Retrosynthetic Combinatorial Analysis Procedure Research Articles

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

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

Site-directed Fragnomics and MD Simulations Approaches to Identify Interleukin-2 Inhibitors.

The aberrant expression of Interleukin-2 (IL2), the chief regulator of immunity, is associated with many auto-immune diseases. At present, there is no FDA approved drug targeting IL2, which puts forth the need for small molecular inhibitors to block IL2 and its receptor interaction. Herein, we used the contemporary fragnomics approach to design novel drug-like inhibitors targeting IL2. Briefly, the RECAP (Retrosynthetic Combinatorial Analysis Procedure) package implemented in MOE (Molecular Operating Environment check) software suite was utilised to obtain fragments fulfilling the 'rule of three' criteria for fragments. The binding site of IL2 was divided into three smaller grooves, and the fragments were docked to screen their affinity for a particular site, followed by site-directed RECAP synthesis. A focused library of 10,000 compounds was prepared by re-combining the fragments according to their affinity for a particular site as observed in docking. Docking and subsequent analysis of newly synthesised compounds identified 40 privileged leads, presenting hydrogen bonding with basic residues of the pocket. A QSAR model was implied to predict the IC50 of the compounds and to analyse the electrostatic and hydrophobic contour maps. The resulting hits were found to be modest IL2 inhibitors with predicted inhibitory activity in the range of 5.17-4.40 nM. Further Dynamic simulation studies were carried out to determine the stability of the inhibitor-IL2 complex. Our findings underline the potential of the novel compounds as valuable pharmacological agents in diseases characterised by IL2 overexpression.

Discovery of new potential CDK2/VEGFR2 type II inhibitors by fragmentation and virtual screening of natural products

Cyclin-Dependent Kinase 2 (CDK2) and Vascular Endothelial Growth Factor Receptor (VEGFR2) have largely been considered as attractive targets for developing anticancer agents. However, there is no dual inhibitor commercially available in the market that interacts simultaneously with the allosteric back pocket of these enzymes. We applied a combined computational strategy that started with the generation of two overlapping pharmacophore models of both kinases at ‘inactive’ conformation. Next, several virtual libraries of natural products, including the databases TCM (Traditional Chinese Medicine), UEFS (Universidade Estadual de Feira de Santana), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products) and AfroDb (African Medicinal Plants Database) were deconstructed using a non-extensive version of the approach RECAP (retrosynthetic combinatorial analysis procedure). These natural-product-derived fragments (NPDFs) were screened and merged into drug-sized compounds, which were filtered by Lipinski’s Rule-of-five (Ro5) and docking. As a result, two pharmacophore models, namely Hypo1 and Hypo2, were developed with an accuracy of 0.94 and 0.84, respectively. Deconstruction of natural products produced a set of 16655 unique non-extensive NPDFs that were screened against both pharmacophore models. Finally, after merging, Ro5-filtering and docking, we obtained a set of 20 hit compounds predicted to be diverse, developable, synthesizable and potent. The computational strategy proved successful to find virtual candidates of kinase inhibitors and therefore contributes to the identification of innovative multi-target compounds with potential anticancer activity. Communicated by Ramaswamy H. Sarma

Integrated In Silico Fragment-Based Drug Design: Case Study with Allosteric Modulators on Metabotropic Glutamate Receptor 5.

