Launch of Flare

Flare™ 1.0 is released and available for evaluation! Flare is designed to bring you new insights for structure-based design in a modern, easy to use interface that provides a framework for future growth. Flare combines the best of Cresset research with cutting edge methods from academia and selected commercial partners to give you a deeper understanding of protein-ligand complexes that will inform and improve new molecule design.

The Flare GUI includes ligand and protein windows that enable you to create and browse through the structures that are important to you.

New methods for understanding your protein-ligand system

Key new technology available in Flare 1.0:

  • Visualize the electrostatics of the protein active site using protein interaction potentials
  • Calculate the positions and stability of water in apo and liganded proteins using 3D-RISM
  • Understand the energetics of ligand binding using the WaterSwap technique.

Protein active site electrostatics, visualized through protein interaction potentials clearly indicate areas of favorable ligand binding such as the electron rich pyrrolo-pyrimidine hinge binding motif in this PERK kinase inhibitor (PDB 4G31).

Robust enabling capabilities

Robust enabling capabilities support the new technology in Flare, providing you with:

  • Protein preparation
  • Ligand docking
  • Minimization using the XED force field.

Docking experiments in Flare are easily configured using one of the preset settings or can be customized with advanced options.

Intuitive  GUI

Flare has a logical menu structure using the ‘tabbed’ menu system to provide functionality that is easy to find and use. We’ve extended the approach to experiment setup that we have developed in our ligand-based tools to enable you to rapidly start a new experiment with a set of reliable default parameters or customize and save your own for future use.

The tabbed menu structure enables rapid identification of the functionality that you desire. For example the View tab contains functions related to the 3D view of the molecules such as the options to enable full screen mode or stereo mode

Try Flare for new insights

Flare is a new generation of structure-based design applications designed to give you new insights into your small molecule discovery project.

Evaluate Flare today.

Flare release imminent

New insights for structure-based design, thanks to our testers

With the release of Flare imminent, I would like to thank all our dedicated alpha and beta testers for their time and patience. Your work has been invaluable to directing the final stages of development and smoothing out workflows before the full release.

Throughout the development of Flare we have worked closely with users to ensure that we concentrate on the capabilities that are most important to you. We trust that Flare will add great value to your work, repaying the time and effort you have put in to its development.

Flare is a new product for us, moving into new scientific space, and has been many years in the making. Extensive scientific testing and benchmarking have been carried out on our own in-house methods and on those we have brought in from our scientific partners. We are completely committed to giving you the best science in the most usable form to push your projects forward and to fit in with your workflows.

The finishing touches are now being completed in advance of release which is scheduled for next week.

Request an evaluation of Flare.

Last chance for early access to Flare, new structure-based design application

We are delighted to announce the release of Flare beta 2. This version has many enhancements suggested by users as part of the on-going beta test program and is available for evaluation from your account manager. This final round of beta testing will focus on fine tuning the operation of Flare – perfecting keyboard shortcuts, adding more quick access items and polishing dialogue boxes in the run up to launch. So you have an application that meets your needs, we are interested in hearing about where you think the application can be improved.

Significant improvements in beta 2

Group ligands together

Since the first beta test we have made a number of improvements both in response to your feedback and from our own experience. One of the most significant changes is an overhaul of the relationship between ligands in the ligand table and their parent protein. In Flare beta 2, each ligand has a parent protein that is set automatically and can be manually adjusted by simply double clicking the table cell. This enables ligands to be grouped together by chemistry, source, or parent protein making full use of the ‘Molecule roles’ feature.

Molecules in two roles within the ligand table with their Title, associated Protein, and Rank Score from docking.

Improved calculation dialogues

All the calculation dialogues have been significantly improved to enable parallel processing and more visual feedback on the extent of the calculations. Now, whenever you setup a calculation the 3D window will display relevant calculation boxes, from the size of an active site in a docking experiment to the clipping boxes for surface generation.

A 3D RISM calculation in preparation showing the cube in which the RISM waters will be placed (magenta) and the hydration shell that surrounds the calculation (green).

Greater display control

The contact detection and display algortithm have been overhauled to give significantly greater performance and to show only the contacts that you are interested in. Flare now gives control over the display of individual interaction types, whether to include waters, and the inclusion of intramolecular interactions (such as H-bonds within a protein).

Interactions for the ligand from PDB 5MTO.

