Identification of Natural-Product-Derived Inhibitors of 5-Lipoxygenase Activity by Ligand-Based Virtual Screening

N-alkylamides from Litsea cubeba (Lour.) Pers. with potential anti-inflammatory activity

Six amides, including a new N-alkylamide (1), four known N-alkylamides (2-5) and one nicotinamide (6) were isolated from Litsea cubeba (Lour.) Pers., which is a pioneer herb traditionally utilized in medicine. Their structures were elucidated on the basis of 1D and 2D NMR experiments and by comparison of their spectroscopic and physical data with the literature values. Cubebamide (1) is a new cinnamoyltyraminealkylamide and possessed obvious anti-inflammatory activity against NO production with IC50 values of 18.45 μM. Further in-depth pharmacophore-based virtual screening and molecular docking were carried out to reveal the binding mode of the active compound inside the 5-LOX enzyme. The results indicate that L. cubeba, and the isolated amides might be useful in the development of lead compounds for the prevention of inflammatory diseases.

Molecular medicinal insights into scaffold hopping-based drug discovery success

In both academia and the pharmaceutical industry, innovative hypotheses, methodologies and technologies that can shorten the drug research and development, leading to higher success rates, are vital. In this review, we demonstrate how innovative variations of the scaffold-hopping strategy have been used to create new druggable molecular spaces, drugs, clinical candidates, preclinical candidates, and bioactive agents. We also analyze molecular modulations that enabled improvements of the pharmacodynamic (PD), physiochemical, and pharmacokinetic (PK) properties (P3 properties) of the drugs resulting from these scaffold-hopping strategies.

Computational Bioactivity Fingerprint Similarities To Navigate the Discovery of Novel Scaffolds

As one of the central tasks of modern medicinal chemistry, scaffold hopping is expected to lead to the discovery of structural novel biological active compounds and broaden the chemical space of known active compounds. Here, we report the computational bioactivity fingerprint (CBFP) for easier scaffold hopping, where the predicted activities in multiple quantitative structure-activity relationship models are integrated to characterize the biological space of a molecule. In retrospective benchmarks, the CBFP representation shows outstanding scaffold hopping potential relative to other chemical descriptors. In the prospective validation for the discovery of novel inhibitors of poly [ADP-ribose] polymerase 1, 35 predicted compounds with diverse structures are tested, 25 of which show detectable growth-inhibitory activity; beyond this, the most potent (compound 6) has an IC₅₀ of 0.263 nM. These results support the use of CBFP representation as the bioactivity proxy of molecules to explore uncharted chemical space and discover novel compounds.

Identification of potential antivirals against SARS-CoV-2 using virtual screening method

SARS-CoV-2 has triggered a major epidemic among people around the world, and it is the newest in the sequence to become prevalent among other infectious diseases. The drug repurposing concept has been utilized effectively for numerous viral infections. Considering the situation and the urgency, the idea of drug repurposing for coronavirus infection (COVID-19) is also being studied. The molecular docking method was used for the screening of 29 antiviral drugs against primary protease proteins (MPP) of SARS-CoV-2, spike ecto-domain, spike receptor binding domain, Nsp9 RNA binding protein, and HR2 domain. Among these drugs, in terms of least binding energy, Indinavir, Sorivudine, Cidofovir, and Darunavir showed minimum docking scores with all the key proteins. For ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis, the ADMET properties of the top 4 drug candidates were retrieved through literature study. This analysis revealed that these drug candidates are well metabolized, distributed, and bioavailable, but have some undesirable effects. Furthermore, some approved structural analogues, such as Telbivudine, Tenofovir, Amprenavir, Fosamprenavir, etc., were predicted as similar drugs which may also be used for treating viral infections. We highly recommend these drug candidates as potential fighters against the deadly SARS-CoV-2 virus, and suggest in vivo trials for experimental validation of our findings.

