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 Rarey torsion frequency for all rotatable bonds
  • Filter conformations by energy, Rarey torsion frequency and calculated distances, angles, torsions
  • Smart coloring of conformations includes coloring by energy and by Rarey torsion frequency.


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

Cresset releases Flare: Powerful structure-based design application with outstanding new methods for protein-ligand systems

Cambridge, UK – 29 June 2017 – Cresset, innovative provider of software and contract research services for small molecule discovery and design, announces the release of Flare, an intuitive desktop application that provides outstanding new methods for understanding protein-ligand systems. Flare enhances Cresset’s existing excellent product range focused on ligand-based design, and becomes their first product explicitly designed to support structure-based design.

“Cresset has been pushing the boundaries of ligand-based design for many years,” says Dr Robert Scoffin, CEO of Cresset. “Flare introduces structure-based design into our portfolio, giving companies access to outstanding new methods for investigating protein-ligand systems.”

Computational, medicinal and synthetic chemists working on small molecule design and optimization will use Flare to:

  • Gain vital knowledge ofprotein and ligand electrostatics to improve new molecule design
  • Compare electrostatic patterns across a protein family to design more selective ligands
  • Design new molecules anddock them to a protein target
  • Minimizeprotein-ligand complexes to achieve the optimal interaction for each compound
  • Calculate the location andstability of water molecules in a protein to guide compound design.

“Flare represents the next generation of structure based design applications,” says Dr Tim Cheeseright, Director of Products. “It has a modern, intuitive interface and is easily configured to enable cloud-based calculations, making excellent science immediately available to all users whatever their experience level.”

Users will benefit from:

  • Simple drag and drop to import/export molecules to the desktop or other compatible applications
  • Ready access to powerful tools through a modern ‘ribbon bar’ interface
  • Simple yet powerful selection capabilities and cutting-edge display options producing highly insightful molecular graphics.

“Flare integrates cutting edge approaches from Cresset with significant open source and commercial methods,” explains Dr Mark Mackey, CSO. “Throughout the product development we have worked alongside users from major pharmaceutical and biotech companies to ensure that we deliver the best science in the most intuitive format.”

Flare can be evaluated free of charge.

Download press release.

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.

What can Torch do for you that TorchLite can’t?


TorchLite is the powerful freeware 3D molecule viewer, editor and design tool from Cresset. However, there are situations in which modeling with TorchLite is simply not enough and you need to access the full power of Torch. This blog post highlights some of the features which make Torch a powerful molecular design tool for medicinal and synthetic chemists.


You can see several interesting applications of TorchLite in our case studies and web clips. With TorchLite, you can view the results of ligand-based or structure-based virtual screening, understand the shape and electrostatic character of active molecules and design new molecules to match their pattern. But what are the differences between TorchLite and its big brother Torch? When should you start using Torch?

In this blog, I highlight some of the additional features available in Torch, but not in TorchLite, with examples of their application.

SAR analysis in TorchLite

The web clip Visualizing field changes to understand SAR shows how to quickly investigate the SAR of a small dataset of NaV1.7 inhibitors using TorchLite. Structures were manually sketched using the built-in 3D molecule editor, quickly minimized and saved in the Molecules table and NaV1.7 activity data manually entered. This works nicely for this small dataset, however, for larger compound sets manual editing and data entry is slow and open to human error. Also, manual editing and minimization in TorchLite cannot replace a full exploration of the conformational space of compounds, which ensures that diverse, low energy conformations are considered in the SAR analysis. Finally, while alignment is straightforward for the simple changes carried out in the web clip, a robust method for sensibly aligning the compounds is required when more complex structural changes are made.

This is the most important difference between the two packages: conformational exploration and alignment can be carried out in Torch (and Forge), but not in TorchLite.

SAR analysis in Torch

In Torch, molecules are aligned to one or more reference molecules using fixed conformations, which can be imported into Torch or calculated on the fly by the application.

Suitable reference molecules are highly active molecules, preferably in their bioactive (protein bound) conformation. This is usually either experimentally observed (when crystallographic information is available), or derived from a docking experiment or pharmacophore modeling (these methods are also available in Lead Finder and Field Templater, respectively).