Cloud ready and enabled with Cresset Engine Broker

Finally, significant work has been put into job parallelization, particularly for WaterSwap. Here we have rewritten our unique Engine Broker that enables client machines (be they Windows®, MacOS® or Linux®) to use remote or cloud based compute resources to super-power their calculations. Using the Cresset Engine Broker (CEB) starting a cloud based calculation could not be simpler:

  1. Set the location of the CEB in the preferences
  2. Set up the calculation
  3. Press ‘Start’.

The new CEB has a completely different architecture such that it now handles all communication. This is particularly useful when running on the cloud or other situations where the client machine knows nothing of, or cannot communicate with, the individual calculation nodes of the cluster. For WaterSwap we have modified the algorithm to make full use of cloud resources where the perfect situation is to have an infinitely wide calculation that completes in seconds. For a monte-carlo based simulation there is a limit to how wide we can make the calculation but we do not have to limit ourselves to a single process either. In Flare Beta 2 we have enabled an option to split the WaterSwap job into parallel chunks that utilize the highly parallel nature of cloud resources to run the same simulation upto 4 times faster.

WaterSwap result for a ligand bound to TNNI3K (PDB 4YFI) showing both the ligand bound and water bound protein results from a WaterSwap experiment.

Try it for yourself

Interested in Flare? Contact your account manager to join the Flare beta 2 program and gain early access to this cutting edge structure-based design method with intuitive GUI.

Sneak peek at Flare

As our new structure-based design application, Flare, nears release, I share some of the innovative features that will give you new insights into protein-ligand binding, and a sneak peek at the interface which is a mixture of a traditional Cresset application and something distinctly different.

A PERK ligand in the active site of pdb 4G31 with RISM waters, green = stable, red = unstable.

Easy ligand and protein navigation

Flare has been created with ligand design at its heart so you can easily navigate ligands and their proteins, comparing, contrasting and improving them. To do this the ‘Molecules’ table has been borrowed from Forge and Torch. The table holds ligands and their data, and has been enhanced with a separate table for proteins. Why two places for molecules? We felt that separating the two types of molecule has distinct advantages. First it enables you to store and display, next to each ligand, all the physico-chemical property data that chemistry designers need to assess designs for progression to synthesis. It enables separate, rapid control of which elements are displayed in the 3D window – for example, you can quickly create a grid and compare one ligand in many different proteins or many different ligands in one protein. Lastly, separating the ligands into their own table enables separation and navigation of ligands in a way that would otherwise not be possible.

To counter any lack of functionality in separating proteins and ligands, drag and drop between the tables has been enabled. To move a ligand into a protein, or separate it away, you simply drag the molecule from one table to the other. Equally, each ligand has a concept of its parent protein and hence it will be associated with the correct protein when viewing multiple ligand protein complexes.

Flare can be used to easily compare ligand-protein complexes. In this case all available A2A crystal structures were loaded into the application and ligands automatically split out.

Each ligand in Flare can be displayed in its associated protein in grid mode making comparisons between ligands or proteins straightforward.

Protein interaction potentials reveal the electrostatics that underlie ligand binding. In this case pdb code 4G31 (red = positive, blue = negative). Widgets can be undocked at any time and placed on additional monitors.

Powerful picking

Picking atoms, whether to change the display style, add a surface or perform a minimization is an amazingly frequent action in structure-based design. We wanted to make it as easy as possible, so common picking actions such as picking the active site or all ligand atoms are available directly from the ‘Home’ tab of the ribbon. However, this is just a small selection of the actions in Flare as they are enhanced through an extension, accessible from the ribbon, which gives a depth of functionality to Flare’s picking algorithms. For example using the extension you can pick atoms based on a SMARTS pattern, pick residues using a text query such as ‘ASN 83’, chains by name, residues by names or numbers, add or subtract to the existing pick or take the intersection. Using the enhanced picking widget you should be able to grab any atom within the application without needing to first find it in the 3D window.

Picking atoms is central to working with proteins. Flare provides common picking actions on the ribbon and gives an extended picking widget that enables complex queries.

Detailed logging

A key piece of feedback from alpha and beta test phases was that you wanted detailed logging. To get the right balance between finding the relevant information and seeing the detail of the step there is a hierarchy of logging. All top level events are recorded to a log window that you can choose to keep visible, move to the side or close as you prefer. At any time if you want the detail behind an operation then you can go to the log window and double click the relevant entry to see all the detail that underlies the operation in question.

Flare contains two levels of logging, a brief summary and detailed log text. Manual entries can be added at any time.

