Structural diversity of biologically interesting datasets: a scaffold analysis approach

Molecular docking, dynamics and in vitro analysis of multi-target inhibitors for Clostridioides difficile

The opportunistic pathogen, Clostridioides difficile owes its extreme pathogenicity for its ability to develop antibiotic resistance and recurrent infections. The current antibiotics used for the treatment are showing declining sensitivity and rising antibiotic resistance. Therefore, it is of interest to develop the anti-clostridial drugs to overcome these issues. Hence, we have explored ZINC library to find the suitable lead compounds against five target proteins of C. difficile. Multistep virtual screening is performed to find the suitable compounds that are checked for their stability using molecular dynamics and are validated in vitro against C. difficile. In our study, five compounds viz., ZINC64969876, ZINC13641164, ZINC13691348, ZINC5554596 and ZINC3894278 that inhibit HisC, Spo0A, PdcA, DAHP synthase and cyclic-di GMP proteins, respectively have been identified. Further, these compounds were tested in vitro against four different isolates of C. difficile and all of them were found to inhibit the pathogen. However, to use these compounds as anti-clostridial drugs, further testing needs to be done. The selected compounds from our study are reported for the first time as antimicrobial agents against C. difficile.

Scaffold and Structural Diversity of the Secondary Metabolite Space of Medicinal Fungi

Medicinal fungi, including mushrooms, have well-documented therapeutic uses. In this study, we perform a cheminformatics-based investigation of the scaffold and structural diversity of the secondary metabolite space of medicinal fungi and, moreover, perform a detailed comparison with approved drugs, other natural product libraries, and semi-synthetic libraries. We find that the secondary metabolite space of medicinal fungi has similar or higher scaffold diversity in comparison to other natural product libraries analyzed here. Notably, 94% of the scaffolds in the secondary metabolite space of medicinal fungi are not present in the approved drugs. Further, we find that the secondary metabolites, on the one hand, are structurally far from the approved drugs, while, on the other hand, they are close in terms of molecular properties to the approved drugs. Lastly, chemical space visualization using dimensionality reduction methods showed that the secondary metabolite space has minimal overlap with the approved drug space. In a nutshell, our results underscore that the secondary metabolite space of medicinal fungi is a valuable resource for identifying potential lead molecules for natural product-based drug discovery.

Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach

Double mutated epidermal growth factor receptor is a clinically important target for addressing drug resistance in lung cancer treatment. Therefore, discovering new inhibitors against the T790M/L858R (TMLR) resistant mutation is ongoing globally. In the present study, nearly 150 000 molecules from various natural product libraries were screened by employing different ligand and structure-based techniques. Initially, the library was filtered to identify drug-like molecules, which were subjected to a machine learning based classification model to identify molecules with a higher probability of having anti-cancer activity. Simultaneously, rules for constrained docking were derived from three-dimensional protein-ligand complexes and thereafter, constrained docking was undertaken, followed by HYDE binding affinity assessment. As a result, three molecules that resemble interactions similar to the co-crystallized complex were selected and subjected to 100 ns molecular dynamics simulation for stability analysis. The interaction analysis for the 100 ns simulation period showed that the leads exhibit the conserved hydrogen bond interaction with Gln791 and Met793 as in the co-crystal ligand. Also, the study indicated that Y-shaped molecules are preferred in the binding pocket as it enables them to occupy both pockets. The MMGBSA binding energy calculations revealed that the molecules have comparable binding energy to the native ligand. The present study has enabled the identification of a few ADMET adherent leads from natural products that exhibit the potential to inhibit the double mutated drug-resistant EGFR.

Chemical Space, Scaffolds, and Halogenated Compounds of CMNPD: A Comprehensive Chemoinformatic Analysis

The comprehensive marine natural products database (CMNPD) is a new free access and comprehensive database developed originally by Lyu's team of our research group, including more than 30 000 marine natural products (MNPs) reported from the 1960s. In this article, we aimed to present CMNPD's value in drug discovery and to present several characteristics of MNPs based on our new comprehensive data. We used chemoinformatic analysis methods to report the molecular properties, chemical space, and several scaffold assessments of CMNPD compared with several databases. Then, we reported the characteristics of MNPs from the aspect of halogens, comparing MNPs with terrestrial natural products (TNPs) and drugs. We found that CMNPD had a low proportion (2.91%) of scaffolds utilized by drugs, and high similarities between CMNPD and NPAtlas (a microbial natural products database), which are worth further investigation. The proportion of bromides in MNPs is outstandingly higher (11.0%) in contrast to other halogens. Furthermore, the results showed great differences in halogenated structures between MNPs and drugs, especially brominated substructures. Finally, we found that many marine species (2.52%) reported only halogenated compounds. It can be concluded from these results that CMNPD is a promising source for drug discovery and has many scientific issues relative to MNPs that need to be further investigated.

