Application of Spark to the discovery of novel LpxC inhibitors

Novartis Institutes for Biomedical Research recently published the paper Application of Virtual Screening to the Identification of New LpxC Inhibitor Chemotypes, Oxazolidinone and Isoxazoline. They report using Spark™, Cresset’s scaffold hopping application, to find core replacements for the indazole moiety of compound 6 in Figure 1. Visual selection of the most promising top-ranking Spark results and further optimization studies led to the identification of novel LpxC inhibitors with subnanomolar binding to LpxC and in vivo antibacterial activity against P. aeruginosa and other Gram-negative bacteria.

Scaffold hopping

The bioactive conformation of compound 26, a simplified version of compound 6 originally reported by Actelion, was used as the starter molecule for the Spark scaffold hopping experiment. Aim of the search was to identify appropriate replacements of the indazole core (Figure 1).


Figure 1. Top: LpxC inhibitor 6. Bottom: The bioactive conformation of compound 26 was used as the starter molecule for the scaffold hopping experiment with Spark.

The allowed atom types for the ‘linker 1 atom’ and ‘atom 2’ (see Figure 2) were set respectively to ‘any carbon atom’ and ‘any atom’. It was further specified that all Spark designs must contain at least 1 ring and should not include any reactive functionalities. The Spark experiment was performed on fragment databases derived from ZINC,1 ChEMBL,2 and the VEHICLe3 collection of theoretical ring systems.

The similarity score of the Spark results towards compound 26 was calculated using 50% field and 50% shape similarity. The 100 top ranking clusters were manually reviewed with respect to synthetic feasibility, introduction of hydrophilic groups in the area of the indazole moiety and calculated physicochemical properties.


Figure 2. Allowed atom types for ‘linker 1 atom’ and ‘atom 2’  in the Spark experiment.

The oxazolidinone 13 and isoxazoline 25 scaffolds were shortlisted among several proposals which link ‘linker 1 atom’ and ‘atom 2’ with an hydrophilic linker (Figure 3).

Analogues of 13 and 25, where the methoxy group in para position was replaced by a bromine atom (Figure 3) showed cellular activity with Minimal Inhibitory Concentrations (MIC) values below 4 μg/mL against P. aeruginosa. Accordingly, these series were selected for further investigation.


Figure 3. Oxazolidinone 13a and isoxazoline 25a showed  MIC <4 μg/mL against P. aeruginosa.

SAR optimization

Further investigation and expansion of both oxazolidinone and isoxazoline series led to the identification of compounds with potent in vitro activity against P. aeruginosa and other Gram-negative bacteria. Representative compound 13f (Figure 4) demonstrated excellent efficacy against P. aeruginosa in an in vivo mouse neutropenic thigh infection model.

The crystal structure of 13f complexed with the P. aeruginosa LpxC enzyme (PDB: 6MAE) shows that the hydroxamic acid moiety is bound to the zinc atom in the active site, and involved in interactions with H78, H237, T190, E77, and D241 (Figure 4). The hydrophobic tail (phenyl group) interacts with several hydrophobic side chains, and the cyclopropyl group further extends to the solvent exposed region. The sulfone oxygen atoms interacts with well-defined crystallographic water molecules (not shown in Figure 4), and the methyl group attached to the sulfone functionality is engaged in hydrophobic interactions with F191. The carbonyl oxygen of the oxazolidinone forms a favorable polar interaction with the C2 CH group of H19.

The crystal structure of 13f thus confirms that the oxazolidinone group can favourably orient the sulfone/hydroxamic acid portion of the inhibitor with respect to the hydrophobic phenyl group while maintaining a low-energy conformation, as predicted by Spark.

Figure 4. X-ray crystal structure of 13f complexed with P. aeruginosa LpxC enzyme (PDB: 6MAE)



This paper from NIBR shows that Spark can guide the rational design and discovery of new drug candidates. In this case, a traditional scaffold hopping experiment led to the identification of novel oxazolidinone and isoxazoline LpxC inhibitors with potent antibacterial activity against Gram-negative bacteria.

Try Spark on your project

Request a free evaluation of Spark to move to new series and non-obvious IP by swapping scaffolds.


