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 0.05 Tversky, α 0.95
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.


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.

Forge V10.5 release delivers new functionality for molecule alignment, and more ….

V10.5 of ForgeTM, the powerful computational chemistry suite for understanding structure activity relationship (SAR) and design, is now available. This release introduces significant enhancements to molecule alignment, plus the new Conformation Explorer, to visualize and inspect conformational populations. Also included are a large number of GUI styling and usability improvements.

Improved molecule alignment

Molecule alignment is the core experiment in Forge. It is key to developing robust qualitative or quantitative SAR models, building FieldTemplater pharmacophore hypotheses, understanding  the design of new compounds and small scale virtual screening experiments (for larger scale virtual screening use Blaze). V10.5 enables fine-tuning of alignment results by introducing appropriate constraints, an optimized substructure alignment algorithm, and new similarity scoring options.

Field and pharmacophore constraints

Field and pharmacophore constraints bias the alignment algorithm by introducing a penalty which down-scores results that do not satisfy the constraint. This provides you with a mechanism for ensuring that the results that you get from your alignment experiment fit with the known SAR or with your expectations.

With field constraints, you can specify that a particular type of field must be present in the aligned molecule. For example, you may want to a constrain a positive field where you want an interaction but this can be matched by both H-bond donors and other electropositive features such as the aromatic hydrogens in the example below.

V10.5 introduces the new pharmacophore constraints, which ensures that your desired pharmacophore features (e.g., Donor H, Acceptor, Cation, Anion) are matched by an atom of a similar type in the alignment results. A pharmacophore constraint can be used when you are certain that a particular interaction requires transfer of electrons (as in H-bonding or metal binding) in addition to the electrostatic character of the interaction.

Pharmacophore constraints introduce a tighter constraint on the alignment than a field constraint. Where field constraints allow matches across chemical features, pharmacophore constraints are limited to matching specific functional groups (e.g., specific donor-acceptor interactions): alignments that do not place a suitable atom on top or close to the constrained atom cause a penalty to be applied to the score. However, pharmacophore constraints in Forge V10.5 go beyond traditional H-bond donor/acceptor definitions to include, for example, covalent centres and metal binding motifs giving the ability to ensure that key warheads always align in the correct positions.

While field and pharmacophoric constraints are a powerful way of fine tuning alignment results, we recommend that they are using sparingly, as they will be introducing a bias in your Forge experiment. E.g., introducing a pharmacophore constraint on the indazole NH of the PDB 4Z3V ligand in Figure 1 – left would not have matched the aromatic hydrogens of the active ligand in Figure 1 – right.

Figure 1. Left: Ligand from PDB: 4Z3V with pharmacophore and field constraints. Right: Active BTK ligand which satisfies both constraints.

Improved alignment and scoring

Enhancements to alignment and scoring, accessed from the advance options panel, include:

  • Option to require full ring matches, and to bias the alignment towards a specific substructure specified by a SMARTS pattern, in the maximum common substructure alignment algorithm
  • New functionality to weigh specific fields independently when scoring
  • New similarity metrics to provide alternate scoring methods for the alignments
  • New widget for adding field and pharmacophore constraints.

New Conformation Explorer

Molecular conformations are central to Forge. The conformation hunter does a good job of generating a diverse range of energetically accessible conformations. V10.5 gives you the opportunity to more easily inspect the conformations generated for your molecules, enabling you to interact with and edit the populations.

In the new Conformation Explorer, you can inspect a set of conformations with respect to energies, measured distances/angles/torsions, as well as calculate the CSD torsion frequency for each rotatable bond to assess the feasibility of the generated conformations.

Conformations are listed in order of increasing relative conformational energy. Unrealistic conformations or those which are not deemed interesting can be selected and removed from the conformation population for that molecule. Preferred conformations can be promoted to the reference role in Forge with the click of a button.

CSD torsion frequencies can be calculated for all rotatable bonds. These are based on the Torsion Library which contains hundreds of rules for small molecule conformations derived from the Cambridge Structural Database (CSD) and curated by molecular design experts. CSD torsion frequencies are useful to highlight cases where the torsion angle in a calculated conformation is not one that is frequently observed in the CSD, and accordingly is a possible cause for concern.

Distances, angles and torsions can be measured for each conformation and those values can be used for filtering or generating a histogram plot.

Conformation energies can also be plotted in an interactive histogram plot. In Figure 2, the column or bucket with the blue highlight reflects the current conformations shown in the 3D view; the grey columns or buckets reflect to conformations which do not pass the set of filters.