Using a ‘Normal’ alignment, the conformation ensemble for each molecule in the data set is aligned to the reference molecule in two stages. In the first stage the field points around a molecule are used to generate an initial alignment. In the second stage the initial alignment is optimized to get the best possible similarity score. In this stage, it is possible for Torch to use an excluded volume, typically derived from the protein crystal structure, that defines a region of space around the reference molecule that acts as a constraint on the alignments.

Torch offers an additional method for automated molecular alignment. Using the Maximum Common Substructure (MCS) approach each ligand is initially fitted to the reference molecule using a common-substructure algorithm and then additional groups are the fitted using the best match of field points and shape. This substructure alignment can be regarded as a ligand-centric view of the match to the reference where the use of the field points alone is akin to a protein-centric view of the alignment.

Each method has their advantages:

  • Field points give an unbiased view of alignment with a score that can be used in, for example, virtual screening
  • The substructure approach highlights the differences between molecules that lie in the same series making them easier to interpret, particularly when using ligand-centric computational techniques such as the activity cliff analyses in Activity Miner and Activity Atlas, as in the example below.

Using alignment in SAR studies

In the case study Activity Atlas analysis of sodium channel antagonists. Part I: SAR of the right-hand side phenyl ring a dataset of 62 pyrrolopyrimidine NaV1.7 antagonists was downloaded from CheMBL, conformationally explored in Forge and aligned by MCS to the chosen reference compound.

Figure 1. The reference compound used to align the NaV1.7 data set.
The SAR of the data set was then analyzed using Activity Atlas, a probabilistic method of analyzing the SAR of a set of aligned compounds as a function of their electrostatic, hydrophobic and shape properties, available in Forge.

A more simple workflow can be implemented in Torch to quickly and effectively explore the SAR on the right-hand side phenyl ring (Figure 1) using Activity Miner, an optional module of Torch (included in Forge).

The ‘Substructure’ filter in Torch was used to select a subset of 17 compounds from the original data set which have the same scaffold and left-hand side substituent as Cmpd 1, but vary on the right-hand side phenyl, following the workflow shown in Figure 2.

Figure 2. Filter by substructure in Torch.
The lowest energy conformation of Cmpd 1 (one of the most active compounds in the data set) was then chosen as a reference structure, following an ‘accurate but slow’ (Max number of conformations: 200; RMS cut-off for duplicate conformers: 0.5; Gradient cut-off for conformer minimization: 0.1 kcal/mol; Energy window: 3 kcal/mol) conformation hunt within Torch. This was used to align the 17 compounds by Maximum Common Substructure, using again an ‘accurate but slow’ set-up for the conformation hunt.

The SAR of the right-hand substituted compounds can then be explored using the activity view maps calculated and displayed by Activity Miner.

The activity view shows a focus compound surrounded by its nearest neighbors according to the chosen similarity metric (Figure 3). In this view the height of each wedge corresponds to the ‘distance’ between the pair: a smaller wedge reflects very similar compounds.

Figure 3. Activity view map for Nav1.7 pIC50, showing the detailed SAR of the phenyl ring.
The color of the wedge reflects the direction the activity is going: red means the activity is decreasing; green means the activity is increasing between the pair.

The shading echoes the disparity, which relates to how steep the activity cliff is. The result is a focused view of the SAR around a chosen compound.

Figure 3 also shows the activity view around the unsubstituted phenyl (pIC50 6.6). This view clearly shows that para substitution is always detrimental for NaV1.7 activity: ortho substitution is beneficial, especially with a small halogen like Fluorine; and meta substitution is also in general beneficial. Ortho, ortho substitution, instead, is less tolerated.

Design of new molecules using Torch

One of the major advantages of field based alignment is that it is agnostic to the chemical series that is being aligned. This can be used to aid in the design of new compounds in Torch by aligning diverse actives to a common reference and then transferring key functional groups across series. In this example, I use the crystal structure of HDT, a potent Cyclin-Dependent Kinase inhibitor, bound to CDK2 (PDB code 1OIT) to modify the design of an oxime based inhibitor.

As can be seen in Figure 4, HDT interacts with the hinge region of the active site of CDK2 by making two H-bond interactions with the backbone carbonyl and NH of Leu 83, and a H-bond interaction with Lys 33. The sulphonamide group also makes H-bond interactions with Asp86 (not shown).