  1. Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. ZINC: A Free Tool to Discover Chemistry for Biology. J. Chem. Inf. Model. 2012, 52 (7), 1757–1768.
  2. Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: A Large- Scale Bioactivity Database for Drug Discovery. Nucleic Acids Res. 2012, 40 (D1), D1100–D1107.
  3. Pitt, W. R.; Parry, D. M.; Perry, B. G.; Groom, C. R. Heteroaromatic Rings of the Future. J. Med. Chem. 2009, 52 (9), 2952–2963

Capturing accessible chemistry space

There is often a divide between computational results and synthetic accessibility in the identification process of alternative compounds. However, there is now a way to produce results more quickly and precisely, and to ultimately accelerate the research process.

Our article Chemical Reaction discusses:

  • Scaffold hopping
  • Case studies:
    • Finding bioisosteres of DPP-IV inhibitors
    • Identifying optimization routes
  • Fragment reconnection
  • Accessible chemistry
  • Available synthetic chemistry

This article is taken from Innovations in Pharmaceutical Technology July 2016, pages 16-19. © Samedan Ltd

Scaffold hopping into new DPP-IV protease inhibitors


Cresset’s powerful scaffold hopping and fragment replacement software can be used to mimic an existing ‘active’ molecule’s electrostatic patterns to quickly, and efficiently, generate new molecular designs. The outcome, based on 3D molecular electrostatic similarity, is more biologically relevant than that from other similarity metrics. Here, we show how Spark can be used to rapidly generate potentially valuable chemical ideas for new DPP-IV inhibitors.


Two peptide hormones, GLP-1 and GIP, mediate lowering of blood glucose level through stimulation of insulin release and inhibition of Glucagon release. DPP-IV cleaves a dipeptide from the N-terminus of both GLP-1 and GIP hormones to give their inactive forms, thus abolishing their glucose lowering action. DPP-IV inhibitors have been shown to be important agents useful for treating type II diabetes.

Scaffold hopping methodology

The method involves:

  • An initial bioactive conformation: Preferably a potent target molecule of interest. This can either be extracted from an X-ray or modeled (e.g. from a pharmacophore or alignment / binding hypothesis).
  • Field pattern generation: Cresset’s proprietary XED force field used to generate electrostatic properties.
  • Database of molecule fragments: Spark uses an internal database of fragments derived from ChEMBL and ZINC. Custom sets can be generated from proprietary chemistry.
  • Automated reconstruction of the new 3D molecules: Can specify linking chemistry and replacement sites (e.g. scaffold or decoration).
  • Aligns and scores output as full minimized molecules: Using ’field similarity’ and shape similarity relative to the starting template(s).
  • Protein target can be used as excluded volume: Can take account of protein pocket by penalising examples with steric clashes.
  • Filter output on physiochemical properties.

DPP-IV X-ray ligand electrostatic and shape similarity analysis

A plethora of molecules are already known which inhibit DPP-IV and have useful anti-diabetic properties.

Hierarchical clustering of DPP-IV x-ray ligands by electrostatic and shape similarity
Figure 1. Hierarchical clustering of DPP-IV x-ray ligands by ‘electrostatic and shape similarity’.

Superimposition of over 20 published x-ray crystal structures, followed by hierarchical clustering using all-by-all field similarity on the ligands, reveals two main clusters and a distinct mode of binding for a fluoro-olefin example (Figure 1). This unique clustering, performed in Cresset’s ligand-focused workbench, Forge, allowed the selection of three distinct inhibitors for further scaffold hopping work.

Experiment and results

Alogliptin (1), Omarigliptin (2) and the fluoro-olefin (3, PDB: 3C45) represent some of the most ligand efficient examples from these clusters. Two experiments were performed in Spark using (1) and (2) in a simple scaffold hopping exercise. A final experiment was a chemotype merging experiment: a truncated (2) was used, with (3) as a second template, to find molecules bridging the two series. Workflow as shown in Figure 2.

Example results (Table 1) shows the diverse range of output suggestions provided for new chemistry and validates the method by providing examples which already have precedents in patents and the literature.

Scaffold hopping and merging_input structures fields field points and output examples
Figure 2. Scaffold hopping and merging – input structures, fields, field points and output examples.

Diverse range of output suggestions provided for new chemistry
Table 1. Scaffold hopping and merging examples of output 2D structures.


The Spark searches output a wide range of diverse chemotypes that included active or very close architectures to known active frameworks. A number of these had been discovered through HTS rather than through rational design. Tight control over the chemistry ensures that feasible chemistry is provided from known fragments whilst maintaining the features necessary for activity.