Profiling and analysis of chemical compounds using pointwise mutual information

Pointwise mutual information (PMI) is a measure of association used in information theory. In this paper, PMI is used to characterize several publicly available databases (DrugBank, ChEMBL, PubChem and ZINC) in terms of association strength between compound structural features resulting in database PMI interrelation profiles. As structural features, substructure fragments obtained by coding individual compounds as MACCS, PubChemKey and ECFP fingerprints are used. The analysis of publicly available databases reveals, in accord with other studies, unusual properties of DrugBank compounds which further confirms the validity of PMI profiling approach. Z-standardized relative feature tightness (ZRFT), a PMI-derived measure that quantifies how well the given compound's feature combinations fit these in a particular compound set, is applied for the analysis of compound synthetic accessibility (SA), as well as for the classification of compounds as easy (ES) and hard (HS) to synthesize. ZRFT value distributions are compared with these of SYBA and SAScore. The analysis of ZRFT values of structurally complex compounds in the SAVI database reveals oligopeptide structures that are mispredicted by SAScore as HS, while correctly predicted by ZRFT and SYBA as ES. Compared to SAScore, SYBA and random forest, ZRFT predictions are less accurate, though by a narrow margin (AccZRFT = 94.5%, AccSYBA = 98.8%, AccSAScore = 99.0%, AccRF = 97.3%). However, ZRFT ability to distinguish between ES and HS compounds is surprisingly high considering that while SYBA, SAScore and random forest are dedicated SA models, ZRFT is a generic measurement that merely quantifies the strength of interrelations between structural feature pairs. The results presented in the current work indicate that structural feature co-occurrence, quantified by PMI or ZRFT, contains a significant amount of information relevant to physico-chemical properties of organic compounds.

Computer-Aided Analysis of Multiple SARS-CoV-2 Therapeutic Targets: Identification of Potent Molecules from African Medicinal Plants

The COVID-19 pandemic, which started in Wuhan, China, has spread rapidly over the world with no known antiviral therapy or vaccine. Interestingly, traditional Chinese medicine helped in flattening the pandemic curve in China. In this study, molecules from African medicinal plants were analysed as potential candidates against multiple SARS-CoV-2 therapeutic targets. Sixty-five molecules from the ZINC database subset (AfroDb Natural Products) were virtually screened with some reported repurposed therapeutics against six SARS-CoV-2 and two human targets. Molecular docking, druglikeness, absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the best hits were further simulated. Of the 65 compounds, only three, namely, 3-galloylcatechin, proanthocyanidin B1, and luteolin 7-galactoside found in almond (Terminalia catappa), grape (Vitis vinifera), and common verbena (Verbena officinalis), were able to bind to all eight targets better than the reported repurposed drugs. The findings suggest these molecules may play a role as therapeutic leads in tackling this pandemic due to their multitarget activity.

Homology Modeling of Leishmanolysin (gp63) from Leishmania panamensis and Molecular Docking of Flavonoids

Leishmaniasis is a chronic disease caused by protozoa of the distinct Leishmania genus transmitted by sandflies of the genus Phlebotomus (old world) and Lutzomyia (new world). Among the molecular factors that contribute to the virulence and pathogenesis of Leishmania are metalloproteases, e.g., glycoprotein 63 (gp63), also known as leishmanolysin or major surface protease (MSP). This protease is a zinc-dependent metalloprotease that is found on the surface of the parasite, abundant in Leishmania promastigote and amastigote. This study describes the prediction of three-dimensional (3D) structures of leishmanolysin (UniProt ID A0A088RJX7) of Leishmania panamensis employing a homology modeling approach. The 3D structure prediction was performed using the SWISS-MODEL web server. The tools PROCHECK, Molprobyty, and Verify3D were used to check the quality of the model, indicating that they are reliable. Best docking configurations were identified applying AutoDock Vina in PyRx 0.8 to obtain a potential antileishmanial activity. Biflavonoids such as lanaroflavone, podocarpusflavone A, amentoflavone, and podocarpusflavone B showed good scores among these molecules. Lanaroflavone appears to be the most suitable compound from binding affinity calculations.