Conformations can be filtered by energy, CSD torsion frequency and calculated distances, angles, torsions. Smart coloring includes coloring by energy and by CSD torsion frequency.

Figure 2. The Conformation Explorer in Forge. Rotatable bonds are colored and labelled by CSD torsion frequency.

Other new features and improvements

This V10.5 release also includes a variety of additional new functionalities and improvements to the Forge interface, including:

  • Enhanced Molecule Editor with a more intuitive layout, featuring a radial plot that is updated as changes are made to a molecule and the new ‘Save a copy’ button to store your molecule directly into the project without leaving the editor
  • New support for touch screen displays
  • Enhanced stereo view functionality with improved accessibility
  • New functionality to export Activity Atlas™ models as surfaces from the GUI
  • New Forge surface command-line binary to export Cresset field surfaces (positive, negative, hydrophobic and vdW)
  • New functionality to sort disparity matrixes in Activity Miner™ by Forge project tags, enabling easier identification of molecules of interest
  • New capability to export molecules by drag-and-drop to the Windows desktop (Windows only)
  • New capability to annotate and re-name Storyboard scenes
  • New tagging of project molecules from the 3D window and according to cluster membership, as calculated in Activity Miner
  • New ‘Send to Flare’ functionality
  • Improved grid view function
  • Improved display of protein ribbons, offering a choice of different ribbon styles and the capability to show ribbons for the active site only
  • Improved look and feel of the GUI with re-designed toolbars and updated and clearer icons for a more modern and sleek interface.

Upgrade to Forge V10.5

Upgrade at your earliest convenience to try the new Conformation Explorer and pharmacophore constraints in Forge, together with the many new and improved features in this release.

Evaluate Forge

If you are not currently a Forge customer, download a free 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.


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.

Student bursary: The Cresset User Group Meeting 2018

Students affiliated to a higher education institution are invited to apply for a bursary to attend, and present at, The Cresset User Group Meeting on June 21st, 2018 in Cambridge, UK.

To apply, submit an extended abstract, no more than 1 side of US letter/A4, on your proposed presentation about the application of Cresset software. Your submission should include your:

  • Course title/PhD title
  • Expected date of graduation
  • Principal investigator/PhD supervisor

Deadline for applications is January 12th, 2018. Award of the bursary will be announced in February 2018.


  • Only 1 submission per student
  • The bursary will be a maximum of £1,000
  • The bursary is awarded to the student submitting the abstract, and cannot be transferred
  • The successful applicant must present in person on June 21st, 2018 in Cambridge, UK
  • Decision of who the bursary is awarded to is made by Cresset and not open to discussion.

Sneak peek at Forge V10.5

New versions (V10.5) of Forge™ and Torch™  are due out next month. This release offers new science and functionality and plenty of improvements that significantly enhance both applications. Below is a sneak peek at some of the new functionality in Forge.

Pharmacophore constraints in alignment

In this release of Forge we have included the new options to constrain the alignments using specific pharmacophoric features. As in Blaze, constraints (e.g., DonorH, Acceptor, Cation, Anion, covalent center) can be added to reference molecules and must be matched in the alignment or a penalty will be applied to the score. Pharmacophore constraints will be useful in those cases (such as specific kinase targets or metal chelators) where explicit interactions dominate the alignments.


Molecule alignment is significantly improved in V10.5. New and enhanced functionality include:

  • Improved substructure alignment algorithm
  • New capability to specify the substructure you wish to match by writing a SMARTS pattern
  • New alternative similarity metrics
  • New individual field similarity weighting
  • Improved field and pharmacophore constraints editor, to define field and pharmacophore constraints and add specified field points in the desired position in 3D.

The result of all these improvements will be significantly improved generation of alignments that match your expectations without manual interference.

Conformation explorer

Molecular conformations are central to what we do. We think that our conformation hunter does a good job of generating a diverse range of energetically accessible conformations. However, we wanted to give you the opportunity to more easily explore the conformations of your molecules, enabling you to interact with and edit the populations.

The conformation explorer is a new tool in Forge for visualizing and analyzing conformation analysis results. Within the conformation explorer you can:

  • Visualize all the conformations created for each molecule in your Forge project
  • Delete unwanted conformations
  • Calculate and plot distances, angles and torsions
  • Calculate the CSD torsion frequency for all rotatable bonds
  • Filter conformations by energy, CSD torsion frequency and calculated distances, angles, torsions
  • Smart coloring of conformations includes coloring by energy and by CSD torsion frequency.