Spark is a powerful molecular modeling tool for the rapid virtual elaboration of scaffold ideas; either in scaffold hopping, merging, fragment growing or linking experiments. Applying Spark to chemotypes bound to the active site of DPP-IV provided a range of interesting and synthetically-feasible suggestions.


Green et al, Diabetes and Vascular Disease Research 2006, 3 No3, p159-65.

Protein pictures were rendered using open source Pymol from Delano Scientific.

[This content was presented as a poster at Proteinase 2015.]

ACS Denver 2015: Scaffold hopping: Balancing novelty, accessibility and physico-chemical properties

Scaffold hopping remains a central task in medicinal chemistry for generating and protecting intellectual property. We have previously presented a technique for rapidly generating reasonable yet novel scaffold replacements using molecular fields which has been extended to include R-group replacement. The approach uses a database of molecule fragments or available reagents to suggest replacements that maintain the shape and electrostatic character of a known active molecule.

However, for both of scaffold hopping and R-group replacement activity is not the only requirement for any suggested replacement. To be useful they must be synthetically accessible and must fall within the window of acceptable physicochemical properties for the project. The task of ranking scaffold hops or bioisosteric replacements is thus one of multi-parameter optimization, where several often-competing requirements have to be considered simultaneously.

In this poster we suggest methods to address these issues with reference to case studies of both scaffold hops and R-group replacements. Synthetic accessibility can be handled by tying the bioisostere search to addressable chemistry space, utilizing the chemist’s knowledge of what synthetic routes are feasible to guide the search. The best guide of novelty in a scaffold-hopping situation is the experience of the user: what other scaffold are known in the literature or in patents? To this end, we suggest a clear and minimal user interface to allow rapid triage of large result lists. Finally, assessing results in the light of physicochemical and predicted ADMET requirements can be achieved through a configurable radar plot giving clear visual feedback on how close any suggested replacement is to the ideal.

Click image to see poster.

ACS Denver 2015 Scaffold hopping Balancing novelty accessibility and physico-chemical properties

Unprecedented control over scaffold hopping searches with Spark V10.3

New fragment databases give 25% increase in available chemical space

Cambridge, UK – 30th October 2014 – Cresset, provider of computational chemistry software and services, announces the release of Spark V10.3 for scaffold hopping and R-group exploration. Spark finds biologically equivalent replacements for sections of an active molecule, generating new ideas and helping you escape patent or toxicity traps. New in this release:

  • Significantly improved viewing, analysis and sharing capabilities help identify compounds with the best balance between novelty, synthesizability and physical properties
  • 25% increase in the number of fragments, giving a greater opportunity to find new IP
  • New advanced filters ensure the drug-likeness of results and narrow the search to specific changes that interest you.

“Spark experiments return structures you have thought of yourself, plus new structures that make chemical sense and are totally unexpected,” says Dr Tim Cheeseright, Director of Products at Cresset. “This new release makes it significantly easier to evaluate and share your results. The new tile view lets you select and view the structures and properties that matter most to your project. Individual chemists can flag key results, add notes and share projects with team members.”

“Spark V10.3 gives users unprecedented control over scaffold and fragment searches,” adds Dr Mark Mackey, Chief Scientific Officer at Cresset. “You can focus your search by filtering on SMARTS patterns or physical properties, so that the results all have the physicochemical profile required by your project.

“In addition, we have enlarged the number of reagents for use in lead optimization and hit growing from 150,000 to over 590,000. This provides a rapid assessment of the synthetically available choices around a lead compound, increasing novelty and reducing the design time for new molecules.”

Spark’s significantly improved results analysis helps you identify the best bioisostere for your project

Web clip: Spark V10.3 – Using Spark’s tile view and tags to rapidly assess scaffold hopping results

Version V10.3 of Spark, Cresset’s computational chemistry software for idea and bioisostere generation, includes the ability to ‘tag’ results with a custom user-defined note that can be used for sorting, filtering, and decision-making. This expands on the ‘favorites’ designation in that tags can be used to explain why a result was flagged as a favorite. For example, you can tag suggested results as being known already, interesting, synthetically unfeasible, or any other designation that you might need.

The tile view of results allows for rapid assessment of the bioisosteric substitution, along with selected properties in a tiled view. This allows for a stream-lining of the visualization of many results from the Spark experiment and can be sorted and filtered the same way as the regular spreadsheet view in the Molecule Table.

See this in action in the web clip below and contact us to find out more.