Muricazine, a new hydrazine derivative from Ranunculus muricatus L. with antioxidant, lipoxygenase and urease inhibitory activities

Ranunculus muricatus L., an important member of family Ranunculaceae upon submission to phytochemical studies, led to the isolation of a novel natural hydrazine derivative, muricazine (1). Chemical structure of the compound was established with the aid of advanced spectroscopic techniques. It was evaluated for in vitro antioxidant, lipoxygenase, and urease (jack-bean) inhibitory activities. Results suggested that compound 1 could scavenge the DPPH free radical (42.1 ± 0.12 μM) to a great extent as compared to the standard (40.6 ± 0.91 μM). However, it showed moderate inhibitory potential against lipoxygenase (65.2 ± 0.45 μM) and urease (54.8 ± 0.23 μM) enzymes.

QSAR Classification-Based Virtual Screening Followed by Molecular Docking Identification of Potential COX-2 Inhibitors in a Natural Product Library

Developments of natural inhibitors to prevent the function of cyclooxygenase-2 (COX-2) protein, responsible for a variety of inflammations and cancers, are a major challenge in the scientific community. In this study, robust QSAR classification models for predicting COX-2 inhibitor were developed, by which the self-organizing feature map neural network and random forest (RF) were adopted to improve the prediction of classification model ability. The F-score-based criterion combined with RF was used for feature selection, and good performance for COX-2 inhibitor prediction in overall accuracy was demonstrated. We used this model as a virtual screening tool for identifying the potential COX-2 inhibitor from a natural product library and found potential hit compounds. This compound further screened by applying molecular docking simulation identified five potential hits such as osthole, kavain, vanillyl acetone, myristicin, and psoralen, having a comparable binding affinity to COX-2 protein. However, in cell experiment, three hit compounds revealed COX-2 inhibitory activity in mRNA and protein level such as osthole, kavain, and psoralen.

QSAR classification-based virtual screening followed by molecular docking studies for identification of potential inhibitors of 5-lipoxygenase

Developments of novel inhibitors to prevent the function of 5-lipoxygenase (5-LOX) proteins that are responsible for a variety of inflammatory and allergic disease are a major challenge in the scientific community. In this study, robust QSAR classification models for predicting 5-LOX activity were developed using machine learning algorithms. The Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbour (NN) and Decision Trees were adopted to improve the prediction ability of the classification models. The most informative molecular descriptors that contribute to the prediction of 5-LOX activity are screened from e-Dragon, Ochem, PowerMV and Combined databases using Filter-based feature selection methods such as Correlation Feature Selection (CFS) and Information Gain (IG). Performances of the models were measured by 5-fold cross-validation and external test sets prediction. Evaluation of performance of feature selection revealed that the CFS method outperforms the IG method for all descriptor databases except for PowerMV database. The best ensemble classification model was obtained with the IG filtered 'PowerMV' descriptor database using kNN (k = 5) algorithm which displayed an overall accuracy of 76.6% for the training set and 77.9% for the test set. Finally, we employed this model as a virtual screening tool for identifying potential 5-LOX inhibitors from the e-Drug3D drug database and found 43 potential hit candidates. This top screened hits containing one known 5-LOX inhibitors zileuton as well as novel scaffolds. These compounds further screened by applying molecular docking simulation and identified four potential hits such as Belinostat, Masoprocol, Mefloquine and Sitagliptin having a comparable binding affinity to zileuton.

Rational Drug Design Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Synthesis of α-santonin derivatives for diminutive effect on T and B-cell proliferation and their structure activity relationships

A new library of 20 compounds from α-santonin was synthesized and tested against Con-A induced T-cell proliferation and LPS-induced B-cell proliferation via MTT assay. The study resulted in the identification of potent immunosuppressant molecules, which were further screened along with α-santonin for Tumor Necrosis Factor Alpha (TNF-α) inhibitory activity. One of the molecules (7) at 10 μM showed equipotency to that of dexamethasone (1 μM conc.) used as a standard. Structure activity relationships of the synthesized compounds along with our earlier reported α-santonin derivatives have been studied. Inferences from the modifications carried out at all the three sites of α-santonin have been elaborated. Computational study of the active compounds shows TNF-α protein as its preferable target rather than Inosine Monophosphate Dehydrogenase (IMPDH).