Flare contains two levels of logging, a brief summary and detailed log text (for RISM in this case). Manual entries can be added at any time.

Ribbon menu

Our intention is for Flare’s capabilities to grow significantly over time so we have built a GUI with room to expand the command structure without compromising usability. A key element is the choice of a ribbon interface instead of traditional menus; these provide a logical framework for commands with an easy search strategy to find the one that you need at that moment. We were always mindful to enable customization in the fullness of time and enable users to control their own work environment and the ribbon interface is the perfect environment for this. Our intention here is to avoid the nightmare growth of multiple, unexplained and unobvious icons suffered by many applications and classically described in the story of the microsoft ribbon.

Flare ribbon menus make actions always visible. Shown here with different application styles (Blue, White, Black).

Try it for yourself

Flare will be available for evaluation very soon. If you would like to test drive the novel interface, or apply one of the novel scientific methods to your project, please contact us to register your interest.

Blaze used in discovery of allosteric modulators of the high affinity choline transporter

A variety of neurological conditions can potentially be treated through the stimulation of cholinergic neurotransmission. The choline uptake into certain neurons is mediated by the choline transporter (CHT), which is well-characterized but otherwise unexplored as a potential drug target.

A team consisting of scientists from Pfizer, Neusentis, Nanion Technologies, and Kissei Pharmaceutical Company used two compound sets: (1) a specially created set of 887 molecules derived from the full Pfizer compound screening collection using Cresset’s virtual screening tool Blaze; (2) 2,753 molecules from the Pfizer Chemogenomic Library. From these sets they were able to identify nine active small molecules that modulate CHT.

This work will enable them to test the hypothesis that positive modulation of CHT will enhance activity-dependent cholinergic signaling. Read the full paper Discovery of Compounds that Positively Modulate the High Affinity Choline Transporter.

Using Blaze to develop a screening set from a corporate compound library

The team had identified two CHT modulators from the literature: one CHT positive allosteric modulator and one CHT negative allosteric modulator. Each of these was used within Blaze to search the full Pfizer compound screening collection for compounds with similar electrostatic and shape properties and therefore potentially similar biological activity.

The computational team kept the top 500 compounds from each virtual screen, based on the Blaze scoring function to form a set of 1000 compounds. This set was filtered based on compound availability and the removal of chemically unattractive groups, resulting in a test set of 887 compounds. This library was screened in assays, as detailed in the paper.

Identification of previously unknown active and structurally distinct molecules

Five compounds of interest were identified from the 887 test set created using Cresset’s Blaze. Three of these were confirmed as positive allosteric CHT modulators and two as negative allosteric modulators of CHT function. A further four compounds of interest were identified from the 2,753 molecules from the Pfizer Chemogenomic Library. The compounds of interest are shown in Table 2 ‘Tool compound data’ which forms part of the paper.

This paper demonstrates the high value of virtual screening in focusing a screening campaign. The team successfully identified previously unknown active and structurally distinct molecules that could be used as tools to further explore CHT biology or as a starting point for further medicinal chemistry.

Selected images from Blaze results with purported CHT modulator seed molecules (PAM MKC-351 and NAM ML-352) (green) shown on the left and output molecules 1-5 shown on the right (grey). Fields are shown with positive (red), negative (cyan), van der Waals (yellow), and hydrophobic (orange) regions.

Call for beta testers for Flare, our new structure-based design application

Flare provides new insights for structure-based design by integrating cutting edge approaches from Cresset with significant open source and commercial methods.

Using Flare you will:

  • Gain vital knowledge of the electrostatic environment of the active site of your protein
  • Compare protein and ligands electrostatics to improve new molecule design
  • Study how the electrostatic pattern of the active site varies across closely related proteins
  • Use electrostatic patterns across a protein family to design more selective ligands
  • Understand the locations and stability of water in your protein using 3D RISM based on XED and AMBER force fields
  • Use energetically favourable water to influence the electrostatic properties of the active site and improve ligand design
  • Design new molecules and dock them into the active using Lead Finder
  • Find the energetic hotspots in your protein using the WaterSwap methodology.

Flare will be available for beta testing in early February. If you would like to get involved then please contact us.