Intelligent library design for protein families and beyond

Finding interesting hits against the plethora of potentially ‘useful’ new protein targets is still a significant challenge despite the growing list of techniques for detection and hit generation paradigms.

For example: high throughput screening of diverse compounds, fragment based drug discovery, crystallography and NMR driven Structure based drug discovery, plasmon resonance protein ligand binding detection and phenotypic screening to mention a few.

There are also important and efficient computational chemistry techniques such as structure or ligand based high throughput virtual screening but also low throughput scaffold hopping and iterative de-novo design for fast followers. Amongst these, rationale design of compounds remains an important and useful technique for providing useful starting chemistry, particularly where either ligand or protein information exists. I presented an overview of library design at CDDD in Verona. My presentation covered an extensive chemogenomic approach for GPCR library design and the use of Cresset’s cutting edge field based software for ligand gated ion channel library design, both of which were published recently in peer reviewed articles. You can view my presentation below.

Scaffold hopping in medicinal chemistry

Scaffold hopping in medicinal chemistry

Scaffold hopping in medicinal chemistry

This book is part of the series ‘methods and principles in medicinal chemistry’.

The first section serves as an introduction to the topic by describing the concept of scaffolds, their discovery, diversity and representation, and their importance for finding new chemical entities. The following parts contain a general description as well as case studies of the most common tools and methods for scaffold hopping, whether topological, shape-based or structure-based. Part two, chapter 13 explores Cresset’s ‘XED force field and Spark‘ and was co-authored by Dr Andy Vinter and Dr Martin Slater. The final part contains three fully documented real-world examples of successful drug development projects by scaffold hopping that illustrate the benefits of the approach for medicinal chemistry.

While most of the case studies are taken from medicinal chemistry, both chemical and structural biologists will also benefit greatly from the insights presented here.

Case study 2 ‘Bioisosteric Replacements for the Neurokinin 1 receptor (NK1R)’ by Francesca Perruccio (pages 259-278) cites Spark, Cresset’s dedicated scaffold hopping too.

Now available as an eBook from various online vendors, the book is scheduled for physical publication in December 2013.

Author information

Nathan Brown is the Head of the In Silico Medicinal Chemistry group in the Cancer Research UK Cancer Therapeutics Unit at the Institute of Cancer Research in London (UK). At the ICR, Dr. Brown and his group support the entire drug discovery portfolio together with developing new computational methodologies to enhance the drug design work. Nathan Brown conducted his doctoral research in Sheffield with Professor Peter Willett focusing on evolutionary algorithms and graph theory applied to challenges in chemoinformatics.

After a two-year Marie Curie Fellowship in Amsterdam in collaboration with Professor Johann Gasteiger in Erlangen, he joined the Novartis Institutes for BioMedical Research in Basel for a three year Presidential Fellowship in Basel working with Professors Peter Willett and Karl-Heinz Altmann.

His work has led to the pioneering work on mulitobjective design in addition to a variety of discoveries and method development in bioisosteric identification and replacement, scaffold hopping, molecular descriptors and statistical modeling. Nathan continues to pursue his research in all aspects of medicinal chemistry.

Spark V10.2 released

We are delighted to announce the release of a new version of Spark, our innovative tool for finding biologically equivalent replacements for key moieties in your molecule. This release contains many new features and improvements to the user interface along with new databases of bioisosteric fragments and hence is highly recommended for all users. Two key focuses for this version have been to improve the link between suggested bioisosteres and the available synthetic and property space of your project, and to make ranking Spark’s suggestions on multiple physicochemical properties easier and more intuitive.

Spark Radial Plot


The new radial plots summarize the properties of Spark result molecules in an instantly readable and interpretable way. These totally customizable and sortable additions to the molecule table enable the rapid visual profiling of new bioisosteres against personal, project or corporate physicochemical properties. Setting up the plots is easy – just pick the property to be added to the plot from a drop down list. The settings for ‘good’ and ‘acceptable’ values are easily customizable so that you can create a corporate or project based profile that can be used in every Spark experiment. Sorting on the radial plot column causes the result molecules with the best overall properties to rise to the top of the table, reducing the time taken to choose the best possible synthetic direction for your project.

A field difference in Spark

The areas of more positive (red) and more negative (blue) field are highlighted for a result molecule (right) compared with the starting compound (left).