Figure 4. HDT bound to the CDK2 active site.
In this design experiment, more potent CDK2 inhibitors are designed starting from the 2D structure of compound CK3 (Figure 5), a smaller and less potent CDK2 inhibitor with a Ki 2200 nM using the interactions of HDT as a guide.
The 2D structure of CK3 (drawn with a favorite drawing package) was imported in Torch by copy/paste. CK3 was then aligned to HDT using an accurate but slow conformation hunt followed by a ‘Normal’ (field based) alignment.

Figure 5. Structure of CK3, an inhibitor of CDK2 (Ki 2200 nM).
Figure 6 shows the results of the alignment experiments. CK3 (grey) is nicely superimposed to HDT (pink) and it is straightforward to see which changes should be made to increase CDK2 potency, replacing the formamidine moiety with a phenyl ring, possibly decorated with a sulphonamide or other H-bond acceptor group in the para position.

Figure 6. CK3 (grey) aligned to HDT (pink).
This change can be easily done in the molecule editor available in Torch, using the reference structure as a guide. As changes are made in the editor, the similarity score (Figure 7) is updated on the fly by clicking on the ‘Minimize’ and ‘Optimize Alignment’ buttons. Once the editing is completed, clicking the ‘Align’ button in the molecule editor will prompt Torch to carry out a full conformation hunt and field alignment on the new design.

Figure 7. The Molecule Editor in Torch.
The structure of CK6, an analogue of CK3 with CDK2 Ki 70 nM, aligned to HDT in Torch are shown in Figure 8 (left). The superimposed crystal structures of CK6 and HDT as in the PDBs 1PXN and 1OIT, respectively shown in Figure 8 (right). The alignment in Torch almost perfectly matches the crystallographic alignment of these two ligands in the CDK2 active site.

Figure 8. Left: CK6 (grey) aligned to HDT (pink) using Torch. Right: superimposed crystal structures of CK6 (grey) and HDT (pink) as in PDB entries 1PXN and 1OIT.

Multi-Parameter Scoring

Multi-Parameter Scoring in Torch helps medicinal and synthetic chemists assess the overall physico-chemical profile of the compounds of interest using colors and radial plots. As can be seen in Figure 9, columns in Torch are colored according to a profile set up in the Torch preferences. Properties perfectly matching the desired profile are colored in green, those with an acceptable value in yellow, while those with an unacceptable value in red.

The profile can be tailored to the specific project needs in the Radial Plot Properties window. In this window, a weight can be also associated to each property based on its importance in the ideal project profile. The score and fit to the project profile for each molecule is then summarized in the radial plot.

The radial plot is based on the idea that molecule properties that are ‘perfect’ should be displayed at the center of the radial plot. Thus, a molecule with perfect or near perfect properties should have a radial plot with a small encapsulated area (shown in green). Conversely, poor properties would be plotted at the edge of the radial plot such that a molecule with sub-ideal properties would have a radial plot with a large enclosed area (this can be reversed using the Radial Plot Preferences).

In Figure 9, you can see the column coloring for the CDK2 project. Comparing the color coloring of CK3 and CK6, most properties have values matching the ideal property profile. CDK2 Ki has significantly improved from CK3 to CK6, while lipophilicity (SlogP) is less good in CK6. CK3+phenyl (Figure 9, Molecules table) is slightly less active than CK6 and its lipophilicity is high with respect to the other two compounds: another good reason for including a hydrophilic H-bond acceptor in the para position of the phenyl ring.

The radial plot properties are combined into a single score that represents the overall fit of molecule to the ideal project profile. Radial plots can be sorted and filtered based on this score, making it easier to select the best candidates for your projects.

Figure 9. Multi-parameter scoring in Torch.


This blog highlights some of the additional features in Torch, the powerful molecular design tool for medicinal and synthetic chemists.

Additional functionality available in Torch includes the capability to:

  • run virtual screening of up to 500 molecules
  • use Activity Atlas and 2D/3D-QSAR models built with Forge
  • create interactive multi-series scatter plots and histograms of biological or physical properties
  • import calculated and/or measured physical properties and data from an external web service through a REST interface.

Contact us to benefit from this functionality and try the full power of Torch.

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.