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Computationally Guided Identification of Novel Mycobacterium tuberculosis GlmU Inhibitory Leads, Their Optimization, and in Vitro Validation

Mycobacterium tuberculosis (Mtb) infections are causing serious health concerns worldwide. Antituberculosis drug resistance threatens the current therapies and causes further need to develop effective antituberculosis therapy. GlmU represents an interesting target for developing novel Mtb drug candidates. It is a bifunctional acetyltransferase/uridyltransferase enzyme that catalyzes the biosynthesis of UDP-N-acetyl-glucosamine (UDP-GlcNAc) from glucosamine-1-phosphate (GlcN-1-P). UDP-GlcNAc is a substrate for the biosynthesis of lipopolysaccharide and peptidoglycan that are constituents of the bacterial cell wall. In the current study, structure and ligand based computational models were developed and rationally applied to screen a drug-like compound repository of 20,000 compounds procured from ChemBridge DIVERSet database for the identification of probable inhibitors of Mtb GlmU. The in vitro evaluation of the in silico identified inhibitor candidates resulted in the identification of 15 inhibitory leads of this target. Literature search of these leads through SciFinder and their similarity analysis with the PubChem training data set (AID 1376) revealed the structural novelty of these hits with respect to Mtb GlmU. IC50 of the most potent identified inhibitory lead (5810599) was found to be 9.018 ± 0.04 μM. Molecular dynamics (MD) simulation of this inhibitory lead (5810599) in complex with protein affirms the stability of the lead within the binding pocket and also emphasizes on the key interactive residues for further designing. Binding site analysis of the acetyltransferase pocket with respect to the identified structural moieties provides a thorough analysis for carrying out the lead optimization studies.

Discovery of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach

Mycobacterium tuberculosis bacteria cause deadly infections in patients [Corrected]. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease. M. tuberculosis proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with IC₅₀ values ranging from 35.32 to 64.15 μM on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.

Rational Drug Design

For the past three decades rationale drug design (RDD) has been developing as an innovative, rapid and successful way to discover new drug candidates. Many strategies have been followed and several targets with diverse structures and different biological roles have been investigated. Despite the variety of computational tools available, one can broadly divide them into two major classes that can be adopted either separately or in combination. The first class involves structure-based drug design, when the target's 3-dimensional structure is available or it can be computationally generated using homology modeling. On the other hand, when only a set of active molecules is available, and the structure of the target is unknown, ligand-based drug design tools are usually used. This review describes some recent advances in rational drug design, summarizes a number of their practical applications, and discusses both the advantages and shortcomings of the various techniques used.

Recent advances in the search for novel 5-lipoxygenase inhibitors

5-Lipoxygenase (5-LO) is an important enzyme of the arachidonic acid cascade and catalyses with the help of FLAP, the 5-LO-activating protein, the formation of bioactive leukotrienes (LTs). LTs are inflammatory mediators playing a pathophysiological role in different diseases such as asthma, allergic rhinitis as well as cardiovascular diseases and certain types of cancer. Up to now, only one 5-LO inhibitor is on the market, zileuton for the treatment of asthma. With the rising number of indications for anti-LT therapy, 5-LO inhibitor drug development becomes more and more important. This MiniReview gives an update on 5-LO inhibitors currently under clinical development. Furthermore, the recent advances in the search for novel 5-lipoxygenase inhibitors with a focus on computational methods are summarized. Currently, licofelone is the compound with the highest clinical development status (completed phase III trials). 5-LO inhibitor screening programmes based on computational methods could deliver several promising drug-like new molecules. These activities can be expected to be driven by the newly resolved structure of human 5-LO in the future, enabling structure-based drug design. For the prospective drugs in late-stage clinical development, the future will show their clinical safety and efficacy in the particular diseases.