GPCR allosteric modulators target at the allosteric binding pockets of G protein-coupled receptors (GPCRs) with indirect influence on the effects of an orthosteric ligand. Such modulators exhibit significant advantages compared to the corresponding orthosteric ligands, including better chemical tractability or physicochemical properties, improved selectivity, and reduced risk of oversensitization towards their receptors. Metabotropic glutamate receptor 5 (mGlu5), a member of class C GPCRs, is a promising therapeutic target for treating many central nervous system diseases. The crystal structure of mGlu5 in the complex with the negative allosteric modulator mavoglurant was recently reported, providing a fundamental model for designing new allosteric modulators. Computational fragment-based drug discovery represents a powerful scaffold-hopping and lead structure-optimization tool for drug design. In the present work, a set of integrated computational methodologies was first used, such as fragment library generation and retrosynthetic combinatorial analysis procedure (RECAP) for novel compound generation. Then, the compounds generated were assessed by benchmark dataset verification, docking studies, and QSAR model simulation. Subsequently, structurally diverse compounds, with reported or unreported scaffolds, can be observed from top 20 in silico synthesized compounds, which were predicted to be potential mGlu5 modulators. In silico compounds with reported scaffolds may fill SAR holes in known, patented series of mGlu5 modulators. And the generation of compounds without reported tests on mGluR indicates that our approach is doable for exploring and designing novel compounds. Our case study of designing allosteric modulators on mGlu5 demonstrated that the established computational fragment-based approach is a useful methodology for facilitating new compound design in the future.

Matched molecular pairs derived by retrosynthetic fragmentation

Matched molecular pairs (MMPs) are defined as pairs of compounds that only differ by a chemical change at a single site. MMPs have become popular in medicinal chemistry to support lead optimization, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, and other applications. Thus far, MMPs have been algorithmically defined and not on the basis of reaction information. This often limits the chemical interpretability and practical utility of MMPs. Therefore, we introduce synthetically accessible MMPs that are automatically generated by applying reaction rules following the retrosynthetic combinatorial analysis procedure (RECAP). A library of more than 92 000 RECAP-MMPs was generated from public domain compounds active against 435 different targets exclusively utilizing high-confidence activity data. This library is made freely available for use in medicinal chemistry.

Rational, computer-aided design of multi-target ligands

Over the past two decades the “one drug – one target – one disease” concept became the prevalent paradigm in drug discovery. The main idea of this approach is the identification of a single protein target whose inhibition leads to a successful treatment of the examined disease. The predominant assumption is that highly selective ligands would avoid unwanted side effects caused by binding to secondary non-therapeutic targets. In recent years the results of post-genomic and network biology showed that proteins rarely act in isolated systems but rather as a part of a highly connected network [1]. In addition this connectivity leads to more robust systems that cannot be interfered by the inhibition of a single target of that network and consequently might not lead to the desired therapeutic effect [2]. Furthermore studies prove that robust systems are rather affected by weak inhibitions of several parts than by a complete inhibition of a single selected element of that system [3]. Therefore there is an increasing interest in developing drugs that take effect on multiple targets simultaneously but is concurrently a great challenge for medicinal chemists. There has to be a sufficient activity on each target as well as an adequate pharmacokinetic profile [4]. Early design strategies tried to link the pharmacophors of known inhibitors, however these methods often lead to high molecular weight and low ligand efficacy. We present a new rational approach based on a retrosynthetic combinatorial analysis procedure [5] on approved ligands of multiple targets. These RECAP fragments are used to design a large combinatorial library containing molecules featuring chemical properties of each ligand class. The molecules are further validated by machine learning models, like random forests and self-organizing maps, regarding their activity on the targets of interest.

Structure-guided fragment-based in silico drug design of dengue protease inhibitors

An in silico fragment-based drug design approach was devised and applied towards the identification of small molecule inhibitors of the dengue virus (DENV) NS2B-NS3 protease. Currently, no DENV protease co-crystal structure with bound inhibitor and fully formed substrate binding site is available. Therefore a homology model of DENV NS2B-NS3 protease was generated employing a multiple template spatial restraints method and used for structure-based design. A library of molecular fragments was derived from the ZINC screening database with help of the retrosynthetic combinatorial analysis procedure (RECAP). 150,000 molecular fragments were docked to the DENV protease homology model and the docking poses were rescored using a target-specific scoring function. High scoring fragments were assembled to small molecule candidates by an implicit linking cascade. The cascade included substructure searching and structural filters focusing on interactions with the S1 and S2 pockets of the protease. The chemical space adjacent to the promising candidates was further explored by neighborhood searching. A total of 23 compounds were tested experimentally and two compounds were discovered to inhibit dengue protease (IC(50)=7.7μM and 37.9μM, respectively) and the related West Nile virus protease (IC(50)=6.3μM and 39.0μM, respectively). This study demonstrates the successful application of a structure-guided fragment-based in silico drug design approach for dengue protease inhibitors providing straightforward hit generation using a combination of homology modeling, fragment docking, chemical similarity and structural filters.