Figure 1: The conformation explorer in Forge. Rotatable bonds are colored and labelled by CSD torsion frequency.


Contact us to register for a free evaluation of Forge V10.5.

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.

Comparing ligand and protein electrostatics of Btk inhibitors


Protein interaction potentials implemented in Flare,1 Cresset’s structure-based design software, were used to calculate a detailed map of the electrostatic character of the protein active site of Bruton’s tyrosine kinase2 (Btk). The interaction potential maps were compared to those of selected Btk ligands to get a detailed understanding of ligand binding and SAR. 3D-RISM analysis in Flare was applied to investigate the stability of the crystallographic water molecules populating the Btk active site.


Bruton’s tyrosine kinase is a member of the Tec family of non-receptor tyrosine kinases. Recent literature findings2 indicate that Btk inhibition could be an attractive approach for the treatment of autoimmune diseases such as rheumatoid arthritis, a progressive autoimmune disease characterized by swelling and erosion of the joints.

The published X-ray crystal structure PDB:4ZLZ shows that the 4RV ligand interacts with the active site of Btk (Figure 1 – left) by making H-bond interactions with Glu475 and Met477 in the hinge region. The pyridyl ring is involved in a cation-pi interaction with Lys430, with the pyridyl nitrogen making a water-mediated interaction to the P-loop residues Phe413 and Gly414. The replacement of 4-methylpyridin-3-yl with small bicyclic heterocycles like indazole in 4L6 (PDB:4Z3V, Figure 1 – right), displacing the water molecule and making direct H-bond interactions with the P-loop, led to the discovery of ligands with improved potency towards Btk such as compounds 4L6, 1 and 2 (see Table 1).3

Figure 1. Left: X-ray crystal structure of 4RV (PDB:4ZLZ) in the active site of Btk making a water mediated hydrogen bond with the P-loop backbone. Right: X-ray crystal structure of 4L6 (PDB:4Z3V) making direct H-bond interactions with the P-loop backbone.

In this case study, we used the protein interaction potentials and the 3D-RISM method available in Flare to investigate the electrostatics of the active site of Btk and the stability of the crystallographic water molecules. This information was then used to understand the SAR of the molecules in Table 1.


The 4ZLZ and 4Z3V ligand-protein complexes were downloaded from the Protein Data Bank into Flare, and carefully prepared using the Build Model4 tool from BioMolTech,5 to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes and assign optimal protonation states to the protein structures. Any truncated protein chains were capped as part of protein preparation.

The protein sequences were aligned in Flare using the COBALT6 multiple alignment tool and subsequently superimposed by means of a least squares fit of equivalent C.alpha carbon atoms.

Protein minimization

The active site of the prepared 4ZLZ and 4Z3V ligand-protein complexes was minimized in Flare using the XED force field7 and Normal conditions (gradient cutoff: 0.200 kcal/mol/Å, 2,000 maximum iterations). The ligand structures were included in the minimization of the active site.

3D-RISM analysis

The Reference Interaction Site Model (RISM) is a modern approach to solvation based on the Molecular Ornstein-Zernike equation.8 3D-RISM has seen increasing use as a method to investigate the location and stability of water molecules in a protein.

Conceptually, 3D-RISM is equivalent to running an infinite-time molecular dynamics simulation on the solvent (keeping the solute fixed), and then extracting the density of solvent particles. The output of a 3D-RISM calculation consists in a grid containing particle densities, one for oxygen and one for hydrogen atoms. A thermodynamic analysis then assigns a ΔG value to each position on the grid, representing the ‘happiness’ of a putative water molecule at that position of the grid relative to bulk water.

3D-RISM calculations in Flare use Cresset’s XED force field, which offers the advantage of incorporating both electronic anisotropy and a certain degree of polarizability, and accordingly improves the effectiveness of the method.

A 3D-RISM analysis was carried out on 4ZLZ and 4Z3V to investigate the stability of crystallographic water molecules surrounding the 4RV and 4L6 ligands bound to the active site of Btk.

The following conditions were used:

  • XED force field and charge method
  • 4Å grid spacing
  • 14Å grid external border width
  • Convergence tolerance: 10-8
  • Maximum number of iterations: 10,000
  • Total formal charge handling: neutralize with counterions.

