BREED: Generating Novel Inhibitors through Hybridization of Known Ligands. Application to CDK2, P38, and HIV Protease

Fragment-Based Discovery of Potent and Selective DDR1/2 Inhibitors

The DDR1 and DDR2 receptor tyrosine kinases are activated by extracellular collagen and have been implicated in a number of human diseases including cancer. We performed a fragment-based screen against DDR1 and identified fragments that bound either at the hinge or in the back pocket associated with the DFG-out conformation of the kinase. Modeling based on crystal structures of potent kinase inhibitors facilitated the "back-to-front" design of potent DDR1/2 inhibitors that incorporated one of the DFG-out fragments. Further optimization led to low nanomolar, orally bioavailable inhibitors that were selective for DDR1 and DDR2. The inhibitors were shown to potently inhibit DDR2 activity in cells but in contrast to unselective inhibitors such as dasatinib, they did not inhibit proliferation of mutant DDR2 lung SCC cell lines.

Fragment-based design of kinase inhibitors: a practical guide

Fragment-based drug design has become an important strategy for drug design and development over the last decade. It has been used with particular success in the development of kinase inhibitors, which are one of the most widely explored classes of drug targets today. The application of fragment-based methods to discovering and optimizing kinase inhibitors can be a complicated and daunting task; however, a general process has emerged that has been highly fruitful. Here a practical outline of the fragment process used in kinase inhibitor design and development is laid out with specific examples. A guide to the overall process from initial discovery through fragment screening, including the difficulties in detection, to the computational methods available for use in optimization of the discovered fragments is reported.

Computational methods for fragment-based ligand design: growing and linking

Fragment-based drug design has proved itself as a powerful technique for increasing the sampling and diversity of chemical space and enabling the design of novel leads and compounds. Computational techniques for identifying fragments, binding sites and particularly for linking, growing, and evolving fragments play a significant role in the process. Information from ADME studies and clustering property information in the form of toxicophores and chemotypes can play a significant role in aiding the design of novel, selective fragments with good activity profiles.

Novel alkylphospholipid-DTC hybrids as promising agents against endocrine related cancers acting via modulation of Akt-pathway

A new series of 2-(alkoxy(hydroxy)phosphoryloxy)ethyl dialkylcarbodithioate derivatives was synthesized and evaluated against endocrine related cancers, acting via modulation of Akt-pathway. Eighteen compounds were active at 7.24-100 μM against MDA-MB-231 or MCF-7 cell lines of breast cancer. Three compounds (14, 18 and 22) were active against MCF-7 cells at IC50 significantly better than miltefosine and most of the compounds were less toxic towards non-cancer cell lines, HEK-293. On the other hand, twelve compounds exhibited cell growth inhibiting activity against prostate cancer cell lines, either PC-3 or DU-145 at 14.69-95.20 μM. While nine of these were active against both cell lines. The most promising compounds 14 and 18 were about two and five fold more active than miltefosine against DU-145 and MCF-7 cell lines respectively and significantly down regulated phospho-Akt. Possibly anti-cancer and pro-apoptotic activity was mostly due to blockade of Akt-pathway.

Structure-based virtual screening approach for discovery of covalently bound ligands

We present a fast and effective covalent docking approach suitable for large-scale virtual screening (VS). We applied this method to four targets (HCV NS3 protease, Cathepsin K, EGFR, and XPO1) with known crystal structures and known covalent inhibitors. We implemented a customized "VS mode" of the Schrödinger Covalent Docking algorithm (CovDock), which we refer to as CovDock-VS. Known actives and target-specific sets of decoys were docked to selected X-ray structures, and poses were filtered based on noncovalent protein-ligand interactions known to be important for activity. We were able to retrieve 71%, 72%, and 77% of the known actives for Cathepsin K, HCV NS3 protease, and EGFR within 5% of the decoy library, respectively. With the more challenging XPO1 target, where no specific interactions with the protein could be used for postprocessing of the docking results, we were able to retrieve 95% of the actives within 30% of the decoy library and achieved an early enrichment factor (EF1%) of 33. The poses of the known actives bound to existing crystal structures of 4 targets were predicted with an average RMSD of 1.9 Å. To the best of our knowledge, CovDock-VS is the first fully automated tool for efficient virtual screening of covalent inhibitors. Importantly, CovDock-VS can handle multiple chemical reactions within the same library, only requiring a generic SMARTS-based predefinition of the reaction. CovDock-VS provides a fast and accurate way of differentiating actives from decoys without significantly deteriorating the accuracy of the predicted poses for covalent protein-ligand complexes. Therefore, we propose CovDock-VS as an efficient structure-based virtual screening method for discovery of novel and diverse covalent ligands.