Significance estimation for sequence-based chemical similarity searching (PhAST) and application to AuroraA kinase inhibitors

BACKGROUND

Chemical similarity searching allows the retrieval of preferred screening molecules from a compound database. Candidates are ranked according to their similarity to a reference compound (query). Assessing the statistical significance of chemical similarity scores helps prioritizing significant hits, and identifying cases where the database does not contain any promising compounds.

METHOD

Our text-based similarity measure, Pharmacophore Alignment Search Tool (PhAST), employs pair-wise sequence alignment. We adapted the concept of E-values as significance estimates and employed a sampling technique that incorporates the principle of importance sampling in a Markov chain Monte Carlo simulation to generate distributions of random alignment scores. These distributions were used to compute significance estimates for similarity scores in a preliminary prospective virtual screen for inhibitors of Aurora A kinase.

CONCLUSION

Assessing the significance of compound similarity computed with PhAST allows for a statistically motivated identification of candidate screening compounds. Inhibitors of Aurora A kinase were retrieved from a large compound library.

Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors

Lipoxygenases (LOXs) are non-heme iron containing dioxygenases involved in the oxygenation of polyunsaturated fatty acids (PUFAs) such as arachidonic acid (AA). Depending on the position of insertion of oxygen, LOXs are classified into 5-, 8-, 9-, 12- and 15-LOX. Among these, 5-LOX is the most predominant isoform associated with the formation of 5-hydroperoxyeicosatetraenoic acid (5-HpETE), the precursor of non-peptido (LTB₄) and peptido (LTC₄, LTD₄, and LTE₄) leukotrienes. LTs are involved in inflammatory and allergic diseases like asthma, ulcerative colitis, rhinitis and also in cancer. Consequently 5-LOX has become target for the development of therapeutic molecules for treatment of various inflammatory disorders. Zileuton is one such inhibitor of 5-LOX approved for the treatment of asthma.

In the recent times, computer aided drug design (CADD) strategies have been applied successfully in drug development processes. A comprehensive review on structure based drug design strategies in the development of novel 5-LOX inhibitors is presented in this article. Since the crystal structure of 5-LOX has been recently solved, efforts to develop 5-LOX inhibitors have mostly relied on ligand based rational approaches. The present review provides a comprehensive survey on these strategies in the development of 5-LOX inhibitors.

Discovery of a natural product-like c-myc G-quadruplex DNA groove-binder by molecular docking

The natural product-like carbamide (1) has been identified as a stabilizer of the c-myc G-quadruplex through high-throughput virtual screening. NMR and molecular modeling experiments revealed a groove-binding mode for 1. The biological activity of 1 against the c-myc G-quadruplex was confirmed by its ability to inhibit Taq polymerase-mediated DNA extension and c-myc expression in vitro, demonstrating that 1 is able to control c-myc gene expression at the transcriptional level presumably through the stabilization of the c-myc promoter G-quadruplex. Furthermore, the interaction between carbamide analogues and the c-myc G-quadruplex was also investigated by in vitro experiments in order to generate a brief structure-activity relationship (SAR) for the observed potency of carbamide 1.

Classification of scaffold-hopping approaches

The general goal of drug discovery is to identify novel compounds that are active against a preselected biological target with acceptable pharmacological properties defined by marketed drugs. Scaffold hopping has been widely applied by medicinal chemists to discover equipotent compounds with novel backbones that have improved properties. In this article we classify scaffold hopping into four major categories, namely heterocycle replacements, ring opening or closure, peptidomimetics and topology-based hopping. We review the structural diversity of original and final scaffolds with respect to each category. We discuss the advantages and limitations of small, medium and large-step scaffold hopping. Finally, we summarize software that is frequently used to facilitate different kinds of scaffold-hopping methods.

From Molecular Docking to 3D-Quantitative Structure-Activity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors

Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors.