The new ‘field difference’ display mode enables greater understanding of the effect of a specific change on the electrostatic and shape properties of your molecule. In this mode the regions of change in field of the result molecule are highlighted next to the starting molecule. Thus regions that become more positive or negative are easily spotted giving greater understanding of the differing shape and electrostatic characteristics of a change.

New in this version of Spark are databases of fragments derived from chemical reagents and building blocks. These new databases enable the use of Spark to scan the immediately available chemical space for the best possible move. The databases come from the processing of sets of commercially-available reagents with simple, chemically intuitive rules for generation of R groups. Over 20 different reagent databases are provided by Cresset using the current rules,which can be easily modified to suit your preferences. If you think we’ve missed something then let us know and we can add it to the list in minutes.

Customers with a database generator license can use our rules to process their own available reagents, giving rapid suggestions for the next set of compounds to be made using the reagents currently in your lab. Often these suggestions will warrant further investigation in Torch or Forge and so we have enhanced the link to these applications from Spark with a new “Send to” menu entry that transfers results to the chosen application.

Lastly we’ve introduced the option to create databases by fragmenting molecules that exist in a predefined conformation, such as those from small molecule crystal structures. We will be investigating the delivery of Cresset calculated databases from these sources in the next few months. However, customers with the database generator can use this mode immediately on their own or public crystal structures to further enhance their sources of bioisosteres.

This release of Spark is a significant advance: existing users will see great benefits from updating, while if you’re not already a Spark user contact us for a free evaluation to see what you’re missing out on!


Kick-starting stalled projects

When a project runs into patent or toxicology issues, scaffold hopping can be a fast track to new chemical space

In the search for a compound that is efficacious, well tolerated and novel most drug discovery projects hit a hurdle or two. In most cases the problem is not in the activity of the lead compound, but in its physicochemical, ADMET or intellectual properties. Sometimes this can be ameliorated by small changes to the structure, but in most cases this is not sufficient. In this case the best solution is usually to move the project into new areas of chemistry towards compounds that have a better ADMET profile, better IP or better properties. The trick is to do this without losing all of the hard-won knowledge on SAR around the existing series and especially not losing the hard-won activity of the lead.

A common approach is to ‘scaffold-hop’ – change the ‘core’ or ‘scaffold’ of an active molecule while retaining the attached R groups as much as possible. In the ideal case the resulting molecule will have a biological activity very similar to the original, but its different core leads to a new ADMET and IP profile, solving the problems with the original compound. Since Cresset’s field similarity approach describes the binding preferences of a molecule, what we want to find is an alternative core that preserves the field pattern of the original.

Cresset consultants have extensive experience in applying this technique to help clients to kick-start a new project or ‘un-stick’ an existing one. We use our synthetic and computational experience together with our software to analyze your data and suggest new chemical series that will take you in the direction that you want to go.

We may start by developing 3D templates or pharmacophores, or developing models of your SAR using Activity Miner or FieldTemplater. We would then go on to use Spark to suggest novel core replacements that we refine in Forge. Alternatively, we may work closely with your synthetic chemists to define all the possible scaffolds that can be introduced into your molecules. We then use this information in Spark, Forge or BlazeGPU to find the best possible option. Either way, we get your project moving in the right direction, delivering the best possible combination of chemical and biological properties. Throughout this process we work closely with you to ensure that our suggestions fit your project goals and synthetic capabilities.

In the pharmaceutical industry, confidentiality is of fundamental importance to our clients. For this reason, our most impressive work is usually not publishable, so we carry out retrospective analyzes of published projects in order to provide examples of our capabilities.

In spring 2013, Tim Cheeseright, Cresset’s Director of Products, presented a detailed study on the use of Spark for scaffold hopping from the core of Sildenafil, in a comparison with a published technique from Pfizer. He showed that Spark not only found all of the scaffolds identified by the Pfizer team, but located additional known active cores while only using a fraction of the computation time.

At this year’s Cresset North American user group meeting the independent medicinal chemistry consultant Alfred Ajami gave a presentation describing the use of Spark to retrospectively analysze two case studies from the bioisosteric replacement literature and to evaluate its performance in a number of projects that he has been involved with. You can see the full presentation ‘Pharmaceutical hunts with Spark: case studies from the literature and current campaigns to develop immunokinase inhibitors’, Alfred M Ajami, DCAM Pharma Inc.

These studies show the power of the Cresset methods to free your drug discovery program from being stuck in a dead-end series. contact us for more information about how Cresset Consultants can help to kick-start or un-stick your project.