Protein interaction potentials

Protein interaction potentials are an extension of Cresset molecular interaction potentials to proteins. Both are calculated using the XED force field. The approach is similar in principle to the calculation of ligand fields: the protein’s active site is flooded with probe atoms, and interaction potentials are calculated at each point. This method makes use of a distance-dependent dielectric function based on the work of Mehler,9 to better cope with the large number of charged groups in protein structures.

All the ligands in Table 1 belong to the same series as 4L6, so for this case study protein interaction potentials were only calculated and displayed for the active site of 4Z3V.

Ligand fields

To obtain a sensible pose for the ligands in Table 1, the corresponding 2D structures were docked into the ‘dry’ (i.e., not including crystallographic water molecules) active site of 4Z3V using the Lead Finder10 method implemented in Flare.

Cresset’s ligand fields were then calculated and compared to the 4Z3V protein interaction potentials, to investigate the SAR for the ligand series.


3D-RISM analysis on 4ZLZ

At the end of a 3D-RISM run, a 3D-RISM water molecule chain is added to the protein structure. The water molecules in this chain occupy regions of high water density as predicted by 3D-RISM, and are colored according to the calculated ΔG for the whole water molecule, averaged over all orientations.

‘Happy’ water molecules (associated with a calculated negative ΔG) are colored in shades of green: these are water molecules which 3D-RISM predicts to be more stable in the protein than in bulk water, and hence more difficult to displace with a ligand.

‘Unhappy’ water molecules (associated with a calculated positive ΔG) are colored in shades of red: these are waters that are less stable relative to bulk water and hence more easily displaced by a ligand.

Figure 2 shows the results of the 3D-RISM calculation on 4ZLZ. The oxygen density surface (Figure 2 – left) clearly shows a region of localized water near the nitrogen of the pyridine, and the 3D-RISM localization algorithm (Figure 2 – right) suggests that a water molecule should exist in exactly the spot where it is seen in the crystal structure. The thermodynamic analysis indicates that this water molecule is neither particularly ‘happy’ nor particularly ‘unhappy’. This is consistent with the fact that this water molecule is displaceable (as proven by 4L6 and the other compounds in Table 1), but also indicates that the displacing group needs to have the correct electrostatics and shape to avoid losing affinity.

3D-RISM analysis on 4Z3V

The oxygen density surface for 4Z3V is shown in Figure 3 – left. The 3D-RISM localization algorithm correctly identifies the position of the majority of crystallographic water molecules surrounding the 4L6 ligand bound to the Btk active site: many of these water molecules are predicted to be ‘happy’. Accordingly, a selected subset of the stable water molecules was included in the calculation of protein interaction potentials for 4Z3V, as they were considered to be an integral part of the protein active site with respect to ligand binding.

Figure 2: 3D-RISM results on 4ZLZ. Left: oxygen isodensity surface at ρ=5. Right: localized 3D-RISM waters, colored by ΔG.

Figure 3: 3D-RISM results on 4Z3V. Left: oxygen isodensity surface at ρ=5. Right: localized 3D-RISM waters, colored by ΔG.

Protein interaction potentials for 4Z3V

As shown in Figure 4, the protein interaction potentials of both the ‘dry’ (not including crystallographic water molecules) and ‘wet’ (including stable crystallographic water molecules lining the active site) active site of 4Z3V match the 4L6 ligand fields in a satisfactory manner.

In particular:

  • the electron-rich cinnoline ring sits in a region of positive interaction potential in the middle of the 4Z3V active site;
  • the 5,6 hydrogens of the cinnoline ring sit near an area of negative interaction potential corresponding to the carbonyl of Leu408;
  • the carbonyl and the NH2 of 3-carboxamide sit respectively within and nearby an area of positive and negative interaction potential corresponding to the backbone NH of Met477 and the backbone carbonyl of Glu475 in the hinge region of Btk, with which they form H-bonds;
  • the 4-amino group on the cinnoline ring also sits nearby an area of negative interaction potential, corresponding to the carbonyls of Met477 and Leu408;
  • the electron-rich 5-membered ring of indazole sits in an area of positive interaction potential corresponding to the protonated side chain of Lys430 (not shown) and the backbone NH of Phe413, with the NH-group pointing towards a negative area corresponding to the backbone carbonyl of Gly414 with which it forms an H-bond.

The inclusion of stable water molecules in the calculation of protein interaction potentials confirms this scenario. In this case though, the region of positive protein interaction potential in the middle of the 4Z3V active site is much larger and embraces most of the cinnoline-indazole ring system. This is indeed fully consistent with the negative ligand field surrounding the cinnoline-indazole ring system (Figure 4 – bottom).