Novel metronidazole-chalcone conjugates with potential to counter drug resistance in Trichomonas vaginalis

Trichomoniasis is the most prevalent, curable sexually transmitted disease (STD), which increases risk of viral STDs and HIV. However, drug resistance has been developed by some strains of Trichomonas vaginalis against Metronidazole (MTZ), the FDA approved drug against trichomoniasis. In the present study twenty two chalcone hybrids of metronidazole have been synthesized in a quest to get new molecules with higher potential against metronidazole-resistant T. vaginalis. All new hybrid molecules were found active against T. vaginalis with varying levels of activity against MTZ-susceptible and resistant strains. Eight compounds (4a, 4c, 4d, 4e, 4f, 4h, 4q and 4s) were found as active as the standard drug with an MIC of 1.56 μg/ml against MTZ-susceptible strain. However, compounds 4e, 4h and 4m were 4-times more active than MTZ against drug-resistant T. vaginalis, amongst which 4e and 4h were most promising against both susceptible and resistant strains.

Very large virtual compound spaces: construction, storage and utility in drug discovery

Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.

In silico study on binding specificity of gonadotropins and their receptors: design of a novel and selective peptidomimetic for human follicle stimulating hormone receptor

Gonadotropins bind to specific receptors in spite of sharing a high level of sequence and structural similarity. This specific binding is crucial for maintaining the reproductive health of an organism. In this study, residues that dictate the receptor binding specificity of the gonadotropins (FSH and LH) have been identified using combination of in silico methods. Docking studies (ZDOCK), based on the systematic replacement of these residues, confirmed its importance in receptor binding. An interesting observation is that the relative positioning of the residues conferring binding specificity varied for the gonadotropin-receptor complexes. This spatial difference of the key residues could be exploited for design of specific modulators. Based on the identified residues, we have rationally designed a peptidomimetic (FSHP) that displays good binding affinity and specificity for hFSHR. FSHP was developed by screening 3.9 million compounds using pharmacophore-shape similarity followed by fragment-based approach. It was observed that FSHP and hFSHâ can share the same receptor binding site thereby mimicking the native hFSHR-FSH interactions. FSHP also displayed higher binding affinity to hFSHR as compared to two reported hFSHR antagonists. MD simulation studies on hFSHR-FSHP complex revealed that FSHP is conformationally rigid and the intermolecular interactions are maintained during the course of simulation.

Novel kinase inhibitors by reshuffling ligand functionalities across the human kinome

Protein kinases remain among the most versatile and prospective therapeutic drug targets with currently 15 distinct compounds approved for use in humans and numerous clinical development programs. The vast majority of kinase inhibitors bind at the ATP site. Here we present an integrated workflow to amplify the rapidly increasing space of structurally resolved small molecule kinase ligands to generate novel inhibitors. Our approach considers both receptor-based similarity constraints in cocomplexes and ligand-based filtering/refinement methods to generate novel, drug-like matter. After building a comprehensive database of the structural kinome and identifying ATP-competitive ligands, we leverage local site similarities and site alignments to shuffle ligand fragments across the kinome. After extensive curation and standardization, our automated protocol starting from 936 cocrystal ATP-competitive binding sites generated about 150,000 new ligand structures among them over 26,000 lead-/drug-like compounds; the majority of those are novel based on structural similarity and scaffolds. In a retrospective analysis we demonstrate that our protocol produced known potent kinase inhibitors and we show how docking can be applied to prioritize the most likely efficacious compounds. Our workflow emulates a common strategy in medicinal chemistry to identify and swap corresponding moieties from known inhibitors to generate novel and potent leads. Here, we systematize and automate this approach leveraging available knowledge covering the entire human Kinome.