Docking Factor-Xa ligands with Lead Finder


Lead Finder1 is a protein-ligand docking tool for the virtual screening of molecules and quantitative evaluation of interactions between protein and ligands. In this case study, two different Lead Finder docking modes (standard and extra precision) were used in docking studies on a small number of Factor-Xa (FXa) protein-ligand complexes originally used in the CSAR 2014 benchmark exercise2. Results show the robustness of Lead Finder at finding the bioactive conformation of the ligands, when starting from a random conformation. In addition, it shows that the standard docking mode and the extra-precision mode work well at docking ligands and the later gives tighter dockings and may highlight ligands with lower activity and that do not fit into the active site.


Lead Finder is a docking tool from BioMolTech3 which generates docked ligand poses starting from the 3D structure of a protein (either experimentally derived by X-ray, or modeled by homology) and one or more 3D ligand structures. Lead Finder assumes that the protein is rigid, and analyses the possible conformations of the ligand by rotating functional groups along each freely rotatable bond.

FXa has been the target of drug discovery efforts at many pharmaceutical companies, where structure-based design has been used extensively. For this reason, FXa has been frequently used to benchmark new methodologies in structure-based design.

In this case study, we sought to replicate the typical experiments performed with docking engines during the lead optimization phase of small molecule discovery. We used, and compared, two different Lead Finder docking

modes (standard and extra precision) in two separate experiments. First we carried out self-docking studies on a small number of FXa protein-ligand complexes originally used in the CSAR 2014 benchmark exercise. Secondly we applied the two docking modes to a set of 45 related compounds, again taken from the CSAR 2014 dataset.

Lead Finder docking workflow

The ideal docking process with Lead Finder (Stage 1 in Figure 1) starts with an accurate protein preparation with BioMolTech’s Build Model.4 This includes:

  • addition of hydrogens to the heavy atoms of the protein, and assignment of optimal ionisation states of protein residues;
  • optimization of the spatial positions of polar hydrogen atoms to maximize hydrogen bond interactions and minimize steric strain;
  • optimization of side chain orientations of His, Asn and Gln residues for which X-ray analysis can return flipped orientations due to apparent symmetry.

Figure 1. A typical Lead Finder workflow.

Build Model uses an original graph-theoretical approach5 to assign optimal ionization states of protein residues at arbitrary pH conditions, which is based on the Screened Coulomb Potential (SCP) model.5,6

After completing protein preparation, an energy grid map (Stage 2) is calculated and saved for the protein binding site. This energy map is then used to dock the ligand structures.

The docking engine in Lead Finder (Stage 3) combines a genetic algorithm search with local optimization procedures, which make Lead Finder efficient in coarse sampling of ligands poses and following refinement of promising solutions.

The standard docking mode provides an accurate and exhaustive search algorithm. However, in extra-precision mode, Lead Finder uses the most rigorous sampling and scoring algorithms to increase accuracy and reliability of predictions at the cost of slightly slower speed of processing.

Scoring functions1 in Lead Finder (Stage 4) are based on a semi-empiric molecular mechanical functional that explicitly accounts for various types of molecular interactions. Individual energy contributions are scaled with empiric coefficients to produce three scoring functions tailored for:

  • correct energy-ranking of docked ligand poses (Rank-score);
  • correct rank-ordering of active and inactive compounds in virtual screening experiments (VS-score);
  • binding energy predictions (dG-score).

In this study we concentrated on the poses that were generated and hence were focused on the Rank-score function.


Initially we used a crystal structure of the FXa protein in complex with compound GCT000006 (GCT, PDB ID: 4ZH8). As can be seen in Figure 2, the 6-chloronapth-2-yl group of GCT binds into the S1 primary specificity pocket, while the morpholino group occupies the aromatic box (Tyr99, Try215, Phe174) of the S4 pocket.


Figure 2. Structure of the FXa protein in complex with the GCT ligand.

The protein was prepared with the default options of Build Model, in which the ligand is removed from the active site and the water molecules are retained. The coordinates of the ligand were then used to define the bounding box for the calculation of the energy grid maps.

Self-docking experiment

We started by re-docking GCT to the 4Z­H8 crystal structure to address the ability of Lead Finder of correctly identifying its bioactive conformation. In order to avoid bias in the self-docking experiment, the 3D conformation of GCT was flattened to 2D and then converted back into 3D using Cresset’s XedConvert7. A minimization with Cresset’s XedMin7 was followed to relax the ligand to a local minimum. The GCT ligand was then docked to the protein PDB 4ZH8 using the standard docking mode and the extra-precision docking mode.

Protein-ligand docking

A sub-set of 45 small molecules from the CSAR  2014 dataset with known activity against FXa (Table 1) were docked to the crystal structure 4ZH8.