Expanding and understanding metabolite space

In metabolomics the identity and role of low mass molecules called metabolites that are produced in cell metabolic processes are investigated. These make them valuable indicators of the phenotype of a biological system. The 'Metabolite Space' is the total chemical universe of metabolites present in all compartments and in all states from any organism. These molecules exhibit common features that form what can be called 'metabolite likeness'. Here, we focus on the human metabolite space, including both endogenous and exogenous (such as drug) metabolites. In order to analyze the 'Metabolite Space', we collected data from the Human Metabolome Database (HMDB) [1] which is a comprehensive database for human metabolites containing over 7000 compounds that were identified in several human biofluids and tissues. As there still remain many compounds to be identified that lay outside the boundaries of this known space, exploring this unknown region is crucial to evaluate 'metabolite likeness'. In order to expand 'Metabolite Space' in our approach we employed the Retrosynthetic Combinatorial Analysis Procedure (RECAP) [2] to generate new molecules that possess features similar to those present in metabolites, however in other (but still likely) rearrangements. We studied how discernible these new molecules are from real metabolites and, hence, whether synthetic organic chemistry reactions are indeed able to expand the known universe of metabolites. We further studied the new chemistry present in the expanded metabolite space by looking at Murcko assemblies [3], ring systems and other chemical properties. The new metabolite space is compared to other small molecules, such as those obtained from the ZINC database, that are not metabolites. By combining all the above analyses we expect to characterize better the metabolite space, and furthermore, to predict the metabolite-likeness of a molecule and to understand its immanent properties.

Advanced PLS Technique Focusing on Visualization and Chemical Interpretation - SOMPLS Analysis of Serine Protease Inhibitors -

In quantitative structure activity relationships (QSAR), partial least squares (PLS) are of particular interest as a statistical method. Since successful applications of PLS to QSAR data set, PLS has evolved for coping with more demands associated with complex data structures. Especially, PLS variants focusing on visualization and chemical interpretation are highly desirable for molecular design. In this paper, we employed the self-organized map PLS (SOMPLS) approach to predict multiple inhibitory activities against three serine protease receptors (Factor Xa, Tryptase and urokinase-type Plasminogen Activator (uPA)). Retrosynthetic Combinatorial Analysis Procedure (RECAP) fingerprints were used as chemical descriptors that express the existence of specific substructure in the molecule. From the SOMPLS analysis and the subsequent correlation map, essential fragments for each serine protease were easily identified. From the correlation map, we designed best combinations of fragments at each substituent position for each serine protease protein. The essential fragments could be validated from X-ray crystal structures of serine protease receptors in computer graphics. SOMPLS is an unique approach that makes data-mining feasible from visualization of structure-activity data biased to ligand-based view point.

ChemInform Abstract: RECAP — Retrosynthetic Combinatorial Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragments with Useful Applications in Combinatorial Chemistry.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

RECAP--retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry.

The use of combinatorial chemistry for the generation of new lead molecules is now a well established strategy in the drug discovery process. Central to the use of combinatorial chemistry is the design and availability of high quality building blocks which are likely to afford hits from the libraries that they generate. Herein we describe "RECAP" (Retrosynthetic Combinatorial Analysis Procedure), a new computational technique designed to address this building block issue. RECAP electronically fragments molecules based on chemical knowledge. When applied to databases of biologically active molecules this allows the identification of building block fragments rich in biologically recognized elements and privileged motifs and structures. This allows the design of building blocks and the synthesis of libraries rich in biological motifs. Application of RECAP to the Derwent World Drug Index (WDI) and the molecular fragments/ building blocks that this generates are discussed. We also describe a WDI fragment knowledge base which we have built which stores the drug motifs and mention its potential application in structure based drug design programs.