Remodelling of the natural product fumagillol employing a reaction discovery approach

In the search for new biologically active molecules, diversity-oriented synthetic strategies break through the limitation of traditional library synthesis by sampling new chemical space. Many natural products can be regarded as intriguing starting points for diversity-oriented synthesis, wherein stereochemically rich core structures may be reorganized into chemotypes that are distinctly different from the parent structure. Ideally, to be suited to library applications, such transformations should be general and involve few steps. With this objective in mind, the highly oxygenated natural product fumagillol has been successfully remodelled in several ways using a reaction-discovery-based approach. In reactions with amines, excellent regiocontrol in a bis-epoxide opening/cyclization sequence can be obtained by size-dependent interaction of an appropriate catalyst with the parent molecule, forming either perhydroisoindole or perhydroisoquinoline products. Perhydroisoindoles can be further remodelled by cascade processes to afford either morpholinone or bridged 4,1-benzoxazepine-containing structures.

Using cheminformatics to find simulants for chemical warfare agents

Direct experimentation with chemical warfare agents (CWA) to study important problems such as their permeation across protective barrier materials, decontamination of equipment and facilities, or the environmental transport and fate of CWAs is not feasible because of the obvious toxicity of the CWAs and associated restrictions on their laboratory use. The common practice is to use "simulants," namely, analogous chemicals that closely resemble the CWAs but are less toxic, with the expectation that the results attained for simulants can be correlated to how the CWAs would perform. Simulants have been traditionally chosen by experts, by means of intuition, using similarity in one or more physical properties (such as vapor pressure or aqueous solubility) or in the molecular structural features (such as functional groups) between the stimulant and the CWA. This work is designed to automate the simulant identification process backed by quantitative metrics, by means of chemical similarity search software routinely used in pharmaceutical drug discovery. The question addressed here is: By the metrics of such software, how similar are traditional simulants to CWAs? That is, what is the numerical "distance" between each CWA and its customary simulants in the quantitative space of molecular descriptors? The answers show promise for finding close but less toxic simulants for the ever-increasing numbers of CWAs objectively and fast.

Novel human mPGES-1 inhibitors identified through structure-based virtual screening

Microsomal prostaglandin E synthase-1 (mPGES-1) is an inducible prostaglandin E synthase after exposure to pro-inflammatory stimuli and, therefore, represents a novel target for therapeutic treatment of acute and chronic inflammatory disorders. It is essential to identify mPGES-1 inhibitors with novel scaffolds as new leads or hits for the purpose of drug design and discovery that aim to develop the next-generation anti-inflammatory drugs. Herein we report novel mPGES-1 inhibitors identified through a combination of large-scale structure-based virtual screening, flexible docking, molecular dynamics simulations, binding free energy calculations, and in vitro assays on the actual inhibitory activity of the computationally selected compounds. The computational studies are based on our recently developed three-dimensional (3D) structural model of mPGES-1 in its open state. The combined computational and experimental studies have led to identification of new mPGES-1 inhibitors with new scaffolds. In particular, (Z)-5-benzylidene-2-iminothiazolidin-4-one is a promising novel scaffold for the further rational design and discovery of new mPGES-1 inhibitors. To our best knowledge, this is the first time a 3D structural model of the open state mPGES-1 is used in structure-based virtual screening of a large library of available compounds for the mPGES-1 inhibitor identification. The positive experimental results suggest that our recently modeled trimeric structure of mPGES-1 in its open state is ready for the structure-based drug design and discovery.

Inhibitors of SARS-3CLpro: virtual screening, biological evaluation, and molecular dynamics simulation studies

SARS-CoV from the coronaviridae family has been identified as the etiological agent of Severe Acute Respiratory Syndrome (SARS), a highly contagious upper respiratory disease that reached epidemic status in 2002. SARS-3CL(pro), a cysteine protease indispensible to the viral life cycle, has been identified as one of the key therapeutic targets against SARS. A combined ligand and structure-based virtual screening was carried out against the Asinex Platinum collection. Multiple low micromolar inhibitors of the enzyme were identified through this search, one of which also showed activity against SARS-CoV in a whole cell CPE assay. Furthermore, multinanosecond explicit solvent simulations were carried out using the docking poses of the identified hits to study the overall stability of the binding site interactions as well as identify important changes in the interaction profile that were not apparent from the docking study. Cumulative analysis of the evaluated compounds and the simulation studies led to the identification of certain protein-ligand interaction patterns which would be useful in further structure based design efforts.