Most of these ligands have in common a chlorinated mono or polyaromatic group and a morpholino group. All ligands were converted into 2D and then back to 3D with XedConvert and subsequently minimized with XedMin. The crystallographic ligand was used to define the bounding box for the energy grid maps. The 45 ligands were then docked to the protein using the standard docking mode and the extra-precision mode.

Table 1. Representative structures for 45 ligands  used in the docking study.

Figure 3. Lead Finder self-docking experiment on 4ZH8 using the standard (top row) and extra precision (bottom row) docking modes. The RMSD (in Å) between the docked pose (thick sticks) and the X-ray coordinates of GCT (thin sticks) is reported for each pose.



When using the standard docking mode and the extra-precision mode, Lead Finder outputs up to 10 best poses (if available) ranked in order of increasing rank score.

Figure 3 shows the five top ranking poses of GCT obtained using the Lead Finder standard (top row) and extra precision (bottom row) docking modes. The poses are ordered from the best ranking (left) to the worst ranking (right).

As can be seen in this picture, the five top ranking poses for both standard docking mode and extra-precision mode are closely aligned to the X-ray conformation of the ligand, correctly orienting the naphthalene ring of GCT into the S1 binding pocket.

In terms of RMSD deviation, values obtained are similar with the two different modes with each method able to find a solution within 2A RMSD of the x-ray pose in the top 5 results. However, the extra precision mode finds this result at position 2 rather than 4 and the pose is very close to the xray-ligand (RMSD 1.44) with a single R-group oriented differently. A small but potentially significant improvement.

Figure 4 shows the self-docking of other FXa proteins (4ZHA, 4Y7A and 4Y79) performed with the standard docking mode and with the extra-precision mode.

For these less flexible ligands, the extra-precision mode seems to have little effect on the RMSD of the results. Both modes are again able to identify the correct orientation of the ligand in the FXa active site with a RMSD that is within 2A for the top scoring pose..

Figure 4. Self-docking experiment on 4ZHA, 4Y7A and 4Y79 using Lead Finder’s extra-precision mode. The RMSD (in Å) between the top-scoring docked pose (thick sticks) and the X-ray coordinates of the native ligand (thin sticks) is reported for each pose.


Figure 5 shows a side-by-side comparison of the superimposed top-ranking poses for the 45 FXa ligands docked into the 4ZH8 protein using standard (left) and extra precision (right) docking modes.

For the standard mode, the majority of ligands are docked with the naphthalene ring correctly pointing down into the S1 binding site. However, one ligand is not docked as expected, with the pyrrolidine group pointing to the outside of the protein (GCT98A). Interestingly, this compound is the one with the lowest activity (pIC50 6.2) in the dataset.

When using the extra-precision mode, the docked poses in general look tidier, even though two ligands docked with the naphthalene group pointing outside of the S1 pocket: one is again GCT98A, and the other is GCT44A, the compound in the dataset with the second lowest activity (pIC50 = 6.4). These findings seem to indicate that Lead Finder may be able to provide useful suggestions for discriminating between active and non-active compounds.

Figure 5. Docking FXa ligands to 4ZH8 using the Lead Finder’s standard (left) and extra precision (right) docking modes.


This case study shows a typical Lead Finder docking workflow and demonstrates the robustness of the program by means of several self-docking experiments. Results show that Lead Finder does a good job at finding the bioactive conformation of flexible ligands, when started from a random conformation. In addition, we explored two docking modes (the standard and extra-

precision) to dock a sub-set of FXa ligands from CSAR 2014. While both methods seem to work well at generating sensibly aligned poses, the extra-precision mode provides tighter dockings and may be able to highlight ligands with lower activity which may not fit into the active site.


  1. Stroganov et al., Lead Finder: An Approach to Improve Accuracy of Protein-Ligand Docking, Binding Energy Estimation, and Virtual Screening, Chem. Inf. Model. 2008; 48, 2371-2385.
  2. Carlson et al., CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma, J. Chem. Inf. Model. 2016; 56, 1063-1077.
  4. Stroganov et al., TSAR, a new graph-theoretical approach to computational modeling of protein side-chain flexibility: Modeling of ionization properties of proteins, Proteins, 2011; 79, 2693-2710.
  5. L. Mehler, Self-Consistent, Free Energy Based Approximation To Calculate pH Dependent Electrostatic Effects in Proteins, J. Phys. Chem. 1996; 100, 16006-16018.
  6. L. Mehler and F. Guarnieri, A Self-Consistent, Microenvironment Modulated Screened Coulomb Potential Approximation to Calculate pH-Dependent Electrostatic Effects in Proteins, Biophysical Journal, 1999, 75, 3–22.