Natural-product-library-based screening for discovery of capsid C-terminal domain targeted HIV-1 inhibitors

Antiretroviral therapy (ART) can effectively suppress replication of human immunodeficiency virus type 1 (HIV-1) and limit disease progression. However, ART is unable to eradicate the virus, and the requirement for lifelong treatment may have side effects and may lead to the development of resistance. New approaches to prevent and treat HIV-1 infection should therefore be developed. HIV-1 capsid (CA) protein is an unexploited but attractive target for antiviral drug development. The hydrophobic cavity of the C-terminal domain of CA (CA CTD) has been validated as a potential target for antiviral drugs. Binding of compounds to this conserved non-polar groove in CA CTD allosterically disrupts the CA assembly. This study screened 2080 natural products to identify potential antiviral agents for further development to combat HIV-1 infection. From the primary screen at a fixed concentration of 50 µM, 16 compounds were found to be effective against this target. Six compounds observed in the primary screen were confirmed in dose-response experiments, and were tested against HIV-1-induced cytopathic effects. Two compounds were found to inhibit HIV-1 replication, and the most active compound - rubranol - inhibited viral replication at a moderate micromolar concentration (EC₅₀ = 15.85 μM). The binding modes of rubranol and hirsutanonol to CA CTD were analysed by molecular docking, providing insight for the design of drugs targeting HIV-1 CA. This study reports, for the first time, identification of natural products that showed potential as anti-HIV-1 agents by targeting the conserved hydrophobic cavity of HIV-1 CA CTD.

Synthesis of Aza-acyclic Nucleoside Libraries of Purine, Pyrimidine, and 1,2,4-Triazole

Under the aegis of the Pilot Scale Library Program of the NIH Roadmap Initiative, a new library of propan-1-amine containing aza acyclic nucleosides was designed and prepared, and we now report a diverse set of 157 purine, pyrimidine, and 1,2,4-triazole- N-acetamide analogues. These new nucleoside analogues were prepared in a parallel high throughput solution-phase format. A set of diverse amines was reacted with several nucleobase N-propaldehydes utilizing reductive amination with sodium triacetoxyborohydride coupling to produce a small and diverse aza acyclic nucleoside library. All reactions were performed using 24-well reaction blocks and an automatic reagent-dispensing platform under an inert atmosphere. Final targets were purified on an automated system using solid sample loading prepacked cartridges and prepacked silica gel columns. All compounds were characterized by NMR and HRMS and were analyzed for purity by HPLC prior to submission to the Molecular Libraries Small Molecule Repository (MLSMR). Initial screening through the Molecular Libraries Probe Production Centers Network (MLPCN) demonstrated diverse and interesting biological activities.

In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all models were built using imbalanced data sets, which caused a high false-positive rate. Therefore, we tried to solve the problem of biased data sets and built a reliable classification model with 2358 compounds. Machine learning and resampling methods were used simultaneously for the refinement of models with both 2 D molecular descriptors and molecular fingerprints to represent the chemicals. Through a series of evaluation, we realized that resampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and SMOTE+edited nearest neighbor could effectively solve the problem of imbalanced data sets and that MACCS fingerprint combined with support vector machine performed the best. After the final construction of a consensus model, the overall accuracy rate was increased to 0.966 for the final external data set. Also, the accuracy rate of the model for the test set was 0.919, with an excellent balanced capacity of 0.925 (sensitivity) to predict BBB-positive compounds and of 0.899 (specificity) to predict BBB-negative compounds. Compared with other BBB classification models, our models reduced the rate of false positives and were more robust in prediction of BBB-positive as well as BBB-negative compounds, which would be quite helpful in early drug discovery.

Robust QSAR prediction models for volume of distribution at steady state in humans using relative distance measurements

The building of quantitative structure-activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms. Support vector machine, complex tree, bagged tree and Gaussian process regression algorithms were tested for fingerprint, similarity and relative distance matrixes used as input data, and the results of the built models were compared. Relative distance matrixes generated robust regression models in the training and external validation stages performed using cross-validation, obtaining values for correlation coefficient, bias, slope and root-mean-square error close to the ideal. Relative distance matrixes were also used for the design of classification models, obtaining excellent results with values of accuracy and area under receiver operating characteristic (AUC) close to 100%.

Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products

This is a new golden age for drug discovery based on natural products derived from both marine and terrestrial sources. Herein, a straightforward but important question is "what are the major structural differences between marine natural products (MNPs) and terrestrial natural products (TNPs)?" To answer this question, we analyzed the important physicochemical properties, structural features, and drug-likeness of the two types of natural products and discussed their differences from the perspective of evolution. In general, MNPs have lower solubility and are often larger than TNPs. On average, particularly from the perspective of unique fragments and scaffolds, MNPs usually possess more long chains and large rings, especially 8- to 10-membered rings. MNPs also have more nitrogen atoms and halogens, notably bromines, and fewer oxygen atoms, suggesting that MNPs may be synthesized by more diverse biosynthetic pathways than TNPs. Analysis of the frequently occurring Murcko frameworks in MNPs and TNPS also reveals a striking difference between MNPs and TNPs. The scaffolds of the former tend to be longer and often contain ester bonds connected to 10-membered rings, while the scaffolds of the latter tend to be shorter and often bear more stable ring systems and bond types. Besides, the prediction from the naïve Bayesian drug-likeness classification model suggests that most compounds in MNPs and TNPs are drug-like, although MNPs are slightly more drug-like than TNPs. We believe that MNPs and TNPs with novel drug-like scaffolds have great potential to be drug leads or drug candidates in drug discovery campaigns.

The Rise and Fall of a Scaffold: A Trend Analysis of Scaffolds in the Medicinal Chemistry Literature

Scaffolds are a core concept in medicinal chemistry, and they can be the focus of multiple independent development efforts over an extended period. Thus, scaffold associated properties can vary over time, possibly showing consistently increasing or decreasing trends. We posit that such trends characterize the attention that the community pays to a scaffold. In this study, we employed data from ChEMBL20 to examine the evolution of scaffold features, such as enumerated compounds, biological activity, and liabilities, over 17 years. Our analysis highlights that certain properties such as enumerated compounds, but not liabilities, show statistically significant increasing trends for some scaffolds. We also attempt to explain why a scaffold receives more attention over time and highlight that obvious aspects such as synthetic feasibility do not explicitly drive attention. In summary, trend analyses of scaffold properties could support scaffold selection and prioritization in small molecule development projects.

Natural products used as a chemical library for protein-protein interaction targeted drug discovery

Protein-protein interactions (PPIs), which are essential for cellular processes, have been recognized as attractive therapeutic targets. Therefore, the construction of a PPI-focused chemical library is an inevitable necessity for future drug discovery. Natural products have been used as traditional medicines to treat human diseases for millennia; in addition, their molecular scaffolds have been used in diverse approved drugs and drug candidates. The recent discovery of the ability of natural products to inhibit PPIs led us to use natural products as a chemical library for PPI-targeted drug discovery. In this study, we collected natural products (NPDB) from non-commercial and in-house databases to analyze their similarities to small-molecule PPI inhibitors (iPPIs) and FDA-approved drugs by using eight molecular descriptors. Then, we evaluated the distribution of NPDB and iPPIs in the chemical space, represented by the molecular fingerprint and molecular scaffolds, to identify the promising scaffolds, which could interfere with PPIs. To investigate the ability of natural products to inhibit PPI targets, molecular docking was used. Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model. These selected natural products showed high binding affinities to the PPI target, namely XIAP, which were validated in an in vitro experiment. In addition, the natural products with novel scaffolds might provide a promising starting point for further medicinal chemistry developments. Overall, our study shows the potency of natural products in targeting PPIs, which might help in the design of a PPI-focused chemical library for future drug discovery.

Comparative analyses of structural features and scaffold diversity for purchasable compound libraries

Large purchasable screening libraries of small molecules afforded by commercial vendors are indispensable sources for virtual screening (VS). Selecting an optimal screening library for a specific VS campaign is quite important to improve the success rates and avoid wasting resources in later experimental phases. Analysis of the structural features and molecular diversity for different screening libraries can provide valuable information to the decision making process when selecting screening libraries for VS. In this study, the structural features and scaffold diversity of eleven purchasable screening libraries and Traditional Chinese Medicine Compound Database (TCMCD) were analyzed and compared. Their scaffold diversity represented by the Murcko frameworks and Level 1 scaffolds was characterized by the scaffold counts and cumulative scaffold frequency plots, and visualized by Tree Maps and SAR Maps. The analysis demonstrates that, based on the standardized subsets with similar molecular weight distributions, Chembridge, ChemicalBlock, Mucle, TCMCD and VitasM are more structurally diverse than the others. Compared with all purchasable screening libraries, TCMCD has the highest structural complexity indeed but more conservative molecular scaffolds. Moreover, we found that some representative scaffolds were important components of drug candidates against different drug targets, such as kinases and guanosine-binding protein coupled receptors, and therefore the molecules containing pharmacologically important scaffolds found in screening libraries might be potential inhibitors against the relevant targets. This study may provide valuable perspective on which purchasable compound libraries are better for you to screen.Graphical abstract

Following Ramachandran: exit vector plots (EVP) as a tool to navigate chemical space covered by 3D bifunctional scaffolds. The case of cycloalkanes

An approach to analysis and visualization of chemical space covered by disubstituted scaffolds, which is based on exit vector plots (EVP), is used for analysis of cycloalkane. Four clearly defined regions (α, β, γ and δ) are found in their EVP.

Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites

BACKGROUND

A recent comparison showed the extensive similarities between the structural properties of metabolites in the reconstructed human metabolic network ("endogenites") and those of successful, marketed drugs ("drugs").

RESULTS

Clustering indicated the related but differential population of chemical space by endogenites and drugs. Differences between the drug-endogenite similarities resulting from various encodings and judged by Tanimoto similarity could be related simply to the fraction of the bitstrings set to 1. By extracting drug/endogenite substructures, we develop a novel family of fingerprints, the Drug Endogenite Substructure (DES) encodings, based on the ranked frequency of the various substructures. These provide a natural assessment of drug-endogenite likeness, and may be used as descriptors with which to derive quantitative structure-activity relationships (QSARs).

CONCLUSIONS

"Drug-endogenite likeness" seems to have utility, and leads to a simple, novel and interpretable substructure-based molecular encoding for cheminformatics.

How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion

One approach to experimental science involves creating hypotheses, then testing them by varying one or more independent variables, and assessing the effects of this variation on the processes of interest. We use this strategy to compare the intellectual status and available evidence for two models or views of mechanisms of transmembrane drug transport into intact biological cells. One (BDII) asserts that lipoidal phospholipid Bilayer Diffusion Is Important, while a second (PBIN) proposes that in normal intact cells Phospholipid Bilayer diffusion Is Negligible (i.e., may be neglected quantitatively), because evolution selected against it, and with transmembrane drug transport being effected by genetically encoded proteinaceous carriers or pores, whose “natural” biological roles, and substrates are based in intermediary metabolism. Despite a recent review elsewhere, we can find no evidence able to support BDII as we can find no experiments in intact cells in which phospholipid bilayer diffusion was either varied independently or measured directly (although there are many papers where it was inferred by seeing a covariation of other dependent variables). By contrast, we find an abundance of evidence showing cases in which changes in the activities of named and genetically identified transporters led to measurable changes in the rate or extent of drug uptake. PBIN also has considerable predictive power, and accounts readily for the large differences in drug uptake between tissues, cells and species, in accounting for the metabolite-likeness of marketed drugs, in pharmacogenomics, and in providing a straightforward explanation for the late-stage appearance of toxicity and of lack of efficacy during drug discovery programmes despite macroscopically adequate pharmacokinetics. Consequently, the view that Phospholipid Bilayer diffusion Is Negligible (PBIN) provides a starting hypothesis for assessing cellular drug uptake that is much better supported by the available evidence, and is both more productive and more predictive.

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs

We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite. This suggests a 'rule of 0.5' mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This 'rule of 0.5' may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.

Solution-phase parallel synthesis of acyclic nucleoside libraries of purine, pyrimidine, and triazole acetamides

Molecular diversity plays a pivotal role in modern drug discovery against phenotypic or enzyme-based targets using high throughput screening technology. Under the auspices of the Pilot Scale Library Program of the NIH Roadmap Initiative, we produced and report herein a diverse library of 181 purine, pyrimidine, and 1,2,4-triazole-N-acetamide analogues which were prepared in a parallel high throughput solution-phase reaction format. A set of assorted amines were reacted with several nucleic acid N-acetic acids utilizing HATU as the coupling reagent to produce diverse acyclic nucleoside N-acetamide analogues. These reactions were performed using 24 well reaction blocks and an automatic reagent-dispensing platform under inert atmosphere. The targeted compounds were purified on an automated purification system using solid sample loading prepacked cartridges and prepacked silica gel columns. All compounds were characterized by NMR and HRMS, and were analyzed for purity by HPLC before submission to the Molecular Libraries Small Molecule Repository (MLSMR) at NIH. Initial screening through the Molecular Libraries Probe Production Centers Network (MLPCN) program, indicates that several analogues showed diverse and interesting biological activities.