Exploring chemical space for drug discovery using the chemical universe database

Herein we review our recent efforts in searching for bioactive ligands by enumeration and virtual screening of the unknown chemical space of small molecules. Enumeration from first principles shows that almost all small molecules (>99.9%) have never been synthesized and are still available to be prepared and tested. We discuss open access sources of molecules, the classification and representation of chemical space using molecular quantum numbers (MQN), its exhaustive enumeration in form of the chemical universe generated databases (GDB), and examples of using these databases for prospective drug discovery. MQN-searchable GDB, PubChem, and DrugBank are freely accessible at www.gdb.unibe.ch.

LigMerge: a fast algorithm to generate models of novel potential ligands from sets of known binders

One common practice in drug discovery is to optimize known or suspected ligands in order to improve binding affinity. In performing these optimizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by altering certain chemical moieties of known/endogenous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically ‘swapping’ the chemical moieties of known ligands to generate novel ligands with potentially improved potency. To address this need, we have created a novel algorithm called ‘LigMerge’. LigMerge identifies the maximum (largest) common substructure of two three-dimensional ligand models, superimposes these two substructures, and then systematically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demonstrate the utility of LigMerge, we identify compounds predicted to inhibit peroxisome proliferator–activated receptor gamma, HIV reverse transcriptase, and dihydrofolate reductase with affinities higher than those of known ligands. We hope that LigMerge will be a helpful tool for the drug design community.

Metal-ligand interactions: an analysis of zinc binding groups using the Protein Data Bank

In the present study, we investigated zinc binding groups (ZBGs) using the coordinates of protein-ligand complex structures obtained from the Protein Data Bank. The distance from the zinc to the nearest ligand atom was measured to determine whether the atom was part of the ZBG. The most frequently found ZBG was carboxylate, followed by sulfonamide, hydroxamate, and phosphonate/phosphate. Because it was found that few heteroatoms, such as nitrogen, oxygen, and sulfur atoms, interacted with zinc, ideal distances between the zinc and these heteroatoms were identified. Whereas carboxylates bound to the zinc via both monodentate and bidentate interactions, the hydroxamates bound dominantly in a bidentate manner. These results will aid in the design of new inhibitors with the potential to interact with zinc in the target protein.

Potentiating Metronidazole Scaffold against Resistant Trichomonas: Design, Synthesis, Biology and 3D-QSAR Analysis

Metronidazole (MTZ), the FDA-approved drug against Trichomonas vaginalis (TV), is being challenged seriously by drug resistance, while its inertness to sperm makes it ineffective as a vaginal contraceptive. Thirteen piperidine dithiocarbamate hybrids of 2-(2-methyl-5-nitro-1H-imidazol-1-yl)ethane (8-20) were designed to potentiate the MTZ framework against drug resistance and sperm. New compounds were 1.2-12.1 times more effective against MTZ-susceptible and -resistant strains of TV. All of the compounds exhibited high safety toward cervical (HeLa) cells and Lactobacillus. Thirty-eight compounds were scrutinized by CoMFA and CoMSIA techniques of 3D quantitative structure-activity relationship. Good predictive r pred (2) values for CoMFA and CoMSIA models reflected the robustness of the predictive ability. This was validated by designing five new analogues (46-50), which were potently microbicidal (3-10 and 10-20 times against MTZ-susceptible and -resistant TV, respectively) and spermicidal. This in vitro study may have significant clinical relevance, which could become evident in due course.

Designing antimicrobial peptides: form follows function

Multidrug-resistant bacteria are a severe threat to public health. Conventional antibiotics are becoming increasingly ineffective as a result of resistance, and it is imperative to find new antibacterial strategies. Natural antimicrobials, known as host defence peptides or antimicrobial peptides, defend host organisms against microbes but most have modest direct antibiotic activity. Enhanced variants have been developed using straightforward design and optimization strategies and are being tested clinically. Here, we describe advanced computer-assisted design strategies that address the difficult problem of relating primary sequence to peptide structure, and are delivering more potent, cost-effective, broad-spectrum peptides as potential next-generation antibiotics.

Computationally-guided optimization of a docking hit to yield catechol diethers as potent anti-HIV agents

A 5-μM docking hit has been optimized to an extraordinarily potent (55 pM) non-nucleoside inhibitor of HIV reverse transcriptase. Use of free energy perturbation (FEP) calculations to predict relative free energies of binding aided the optimizations by identifying optimal substitution patterns for phenyl rings and a linker. The most potent resultant catechol diethers feature terminal uracil and cyanovinylphenyl groups. A halogen bond with Pro95 likely contributes to the extreme potency of compound 42. In addition, several examples are provided illustrating failures of attempted grafting of a substructure from a very active compound onto a seemingly related scaffold to improve its activity.