Also, the 4-amino group on the cinnoline ring sits in an area of negative interaction potential which nicely matches the positive ligand field corresponding to this group.

Figure 4: 4L6 superimposed to the protein interaction potentials of 4Z3V. Top-left: ‘dry’ active site, not including crystallographic water molecules. Top-right: ‘wet’ active site including stable water molecules. Bottom: Ligand fields for 4L6. Protein interaction potentials shown at isolevel = 3; ligand fields shown at isolevel = 2.

SAR of Btk inhibitors

A comparison of ligand fields with the protein interaction potentials for the active site of Btk provides some useful insight into the SAR of compounds in Table 1.

Compound 1

Compound 1 (pIC50 8.7) is one of the two most potent compounds in this data series,3 carrying a -OMe side chain on the indazole ring and a fluorine in position 5 of the cinnoline ring. The binding mode of 1 (Figure 5) is similar to that of 4L6. The compound makes H-bond interactions with Glu475 and Met477 in the hinge region, a cation-pi interaction with Lys430 (not shown), and H-bond interactions with the backbone of P-loop residues Phe413 and Gly414.

The fluorine group sits in a relatively large pocket close to a water molecule which it possibly displaces. The CH3 of the OMe group sits in an area of negative interaction potential.

Figure 5: Left: compound 1 (pIC50 = 8.7) superimposed to the protein interaction potentials for the active site of 4Z3V at isolevel = 3. Right: ligand fields for compound 1 at isolevel = 2.

Compound 2

Compound 2 is also one of the most active compounds in the data series3. Quite interestingly though, the NH on the indazole does not make an H-bond with Gly414, as it is turned on the other side, possibly making an
H-bond interaction with a nearby water molecule.

Figure 6: Compound 2 (pIC50 = 8.7) superimposed to the protein interaction potentials for the active site of 4Z3V at isolevel = 3.

Compounds 3 and 4

The good activity (pIC50=8.4) of compound 3 confirms that an H-bond donor on the bicyclic system is not an essential feature for a Btk ligand to reach good levels of activity. Quite interestingly, compound 4 (pIC50=7.7) is structurally very similar to 3, but significantly less active. The comparison of the ligand fields for these two compounds with the protein interaction potentials of the active site of 4Z3V provides a possible explanation, as shown in Figure 7. While for both compounds (Figure 7 – middle column) the negative ligand field shows a good complementarity with the positive interaction potential of the backbone NH of Phe413, the positive ligand field of 4 (Figure 7 – right column) does not match the negative interaction potential generated by the backbone carbonyl of Gly414.

For both compounds, the methyl group in position 7 of the cinnoline ring plays the same role of the methyl on the indazole ring of 4L6 in ensuring that the ligands achieve the correct conformation in the active site.

Figure 7: Compounds 3 and 4 superimposed to the protein interaction potentials for the active site of 4Z3V at isolevel = 3. Ligand fields shown at isolevel = 4.
Middle: positive interaction potentials superimposed to negative ligand fields.
Right: negative interaction potentials superimposed to positive ligand fields.


Protein interaction potentials and ligand fields, as implemented in Flare, are a powerful way of understanding the electrostatics of ligand-protein interactions. The inclusion of stable water molecules following a 3D-RISM analysis dramatically improves the precision of the method for the characterization of protein active sites. The information gained from protein interaction potentials can be used to inform ligand design, compare related proteins to identify selectivity opportunities, and understand SAR trends and ligand binding from the protein’s perspective.

References and links

2. C.R. Smith et al., J. Med. Chem. 2015, 58, 5437−5444
3. US patent 2015/0038510
4. V. Stroganov et al., Proteins 2011, 79(9), 2693-2710
7. J.G. Vinter, J. Comput.-Aided Mol. Des. 1994, 8, 653-668
8. R. Skyner et. al., Phys. Chem. Chem. Phys. 2015, 17(9), 6174
9. E. L. Mehler, The Lorentz-Debye-Sack theory and dielectric screening of electrostatic effects in proteins and nucleic acids, in Molecular Electrostatic Potentials: Concepts and Applications, Theoretical and Computational Chemistry Vol. 3, 1996
10. O. V. Stroganov et al., J. Chem. Inf. Model. 2008, 48(12), 2371-2385

Presentations from The Cresset User Group Meeting 2017

Thank you to the invited speakers, and delegates, who contributed to the success of The Cresset User Group Meeting.

The presentations we have permission to publish can be downloaded upon completion of the form below.


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.