Rationalizing the role of SAR tolerance for ligand-based virtual screening

It is well appreciated that the results of ligand-based virtual screening (LBVS) are much influenced by methodological details, given the generally strong compound class dependence of LBVS methods. It is less well understood to what extent structure-activity relationship (SAR) characteristics might influence the outcome of LBVS. We have assessed the hypothesis that the success of prospective LBVS depends on the SAR tolerance of screening targets, in addition to methodological aspects. In this context, SAR tolerance is rationalized as the ability of a target protein to specifically interact with series of structurally diverse active compounds. In compound data sets, SAR tolerance articulates itself as SAR continuity, i.e., the presence of structurally diverse compounds having similar potency. In order to analyze the role of SAR tolerance for LBVS, activity landscape representations of compounds active against 16 different target proteins were generated for which successful LBVS applications were reported. In all instances, the activity landscapes of known active compounds contained multiple regions of local SAR continuity. When analyzing the location of newly identified LBVS hits and their SAR environments, we found that these hits almost exclusively mapped to regions of distinct local SAR continuity. Taken together, these findings indicate the presence of a close link between SAR tolerance at the target level, SAR continuity at the ligand level, and the probability of LBVS success.

State-of-the-art in ligand-based virtual screening

Virtual screening is a much discussed topic in chemoinformatics and medicinal chemistry, and widely applied in pharmaceutical research. Here, we provide an in-depth analysis of currently available ligand-based virtual screening applications. We formulate several scientific quality criteria for prospective ligand-based virtual screens and analyze, in detail, the information provided by currently available peer-reviewed publications. The results presented herein provide a detailed view of the current state-of-the-art in this field and point at several problematic issues but also opportunities for further advances.

Current strategies for drug discovery through natural products

IMPORTANCE TO THE FIELD

Natural products are the most consistently successful source of drug leads, both historically and currently. Despite this, the use of natural products in industrial drug discovery has fallen out of favour. Natural products are likely to continue to be sources of new commercially viable drug leads because the chemical novelty associated with natural products is higher than that of any other source: this is particularly important when searching for lead molecules against newly discovered targets for which there are no known small molecule leads. Areas to be covered: Current drug discovery strategies involving natural products are described in three sections: developments from traditionally used medicines, random testing of natural compounds on biological assays and use of virtual screening techniques with structures of natural products.

WHAT THE READER WILL GAIN

The reader will gain an insight into the potential for natural products in current drug discovery paradigms, particularly in the value of using natural products in virtual screening approaches.

TAKE HOME MESSAGE

Drug discovery would be enriched if fuller use was made of the chemistry of natural products.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.

Scaffold diversity of natural products: inspiration for combinatorial library design

Natural products contain scaffold structures that can be systematically exploited for the design of combinatorial compound libraries with druglike properties. We review approaches for scaffold identification, and compare properties and pharmacophoric features of drugs and natural products. In particular, an application of the self-organizing map technique is presented for natural product-derived compound and library design.

Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization

We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

Scaffold hopping in drug discovery using inductive logic programming

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.

Natural Products in Parallel Chemistry––Novel 5-Lipoxygenase Inhibitors from BIOS-Based Libraries Starting from α-Santonin

Recently, we developed a concept known as biology-oriented synthesis (BIOS), which targets the design and synthesis of small- to medium-sized compound libraries on the basis of genuine natural product templates to provide screening compounds with high biological relevance. We herein describe the parallel solution phase synthesis of two BIOS-based libraries starting from alpha-santonin (1). Modification of the sesquiterpene lactone 1 by introduction of a thiazole moiety followed by a Lewis-acid-mediated lactone opening yielded a first library of natural product analogues. An acid-mediated dienone-phenol rearrangement of 1 and a subsequent etherification/amidation sequence led to a second natural product-based library. After application of a fingerprint-based virtual screening on these compounds, the biological screening of 23 selected library members against 5-lipoxygenase resulted in the discovery of four potent novel inhibitors of this enzyme.