The benefits of constructing leads from fragment hits

‘Fragments’ refer to particularly small molecular starting points in medicinal chemistry. The small size of fragments requires adapted techniques for their screening and subsequent elaboration. The detection of the weak binding affinity of fragments for their target, and associated screening issues, have been debated at length. Since it is now clear that fragments can be developed into clinical candidates, the discussion is shifting to the design of good-quality lead compounds from fragment hits. The increasing ability to control and tailor this construction process highlights the potential benefits of fragment-based drug discovery.

De novo drug design

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

Fragment-based approaches and computer-aided drug discovery

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in "fragment space" has boosted computational biology and chemistry.

MORPH: a new tool for ligand design

A frequently employed strategy in drug discovery efforts is to replace aromatic rings in known active compounds with alternative aromatic moieties to create novel compounds with improved potency and/or adsorption, distribution, metabolism, excretion, and toxicity properties. Here we introduce MORPH, which is a simple software tool for systematically modifying aromatic rings in three-dimensional models of molecules without altering the coordinates of the nonhydrogen atoms in the rings. MORPH works on individual rings as well as fused ring systems and additionally provides the ability to filter out modified compounds which do not contain hydrogen-bond donors or acceptors at specific positions on the rings or contain more or less than the desired number of heteroatoms. The MORPH program and its application to two ligands extracted from cocrystal structures with cyclin-dependent kinase 2 (CDK2)/cyclin A and CDK2 are discussed below.

Molecular shape and medicinal chemistry: a perspective

The eight contributions here provide ample evidence that shape as a volume or as a surface is a vibrant and useful concept when applied to drug discovery. It provides a reliable scaffold for "decoration" with chemical intuition (or bias) for virtual screening and lead optimization but also has its unadorned uses, as in library design, ligand fitting, pose prediction, or active site description. Computing power has facilitated this evolution by allowing shape to be handled precisely without the need to reduce down to point descriptors or approximate metrics, and the diversity of resultant applications argues for this being an important step forward. Certainly, it is encouraging that as computation has enabled our intuition, molecular shape has consistently surprised us in its usefulness and adaptability. The first Aurelius question, "What is the essence of a thing?", seems well answered, however, the third, "What do molecules do?", only partly so. Are the topics covered here exhaustive, or is there more to come? To date, there has been little published on the use of the volumetric definition of shape described here as a QSAR variable, for instance, in the prediction or classification of activity, although other shape definitions have been successful applied, for instance, as embodied in the Compass program described above in "Shape from Surfaces". Crystal packing is a phenomenon much desired to be understood. Although powerful models have been applied to the problem, to what degree is this dominated purely by the shape of a molecule? The shape comparison described here is typically of a global nature, and yet some importance must surely be placed on partial shape matching, just as the substructure matching of chemical graphs has proved useful. The approach of using surfaces, as described here, offers some flavor of this, as does the use of metrics that penalize volume mismatch less than the Tanimoto, e.g., Tversky measures. As yet, there is little to go on as to how useful a paradigm this will be because there is less software and fewer concrete results.Finally, the distance between molecular shapes, or between any shapes defined as volumes or surfaces, is a metric property in the mathematical sense of the word. As yet, there has been little, if any, application of this observation. We cannot know what new application to the design and discovery of pharmaceuticals may yet arise from the simple concept of molecular shape, but it is fair to say that the progress so far is impressive.

PhDD: a new pharmacophore-based de novo design method of drug-like molecules combined with assessment of synthetic accessibility

This account describes a new pharmacophore-based de novo design method of drug-like molecules (PhDD). The method PhDD first generates a set of new molecules that completely conform to the requirements of a given pharmacophore model, followed by a series of assessments to the generated molecules, including assessments of drug-likeness, bioactivity, and synthetic accessibility. PhDD is tested on three typical examples, namely, pharmacophore hypotheses of histone deacetylase (HDAC), cyclin-dependent kinase 2 (CDK2) and HIV-1 integrase (IN) inhibitors. The test results demonstrate that PhDD is able to generate molecules with novel structures but having similar biological functions with existing inhibitors. The validity of PhDD together with its ability of assessing synthetic accessibility makes it a useful tool in rational drug design.

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.