Progress in structure-based design at Cresset

Last year we announced our intention to develop a new structure based design application based on key research projects that we have been working on at Cresset. We have showed that our XED force field has significant advantages in the calculation of electrostatic protein interaction potentials and the calculations of water positions and energetics using RISM.

A new application for new science

To bring this new science to you presented us with a significant challenge. Our existing ligand-based design products work well for ligands but lack the necessary data structures to support structure based approaches and would have required significant re-design to retro-fit them. Accordingly, we chose to build a new application that will not only provide the framework for our new science but will also be a receptacle for future innovations, both our own and those of leading academic groups.


Figure 1. Protein interaction potentials (red=positive, blue=negative) for PDB 4MBS; (a) for the entire structure highlighting the helix dipoles and (b) in the ligand binding region showing the negative potential derived from GLU283 and the aromatic box encasing the ligand’s pendent phenyl group.

Hard work to solve hard problems

The last six months have seen significant progress towards our goal of a fully functional, released product by spring 2017. We now have in-house an application that includes primitive interfaces to most of the cutting edge scientific approaches that we had identified for the release version as well as to the Lead Finder docking engine. This alpha version is undergoing extensive validation and is sufficiently functional to be of active use by Cresset Discovery Services.

Beta testing starts in 2017

However, our long term aim is to provide exciting new science while retaining the simplicity of user interface and ease of use. Significant work remains: we still have to incorporate the last few algorithms and extend the interfaces to the new science ready for a full beta test release in early in 2017. If you are interested in taking part in the beta release early in 2017, then please contact your account manager.


Figure 2. Screenshot of the new application showing protein interaction potentials for 32 BTK proteins in the RCSB. Proteins were aligned within the application using COBALT and superposed by matching C-alpha atoms. The ligand of pdb 4zlz is shown for reference. A positive interaction potential (red) is present in most cases indicating the preference for an electron rich ligand.


Figure 3. Spark results for replacement of a pyridine-water complex docked to pdb 4zlz using Lead Finder within the structure-based design application and shown with the protein interaction potential and ligand field points.

New updated Spark reagent databases now released monthly

In our April 2016 newsletter we announced the release new reagent databases derived from eMolecules building blocks. These new reagent databases closely relate Spark results to the chemistry that is immediately available to you and enable rapid assessment of reagent availability. An enhanced set of rules process reagents into R-groups and provide availability information from eMolecules directly within the Spark results table.

From October 2016, Spark users will benefit from monthly releases of updated eMolecules derived reagent databases. The rolling updates are intended to provide the very best availability information on the reagents that you wish to employ.

The updated databases can be downloaded now through the Spark Database update widget (instructions on the installing Spark databases page) or using a command line utility (such as wget, please contact us for details), but we do not recommend using a web browser.

The exact number of R-groups in each of the eMolecules derived reagent databases is available through the Spark interface with approximate numbers and physicochemical property profiles available on the Current Spark Databases page. Note that we have not updated the fragment databases derived from ZINC at this time. Should you require assistance updating your databases then please contact Cresset support.

Inspiration through examples: A round-up of our case studies

We are privileged to be able to use our existing and upcoming software solutions to solve real world problems through Cresset Discovery Services collaborations. Wherever possible we publish our work, either in peer review journals or as case studies. However, we are often limited in what we can communicate by confidentiality issues. In these cases we use case studies on existing public data to exemplify our approaches or the novel ways in which our unique science has been applied. Below you will find a selection of case studies that represent our larger body of work with selections taken from recent, most popular and historical studies.

Recent case studies you may have missed

Naturally, more recent studies tend to use newer features of the software such as Forge’s Activity Atlas or Spark’s reagent databases to exemplify the new feature or demonstrate a new workflow.

Most read case articles

We endeavor to make all our case studies relevant to the problems that our customers are facing. However, some resonate more than others, either because the problem is particularly apposite or because the approach is novel or differnt.

Older case studies that are still of relevance

I encourage new users to look back at some of the older examples as they are just as valid today as when we first communicated them.

Over 20 individual case studies

Our list of case studies is growing steadily, with over 20 individual studies available for download across the following categories: