New Spark reagent databases: eMolecules’ Tiers 1, 2, and 3

Each month we release updated Spark databases derived from eMolecules’ building blocks. These have proved very popular with our customers. This month a small change is being made to the databases in that we now only include reagents that are in eMolecules’ Tiers 1, 2, and 3. These correspond to the most accessible reagents and should be a good source of inspiration for R-group design experiments in Spark.

Why the change?

The number of reagents that are now listed as available has grown significantly. In the last couple of months we have been processing around 650,000 reagents but this month that number is close to 1.1 million. Unfortunately the majority of this increase is in eMolecules’ Tier 4 category with availability in the multiple-weeks time frame. We felt that these additional reagents were largely noise in the majority of Spark experiments. As a result we have slimmed the downloads, search times and results by only including Tiers 1, 2 and 3. These still encompass 295,000 reagents and hence provide you with an excellent source of readily available R-group bioisosteres.

If you are interested in the Tier 4 reagents, please contact Cresset support to discuss the options.

Installing the Spark reagent databases is easy using the built in Spark database update facility.

Tversky similarity in field-based virtual screening

In the releases of Blaze V10.3 and Forge V10.5 we introduced new similarity metrics alongside the new capabilities to manually weight the similarity function using pharmacophore constraints. With the introduction of Tanimoto and particularly Tversky measures of similarity, a new range of experiments are available to you that help you tailor the results you get. In this post I will use the Tversky similarity to perform substructure and superstructure type searches using Blaze. These new options are also available in Forge.


Figure 1: Blaze results can be tailored to generate the type of results that interest you, from substructure like to pure chemotype switching or super-structure like.

Similarity in Blaze

Blaze uses the field point patterns of molecules combined with their shape to align and score a ‘database’ of molecules against a ‘reference’ or ‘query’ that is usually a known active. In this context the default Dice similarity has worked well. It returns active molecules that are similar in size to the query, but is not too size-dependent allowing Blaze to find hits that are smaller than the reference. In most cases this is exactly what you want – a ligand the same size or smaller than the reference that maintains most of the potential sites of interaction. The scoring algorithm could be altered to generate more substructure like or more superstructure like results. However, this was complex to set up and sub-optimal in performance. In Blaze V10.3 the new Tversky similarity makes these searches more accessible. A look at the average MW of the first 100 compounds returned using the standard Dice and the new Tversky options highlights the difference:

Table of average MW of first 100 compounds returned using different similarity metrics. Database of 35283 positively charged Chembl compounds with 5-30 heavy atoms on Blaze demo server. Query MW: 319. Database average MW: 318

Dice Tanimoto Tversky, α 0.95 Tversky, α 0.05
314 313 192 363

 

Substructure searches with Blaze

The Tversky metric has two parameters, α and β. Using the Tversky similarity option in Blaze, and setting α to 0.05 and β 0.95, results in a substructure-like search. In fact, we don’t deal with structures so this actually equates to a ‘sub-field’ search. It returns molecules that contain a field pattern that is contained within the query – i.e. field fragments of the query. This is useful where you have a large known active but want to screen or design a fragment library of smaller molecules that match parts of the query.


Figure 2: Search query and 3 selected results (ranks 3, 5, 11) from a sub-field search using the A2C active from the Fragment hopping with Blaze case study. Each result includes some features of the search query but also omits at least one functional group.

Superstructure searches with Blaze

Setting a Tversky similarity with α at 0.95 and β at 0.05 generates a ‘super-field’ search. That is, molecules that contain a field pattern similar to the query are scored highly whether or not they have additional field points. This is useful for growing hits from a fragment screen or in other situations where you do not want to penalize results for having additional functionality to the query. As hits could contain the query at any position and any orientation, this option works particularly well when combined with field, pharmacophore or excluded volume constraints. For example, using an excluded volume will direct the results towards the available space around the query. Equally, using field constraints or the new pharmacophore constraints will ensure that results contain the interactions that you know to be important.

Figure 3: Search query and 3 selected results (ranks 2, 4, 6) from a super-field search using A2C active from the Fragment hopping with Blaze case study and an expanded database to include larger fragments. Each result contains a similar field pattern to the query plus additional features or functional groups.

Tanimoto similarity in place of Dice

In addition to Tversky, the new versions of Blaze and Forge offer the opportunity to change from the default Dice similarity to Tanimoto. This will make a difference to how the individual elements of the score are combined, resulting in a small change in the order that molecules are returned in a virtual screening experiment, but the two experiments are highly correlated. The effect is somewhat complicated to describe and hence will be explored in a future post.

Figure 4: Plot of rank returned using Tanimoto similarity vs Dice similarity for ~10,600 compounds. The results are highly correlated with r2 0.96.

Conclusion

The new similarity metrics increase the range of experiments that can be easily performed within Blaze. Using the new metrics in Forge enables refinement or enhancement of Blaze results using the same metrics. Sub-field and super-field searches in particular should prove useful for fragment-based discovery.

If you would like to try the Blaze interface, or study the effects of the new similarity metrics, then signup for a Blaze demo server account.

To try Blaze on your datasets or your projects, request a full evaluation.

Torch V10.5 release includes new science and improved workflows

Now released and available for download, V10.5 of TorchTM brings new science, improved design workflows, an updated GUI and is recommended for all users.

Highlights

New pharmacophore constraints give you another way to bias the alignment towards the results that you expect. This new science has applicability to design, ligand alignment and virtual screening. Available pharmacophore types include H-bond donors and acceptors, metal chelators, and covalent centers.

The design workflow has been improved by including and updating physico-chemical properties in the editor as you design. You now get immediate feedback on how your design fits with critical physio-chemical property profiles and for predicted activity (through Forge QSAR models) through inclusion of the radial plot in the editor.

Lastly we have updated the GUI styling and improved usability throughout the application to streamline your molecule design process.

Enhanced design workflow

Design is central to Torch. In this version we have taken a look at the workflow and come up with significant enhancements. You told us how much you liked the immediate feedback for scoring of molecules against the reference and against QSAR models developed in Forge, but wanted us to extend this to physico-chemical properties. To satisfy your request we have introduced the radial plot into the editor enabling rapid, visual feedback of the fit of physico-chemical properties to a project profile as you draw your new designs. This will significantly help you to design molecules that have the properties that you want without having to spend time parsing large amounts of numerical data or having to exit the editor when you think that you have a good design.

To further enhance and smooth your design experience in Torch, we have added the capability to snapshot designs into the main project without leaving the editor. The new ‘Save a copy’ button stores your molecule directly into the project with current molecule title and any notes that you have made. The new workflow enables greater granularity in the deisgn process, capturing more designs and ensuring that no good idea or inspirational moment is lost.

The last stage for any design is to communicate it to others. In the previous version of Torch we introduced Storyboards to enable you to capture particular 3D views for later recall. In V10.5 we have signicantly improved storyboards to better serve your communication. All storyboard images are now stamped with a time and date, can be given a title and annotated with detailed notes to enable others to understand the story, whether or not you are there to talk it through.

Pharmacophore constraints

We know that our approach often gives superior results to other methods when aligning diverse and congeneric series but there are times when you want more control to weight the alignment towards a particular interaction. This has always been possible in field space, but you wanted the ability to more tightly control the type of atoms that are aligned. In this version we have added pharmacophore constraints into the alignment. This option enhances the already present field and excluded volume constraints such that you can specify that a particular pharmacophoric atom type must be in a specific location in the aligned molecules or the score is penalised. The result is significantly higher control over the alignments. Forge V10.5 has more on this exciting feature and how it affects the alignments, while the recent Blaze V10.3 announcement describes the effect on virtual screening performance.

Improved substructure alignments

Our field based alignments give an excellent view of how ligands compare from a potential binding interactions’s point of view. The results compare favorably with structure-based approaches such as docking. However, when looking at activity cliffs, and particularly the underlying causes of the change in activity, a more ligand centric alignment often gives better results. For that reason we introduced the option to align using substructure in previous versions of the software. Torch V10.5 revisits that algorithm, making a number of improvements behind the scenes to deliver the results that you expect even more frequently. This is the heart of Torch and we are delighted to release an improvement to what was already very good.

General improvements

Alongside the specific workflow and scientific improvements we have introduced a number of enhancements to the Torch interface. These include new options for protein ribbon display, improved measurement and protein-ligand contact display, an improved grid view function, improved support for stereo, tagging of molecules directly from the 3D window, updated and clearer icons, and completely new widget for adding constraints.

Upgrade to Torch V10.5

Upgrade at your earliest convenience to benefit from the many new and improved features in this release.

Evaluate Torch

If you are not currently a Torch customer, download a free evaluation.

Blaze V10.3 released for even better virtual screening

The latest version of BlazeTM, our virtual screening platform is now available. V10.3 introduces pharmacophore constraints to enable you to find the best possible new hits and leads. Alongside pharmacophore constraints, we’ve added additional similarity metrics and updated the user interface.

Figure 1: Blaze has a new look that includes WebGL views of the search molecules

New pharmacophore constraints

In previous versions of Blaze we have enabled the setting of Field constraints. These work by down-weighting any result that did not have a specific electrostatic or hydrophobic field at a location you specify. V10.3 enhances this capability by giving you the ability to add a specific atom as a pharmacophoric feature that must be matched by an atom of a similar type in the results. The effect of this is to provide you with a mechanism for ensuring that the results that you get from your virtual screening experiment fit with the known SAR or with your expectations. For example, using pharmacophore constraints you can ensure that all results retrieved from a virtual screen for new kinase hinge binders have the donor-acceptor-donor pharmacophore motif. This differs from field constraints by the severity of the match required. Using field constraints a donor can match other motifs that also express positive electrostatics – such as electron deficient aromatic C-Hs where a pharmacophore feature would only match hydrogen atoms attached to heteroatoms.

Figure 2: (a) Ligand from PDB 4Z3V with field and pharmacophore constraints added. (b) Active BTK inhibitor that satisfies both constraints. Note that the aromatic hydrogens match the field constraint but would not have matched a pharmacophore constraint placed on the indazole NH.

 

We tested the new pharmacophore constraints using a selection of kinase targets taken from the DUD dataset. We applied constraints to the hinge binding motif of each query molecule and studied the retrieval rates. Overall we found an average improvement of around 0.13 in ROC-AUC across the tested targets which represents a reasonable gain given the deficiencies in the dataset.

The ability to constrain result molecules to those that fit a specific pharmacophoric feature is very powerful. However, we advise caution – there are many known actives that do not necessarily contain a specific pharmacophore. This is highlighted in the BTK example above but can also be seen in kinases. For example, the CDK2 ligand from PDB 2uzl, although less active than some chemotypes, lacks any of the classic pharmacophore features associated with hinge binding and hence would not be retrieved by a query with constraints on these features.

 


Figure 3: Overlay of equivalent C-alpha atoms of PDB 2uzl (ligand in brown) and PDB 3c6o (ligand in pink). The 2uzl ligand lacks all of the classic pharmacophoric hinge binding motifs.

 

Beyond standard pharmacophores

Perhaps the most interesting aspect of the new pharmacophore constraints is in the application to virtual screening for covalent inhibitors. These enable you to specify that the retrieved molecules must contain a electrophillic center at same position as in your query. This works in exactly the same way as the traditional H-bond donor, H-bond acceptor type of pharmacophore constraint. In the ligand alignment algorithm we downweight any alignment where an electrophile is not overlaid with the constrained atom. This could be especially useful when screening large virtual libraries or other custom collections where the standard filters for screening collections are not appropriate. As well as electrophiles, we have an definition for metal binding warheads which, again, should help find a richer set of compounds for wet-screening than was previously possible.

Updated look and feel

The Blaze interface has a new crisp look that emphasizes the easy-to-use nature of the web interface. Unlike other virtual screening algorithms, Blaze is a complete system that enables easy compound and collection management combined with user and project based permissions. All of this is accessed through a web interface that has a wizard approach to experimental setup.

Figure 4: The New Blaze interface is cleaner with color coordinated help and prompts.

 

The web interface is not the only way to use Blaze. Our desktop applications Forge and Torch use Blaze’s REST API to submit searches and retrieve results giving you access to the power of Blaze from your desktop. However, the REST API can be incorporated into virtually any other application and we provide Pipeline pilot protocols, and example KNIME workflows to show how to search and manage compounds from these workflow solutions.

All the new science released in Blaze V10.3, described above, is available through the REST API.

Try Blaze V10.3

See the new interface, and try out the new science for yourself, by signing up for our Blaze demo server. Blaze is available as software for installation on your internal cluster, as an Amazon Machine Image that will run within your Amazon deployment or for rental on a per project basis using our Blaze Cloud installation. Contact us for more information.

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.