancerous activities. We also deploy the proposed ensemble model as an online web server with a user-friendly interface to support the research community in identifying natural products with anticancer activities.

Mining Natural Products with Anticancer Biological Activity through a Systems Biology Approach

Natural products, like turmeric, are considered powerful antioxidants which exhibit tumor-inhibiting activity and chemoradioprotective properties. Nowadays, there is a great demand for developing novel, affordable, efficacious, and effective anticancer drugs from natural resources. In the present study, we have employed a stringent in silico methodology to mine and finally propose a number of natural products, retrieved from the biomedical literature. Our main target was the systematic search of anticancer products as anticancer agents compatible to the human organism for future use. In this case and due to the great plethora of such products, we have followed stringent bioinformatics methodologies. Our results taken together suggest that natural products of a great diverse may exert cytotoxic effects in a maximum of the studied cancer cell lines. These natural compounds and active ingredients could possibly be combined to exert potential chemopreventive effects. Furthermore, in order to substantiate our findings and their application potency at a systems biology level, we have developed a representative, user-friendly, publicly accessible biodatabase, NaturaProDB, containing the retrieved natural resources, their active ingredients/fractional mixtures, the types of cancers that they affect, and the corresponding experimentally verified target genes.

High-throughput screening for natural compound-based autophagy modulators reveals novel chemotherapeutic mode of action for arzanol

Autophagy is an intracellular recycling pathway with implications for intracellular homeostasis and cell survival. Its pharmacological modulation can aid chemotherapy by sensitizing cancer cells toward approved drugs and overcoming chemoresistance. Recent translational data on autophagy modulators show promising results in reducing tumor growth and metastasis, but also reveal a need for more specific compounds and novel lead structures. Here, we searched for such autophagy-modulating compounds in a flow cytometry-based high-throughput screening of an in-house natural compound library. We successfully identified novel inducers and inhibitors of the autophagic pathway. Among these, we identified arzanol as an autophagy-modulating drug that causes the accumulation of ATG16L1-positive structures, while it also induces the accumulation of lipidated LC3. Surprisingly, we observed a reduction of the size of autophagosomes compared to the bafilomycin control and a pronounced accumulation of p62/SQSTM1 in response to arzanol treatment in HeLa cells. We, therefore, speculate that arzanol acts both as an inducer of early autophagosome biogenesis and as an inhibitor of later autophagy events. We further show that arzanol is able to sensitize RT-112 bladder cancer cells towards cisplatin (CDDP). Its anticancer activity was confirmed in monotherapy against both CDDP-sensitive and -resistant bladder cancer cells. We classified arzanol as a novel mitotoxin that induces the fragmentation of mitochondria, and we identified a series of targets for arzanol that involve proteins of the class of mitochondria-associated quinone-binding oxidoreductases. Collectively, our results suggest arzanol as a valuable tool for autophagy research and as a lead compound for drug development in cancer therapy.

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.

Nutraceuticals against Oxidative Stress in Autoimmune Disorders

Antioxidant mechanisms are constituted of enzymes, endogenous, and non-enzymatic, exogenous, which have the role of counterbalancing oxidative stress. Intake of these compounds occurs in the diet. Vegetables, plants, and fruits contain a wide range of alkaloids, polyphenols, and terpenoids which are called “phytochemicals”. Most of these substances are responsible for the positive properties of fruits and vegetables, which are an essential part of a healthy life with roles in ameliorating chronic illnesses and favoring longevity. Nutraceuticals are substances contained in a food or fragment of it influencing health with positive effects on health helping in precenting or treating disorders. We conducted a review illustrating the principal applications of nutraceuticals in autoimmune disorders. Literature reported several studies about exogenous dietary antioxidant supplementation in diverse autoimmune diseases such as rheumatoid arthritis, lupus, diabetes, and multiple sclerosis. In these pathologies, promising results were obtained in some cases. Positive outcomes were generally associated with a reduction of oxidative stress parameters and a boost to antioxidant systems, and sometimes with anti-inflammatory effects. The administration of exogenous substances through food derivates or dietary supplements following scientific standardization was demonstrated to be effective. Further bias-free and extended studies should be conducted that include ever-increasing oxidative stress biomarkers.

COCONUT online: Collection of Open Natural Products database

Natural products (NPs) are small molecules produced by living organisms with potential applications in pharmacology and other industries as many of them are bioactive. This potential raised great interest in NP research around the world and in different application fields, therefore, over the years a multiplication of generalistic and thematic NP databases has been observed. However, there is, at this moment, no online resource regrouping all known NPs in just one place, which would greatly simplify NPs research and allow computational screening and other in silico applications. In this manuscript we present the online version of the COlleCtion of Open Natural prodUcTs (COCONUT): an aggregated dataset of elucidated and predicted NPs collected from open sources and a web interface to browse, search and easily and quickly download NPs. COCONUT web is freely available at https://coconut.naturalproducts.net .

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

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.

NPBS database: a chemical data resource with relational data between natural products and biological sources

NPBS (Natural Products & Biological Sources) database is a chemical data resource with relational data between natural products and biological sources, manually curated from literatures of natural product researches. The relational data link a specific species and all the natural products derived from it and contrarily link a specific natural product and all the biological sources. The biological sources cover diverse species of plant, bacterial, fungal and marine organisms; the natural molecules have proper chemical structure data and computable molecular properties and all the relational data have corresponding references. NPBS database provides a wider choice of biological sources and can be used for dereplication to prevent re-isolation and re-characterization of already known natural products.

Database URL: http://www.organchem.csdb.cn/scdb/NPBS

Pharmacoinformatic Investigation of Medicinal Plants from East Africa

Medicinal plants have widely been used in the traditional treatment of ailments and have been proven effective. Their contribution still holds an important place in modern drug discovery due to their chemical, and biological diversities. However, the poor documentation of traditional medicine, in developing African countries for instance, can lead to the loss of knowledge related to such practices. In this study, we present the Eastern Africa Natural Products Database (EANPDB) containing the structural and bioactivity information of 1870 unique molecules isolated from about 300 source species from the Eastern African region. This represents the largest collection of natural products (NPs) from this geographical region, covering literature data of the period from 1962 to 2019. The computed physicochemical properties and toxicity profiles of each compound have been included. A comparative analysis of some physico-chemical properties like molecular weight, H-bond donor/acceptor, logPo/w , etc. as well scaffold diversity analysis has been carried out with other published NP databases. EANPDB was combined with the previously published Northern African Natural Products Database (NANPDB), to form a merger African Natural Products Database (ANPDB), containing ∼6500 unique molecules isolated from about 1000 source species (freely available at http://african-compounds.org). As a case study, latrunculins A and B isolated from the sponge Negombata magnifica (Podospongiidae) with previously reported antitumour activities, were identified via substructure searching as molecules to be explored as putative binders of histone deacetylases (HDACs).

Proanthocyanidins reduce cellular function in the most globally diagnosed cancers in vitro

BACKGROUND

Growing evidence indicates that proanthocyanidins (PACs) may be effective in treating and preventing various cancers. The fundamental mechanism of PACs inhibiting the proliferation at cellular and molecular levels in most of the cancer types remains unclear.

OBJECTIVE

The anticancer efficacy of PACs was investigated in vitro using three human cancer cell lines: human colorectal adenocarcinoma (HT-29), human breast carcinoma (MCF-7), and human prostatic adenocarcinoma (PC-3).

METHODS

Cytotoxicity was evaluated by MTT assay, while cell proliferation was measured by trypan blue exclusion method. Cell migration was measured by wound healing assay, and DAPI staining was used to evaluate apoptotic nucleus morphology. RT-PCR was used to analyze the expression of Bax and Bcl-2, and caspase enzyme activity assay was measured by caspase colorimetric assay.

RESULTS

PACs could inhibit both cellular viability and proliferation in a concentration- and time-dependent fashion in all investigated cells. Further, all tested cells showed similarly decreased migration after 24- and 48-h PAC treatment. We observed increased apoptotic nucleus morphology in treated cells (p ≤ 0.01). BAX expression significantly increased in HT-29 (p < 0.01), PC-3(p < 0.01), and MCF-7 (p < 0.05) cells, while BCL-2 expression significantly declined (p < 0.05). Caspase activities were significantly increased in all tested cancer cell lines after 24-h PAC treatment.

CONCLUSION

PACs may have potential therapeutic properties against colorectal, breast, and prostate cancer.

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).

An enumeration of natural products from microbial, marine and terrestrial sources

The discovery of a new drug is a multidisciplinary and very costly task. One of the major steps is the identification of a lead compound, i.e. a compound with a certain degree of potency and that can be chemically modified to improve its activity, metabolic properties, and pharmacokinetics profiles. Terrestrial sources (plants and fungi), microbes and marine organisms are abundant resources for the discovery of new structurally diverse and biologically active compounds. In this chapter, an attempt has been made to quantify the numbers of known published chemical structures (available in chemical databases) from natural sources. Emphasis has been laid on the number of unique compounds, the most abundant compound classes and the distribution of compounds in terrestrial and marine habitats. It was observed, from the recent investigations, that ~500,000 known natural products (NPs) exist in the literature. About 70 % of all NPs come from plants, terpenoids being the most represented compound class (except in bacteria, where amino acids, peptides, and polyketides are the most abundant compound classes). About 2,000 NPs have been co-crystallized in PDB structures.

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery

Traditional herbal medicine has been an inseparable part of the traditional medical science in many countries throughout history. Nowadays, the popularity of using herbal medicines in daily life, as well as clinical practices, has gradually expanded to numerous Western countries with positive impacts and acceptance. The continuous growth of the herbal consumption market has promoted standardization and modernization of herbal-derived products with present pharmacological criteria. To store and extensively share this knowledge with the community and serve scientific research, various herbal metabolite databases have been developed with diverse focuses under the support of modern advances. The advent of these databases has contributed to accelerating research on pharmaceuticals of natural origins. In the scope of this study, we critically review 30 herbal metabolite databases, discuss different related perspectives, and provide a comparative analysis of 18 accessible noncommercial ones. We hope to provide you with fundamental information and multidimensional perspectives from herbal medicines to modern drug discovery.

Examination of molecular space and feasible structures of bioactive components of humic substances by FTICR MS data mining in ChEMBL database

Humic substances (HS) are complex natural mixtures comprising a large variety of compounds produced during decomposition of decaying biomass. The molecular composition of HS is extremely diverse as it was demonstrated with the use of high resolution mass spectrometry. The building blocks of HS are mostly represented by plant-derived biomolecules (lignins, lipids, tannins, carbohydrates, etc.). As a result, HS show a wide spectrum of biological activity. Despite that, HS remain a 'biological activity black-box' due to unknown structures of constituents responsible for the interaction with molecular targets. In this study, we investigated the antiviral activity of eight HS fractions isolated from peat and coal, as well as of two synthetic humic-like materials. We determined molecular compositions of the corresponding samples using ultra-high resolution Fourier-transform ion cyclotron resonance mass-spectrometry (FTICR MS). Inhibitory activity of HS was studied with respect to reproduction of tick-borne encephalitis virus (TBEV), which is a representative of Flavivirus genus, and to a panel of enteroviruses (EVs). The samples of natural HS inhibited TBEV reproduction already at a concentration of 1 µg/mL, but they did not inhibit reproduction of EVs. We found that the total relative intensity of FTICR MS formulae within elemental composition range commonly attributed to flavonoid-like structures is correlating with the activity of the samples. In order to surmise on possible active structural components of HS, we mined formulae within FTICR MS assignments in the ChEMBL database. Out of 6502 formulae within FTICR MS assignments, 3852 were found in ChEMBL. There were more than 71 thousand compounds related to these formulae in ChEMBL. To support chemical relevance of these compounds to natural HS we applied the previously developed approach of selective isotopic exchange coupled to FTICR MS to obtain structural information on the individual components of HS. This enabled to propose compounds from ChEMBL, which corroborated the labeling data. The obtained results provide the first insight onto the possible structures, which comprise antiviral components of HS and, respectively, can be used for further disclosure of antiviral activity mechanism of HS.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

An Apparent Binary Choice in Biochemistry: Mutual Reactivity Implies Life Chooses Thiols or Nitrogen-Sulfur Bonds, but Not Both

A fundamental goal of biology is to understand the rules behind life's use of chemical space. Established work focuses on why life uses the chemistry that it does. Given the enormous scope of possible chemical space, we postulate that it is equally important to ask why life largely avoids certain areas of chemical space. The nitrogen-sulfur bond is a prime example, as it rarely appears in natural molecules, despite the very rich N-S bond chemistry applied in various branches of industry (e.g., industrial materials, agrochemicals, pharmaceuticals). We find that, out of more than 200,000 known, unique compounds made by life, only about 100 contain N-S bonds. Furthermore, the limited number of N-S bond-containing molecules that life produces appears to fall into a few very distinctive structural groups. One may think that industrial processes are unrelated to biochemistry because of a greater possibility of solvents, catalysts, and temperatures available to industry than to the cellular environment. However, the fact that life does rarely make N-S bonds, from the plentiful precursors available, and has evolved the ability to do so independently several times, suggests that the restriction on life's use of N-S chemistry is not in its synthesis. We present a hypothesis to explain life's extremely limited usage of the N-S bond: that the N-S bond chemistry is incompatible with essential segments of biochemistry, specifically with thiols. We support our hypothesis by (1) a quantitative analysis of the occurrence of N-S bond-containing natural products and (2) reactivity experiments between selected N-S compounds and key biological molecules. This work provides an example of a reason why life nearly excludes a distinct region of chemical space. Combined with future examples, this potentially new field of research may provide fresh insight into life's evolution through chemical space and its origin and early evolution.

Free Accessible Databases as a Source of Information about Food Components and Other Compounds with Anticancer Activity⁻Brief Review

Diet is considered to be a significant factor in cancer prevention and therapy. Many food components reveal anticancer activity. The increasing number of experiments concerning the anticancer potential of chemical compounds, including food components, is a challenge for data searching. Specialized databases provide an opportunity to overcome this problem. Data concerning the anticancer activity of chemical compounds may be found in general databases of chemical compounds and databases of drugs, including specialized resources concerning anticancer compounds, databases of food components, and databases of individual groups of compounds, such as polyphenols or peptides. This brief review summarizes the state of knowledge of chemical databases containing information concerning natural anticancer compounds (e.g., from food). Additionally, the information about text- and structure-based search options and links between particular internet resources is provided in this paper. Examples of the application of databases in food and nutrition sciences are also presented with special attention to compounds that are interesting from the point of view of dietary cancer prevention. Simple examples of potential database search possibilities are also discussed.

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.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.

Resources for Chemical, Biological, and Structural Data on Natural Products

Natural products from plants, marine life, animals, fungi, bacteria, and other organisms remain the most productive source of inspiration for small-molecule drug discovery. Today, a wealth of information on natural products that is particularly valuable to applications in cheminformatics is at our disposal. In this contribution, we provide a timely overview of relevant resources for measured chemical, biological, and structural data on natural products. In particular, we comment on the accessibility, scope, chemical space, and limitations of the individual data sources. The bottleneck of natural products remains the limited availability of material for testing. In this context, we analyze the number of natural products readily obtainable from commercial and other sources.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

An Updated Review on Marine Anticancer Compounds: The Use of Virtual Screening for the Discovery of Small-Molecule Cancer Drugs

Marine secondary metabolites are a promising source of unexploited drugs that have a wide structural diversity and have shown a variety of biological activities. These compounds are produced in response to the harsh and competitive conditions that occur in the marine environment. Invertebrates are considered to be among the groups with the richest biodiversity. To date, a significant number of marine natural products (MNPs) have been established as antineoplastic drugs. This review gives an overview of MNPs, both in research or clinical stages, from diverse organisms that were reported as being active or potentially active in cancer treatment in the past seventeen years (from January 2000 until April 2017) and describes their putative mechanisms of action. The structural diversity of MNPs is also highlighted and compared with the small-molecule anticancer drugs in clinical use. In addition, this review examines the use of virtual screening for MNP-based drug discovery and reveals that classical approaches for the selection of drug candidates based on ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering may miss potential anticancer lead compounds. Finally, we introduce a novel and publically accessible chemical library of MNPs for virtual screening purposes.