Virtualizing the p-ANAPL library: a step towards drug discovery from African medicinal plants

Background

Natural products play a key role in drug discovery programs, both serving as drugs and as templates for the synthesis of drugs, even though the quantities and availabilities of samples for screening are often limitted.

Experimental approach

A current collection of physical samples of > 500 compound derived from African medicinal plants aimed at screening for drug discovery has been made by donations from several researchers from across the continent to be directly available for drug discovery programs. A virtual library of 3D structures of compounds has been generated and Lipinski’s “Rule of Five” has been used to evaluate likely oral availability of the samples.

Results

A majority of the compound samples are made of flavonoids and about two thirds (2/3) are compliant to the “Rule of Five”. The pharmacological profiles of thirty six (36) selected compounds in the collection have been discussed.

Conclusions and implications

The p-ANAPL library is the largest physical collection of natural products derived from African medicinal plants directly available for screening purposes. The virtual library is also available and could be employed in virtual screening campaigns.

AfroDb: a select highly potent and diverse natural product library from African medicinal plants

Computer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. We assess the bioactivity and "drug-likeness" of a relatively small but structurally diverse dataset (containing >1,000 compounds) from African medicinal plants, which have been tested and proven a wide range of biological activities. The geographical regions of collection of the medicinal plants cover the entire continent of Africa, based on data from literature sources and information from traditional healers. For each isolated compound, the three dimensional (3D) structure has been used to calculate physico-chemical properties used in the prediction of oral bioavailability on the basis of Lipinski's "Rule of Five". A comparative analysis has been carried out with the "drug-like", "lead-like", and "fragment-like" subsets, as well as with the Dictionary of Natural Products. A diversity analysis has been carried out in comparison with the ChemBridge diverse database. Furthermore, descriptors related to absorption, distribution, metabolism, excretion and toxicity (ADMET) have been used to predict the pharmacokinetic profile of the compounds within the dataset. Our results prove that drug discovery, beginning with natural products from the African flora, could be highly promising. The 3D structures are available and could be useful for virtual screening and natural product lead generation programs.

The Acid/Base Profile of the Human Metabolome and Natural Products

Human small molecule metabolites (the human metabolome) are a set of compounds that interact with at least one macromolecule in the biosphere. This study investigates the acid/base profile of the human metabolome, natural products and drugs, together with an analysis of their physicochemical properties. Ionisation constants (pKa values) are estimated for each compound and the identity of the ionisable functional groups in each set is determined. The acid/base and physicochemical property profile of the lipid component of the metabolome differed considerably to the other datasets. In contrast, the acid/base properties of non-lipid metabolites were found to be similar to both drugs and natural products. While the non-lipid metabolites have lower average ClogP values and more hydrogen bond donors than the other datasets, the distribution of physicochemical property values overlapped considerably with the drug dataset. Considering also that the non-lipid metabolites are of biochemical interest, their characteristics have great potential to influence the selection of screening compounds for drug discovery.

StreptomeDB: a resource for natural compounds isolated from Streptomyces species

Bacteria from the genus Streptomyces are very important for the production of natural bioactive compounds such as antibiotic, antitumour or immunosuppressant drugs. Around two-thirds of all known natural antibiotics are produced by these bacteria. An enormous quantity of crucial data related to this genus has been generated and published, but so far no freely available and comprehensive database exists. Here, we present StreptomeDB (http://www.pharmaceutical-bioinformatics.de/streptomedb/). To the best of our knowledge, this is the largest database of natural products isolated from Streptomyces. It contains >2400 unique and diverse compounds from >1900 different Streptomyces strains and substrains. In addition to names and molecular structures of the compounds, information about source organisms, references, biological role, activities and synthesis routes (e.g. polyketide synthase derived and non-ribosomal peptides derived) is included. Data can be accessed through queries on compound names, chemical structures or organisms. Extraction from the literature was performed through automatic text mining of thousands of articles from PubMed, followed by manual curation. All annotated compound structures can be downloaded from the website and applied for in silico screenings for identifying new active molecules with undiscovered properties.

Expanding medicinal chemistry space

Clinically useful drugs target a relatively small number of proteins that lie within a clearly defined and chemically accessible space. However, many high value biological targets lie outside this chemical space, and an ability to access such 'intractable' targets not amenable to traditional small molecule intervention would expand treatment options and be a major boost for patients and the pharmaceutical industry. To date, success has been limited but new technologies and approaches are beginning to emerge that could provide novel lead generation capabilities that enable access to new drug target classes. We review these new approaches and their ability to provide the novel leads needed to tackle a new generation of biological targets.