Sneak peek: PickR to select electrostatically diverse monomers for libraries

In a presentation at the Cresset User Group Meeting in 2016, Nik Stiefl and Finton Sirockin from Novartis discussed the selection of building blocks for DNA encoded libraries using electrostatic and shape diversity as the key descriptor. This work was powered by a custom binary and scripts written by Cresset. Over the last couple of years this approach has been applied to a wider range of library designs and has gained a reputation as a method of choice for many library designs.

PickR will be a new tool that formalizes the approaches that we developed in collaboration with Novartis. It is a command line binary that provides a diverse pick of reagents to be incorporated into a library. Unlike other approaches, PickR uses the electrostatic and shape properties of molecules to generate the descriptor matrix that is used as the basis for the diversity pick.

Generating diversity using a 3D property is not straight forward. It is necessary to explore conformations of R-groups and rotate them about the proposed connection point in order to fully understand the distribution of properties. Much of this was well described in Nik and Finton’s presentation (slides 12-15). I will leave a formal discussion to the final release announcement.

Application of PickR to amino acid side chains

I applied a dataset of approximately 1,000 amino acids that can be purchased from eMolecules. The raw reagents were processed to convert the side chains into R-groups with the C-alpha atom being converted to Iodine (all other Iodine containing reagents were excluded as were those containing Br and those with side chains >150Da). Using PickR, I generated a 3D similarity matrix for the side chains, aligning on the C-alpha to C-beta bond. 100 clusters were requested initially.


3D representations of all 100 clusters generated from amino acid side chains, aligned to each other using the I-C bond of the fragments.

Looking at the results, there are some very nice relationships. For example, in Cluster 2, together with tyrosine, are other phenolic side chains but also an indazole that contains the donor-acceptor motif.


2D representations of all the side chains in the same cluster as tyrosine (highlighted).

Along with the indole of trytophan are other substituted indoles, pyropyridines and benzofuran. In with the isobutyl side chain of leucine are a number of cyclic analogues which I expect would cause issues with many 2D similarity methods. Interestingly, indoline is placed together with the equivalent of homo-phenylalanine.


2D representations of the leucine related cluster.


2D representations of the homo-phenylalanine related side chains


The cluster containing the phenylalanine side chain highlights the major difference of PickR over other methods – R-groups are clustered on 3D electrostatic properties. Hence, together with the phenylalanine side chain you have thiophenes and pyroles but few other aromatics – pyridine and pyrimidines go to their own clusters because using electrostatics they are quite different to a plain phenyl ring.


3D and 2D pictures of all side chains in the phenylalanine cluster


Request project file and find out more

Contact me to receive the full results in a Forge project file.

Contact your account manager if you are interested in learning more about PickR.

Integrating Jupyter Notebook into Flare

In a recent blog post I have shown the integration of the Jupyter QtConsole in Flare.

The Jupyter QtConsole nicely fits in with the rest of the Flare GUI and provides a comfortable Python shell environment with most of the nifty Jupyter features such as history, TAB completion, syntax highlighting, embedding of images, etc.

Since I published that post, I have started thinking that it would have been great to embed a Jupyter Notebook, as it offers an even richer interface: most importantly, it enables editing and running individual code cells, thus constituting the ideal environment for Python enthusiasts.
There were a few technical hoops that I had to jump through to get this to work, but I finally managed.

So here I am proudly presenting the Flare Python Notebook, i.e. an instance of the Jupyter Notebook embedded into the Flare GUI which has direct access to the Flare GUI objects and methods just as the Python QtConsole (Figure 1).

Figure 1. A screenshot showing the new Python Notebook embedded in Flare.
To demonstrate the new fuctionality, last week at the 7th RDKit UGM in Cambridge I gave a lightning talk showcasing a sample Python Notebook which downloads a set of AChE inhibitors from ChEMBL, loads them into Flare, generates 3D coordinates and field points, and finally docks them to a crystallographic AChE protein downloaded from the Protein Data Bank.
The RDKit is used to compute 2D similarities and maximum common substructure across ligands and to generate 2D molecule layouts. RDKit molecules are fully interoperable with Cresset molecules within Flare, so Cresset 3D technologies and RDKit methods can be synergycally combined in one Python workflow.
Matplotlib, NumPy and SciPy are used to generate a scatterplot with a regression line and compute some statistics.

Now, on to the Jupyter Notebook:

The Flare Python Notebook unleashes the full potential of embedding a highly interactive Python environment within the Flare GUI.
RDKit cheminformatics methods and Cresset 3D technologies can be used side-by-side and their results visualized in real time while writing Python code, making the development cycle much more efficient and expedite.
The Jupyter Notebook has made scripting a simple everyday task for cheminformaticians, bioinformaticians and data scientists. I am confident that the Flare Python Notebook will do the same for CADD scientists and computational chemists.

And if you are not a Python guru, I can help you out; actually, I can’t wait to write my next script!

We will be releasing the Jupyter Notebook integration to our GitLab repository of Python extensions as soon as it is finalised. To find out more about Flare or to talk about how the Python integration can help you in your research or to request a Python script to achieve a particular task within Flare, then please contact us.

Using Python in Flare to find common contacts

In a recent blog post Pat Walters nicely used the structures of Viagra and Cialis when bound to PDE5 to argue that scaffold hopping between these two drugs was not a task that could be performed easily. He used Python to demonstrate that each drug interacted with siginificantly different parts of the protein and that they only shared interactions with 4 residues. Inspired by this, I sought (with the help of Paolo Tosco) to implement Pat’s code in Flare.

Paolo has been working on the implementation of a Jupyter notebook within Python (see his post here) and this provides the ideal environment to implement and discuss code to explore the common and specific interactions of Cialis and Viagra with PDE5. The notebook contents are shown in the iframe below.

As you see the output (last line) is the same as originally reported. However, with the addition of the Flare interface we are able to create a nice visual representation of the results, rendering the common and ligand specific residues differently. The script takes around a 30 seconds to run:

If you would like to learn more about Flare and using Python to customize, script or automate common actions or you would like to try the code out for yourself then please contact us. The current range of Python extensions for Flare are avaiable from our GitLab repository.

Investigating the SAR of XIAP ligands with Electrostatic Complementarity maps and scores


Electrostatic Complementarity™ maps implemented in Flare™,1 Cresset’s structure-based design application, were used to investigate the protein-ligand electrostatic interactions and the Structure-Activity Relationship (SAR) of a small set of inhibitors of the X-linked IAP (XIAP)-caspase protein-protein interaction. A good correlation was also obtained between XIAP-BIR3 affinity and the Electrostatic Complementarity scores for the same data set.


Inhibitor of apoptosis proteins (IAPs) are key regulators of antiapoptotic and pro-survival signaling pathways.2-4 Their deregulation occurs in various cancers and is associated with tumor growth, resistance to treatment and poor prognosis. This makes them an attractive target for anticancer drug discovery.5-7  The best characterized IAP, X-linked IAP (XIAP), exerts its antiapoptotic activity by binding and inactivation of caspases 3, 7, and 9 via its BIR domains. Disruption of the protein-protein interaction (PPI) between XIAP-BIR domains and caspases via small molecules is a promising strategy to inhibit XIAP. However, drugging PPIs can be particularly challenging due to their unusual binding interfaces, which are unlike classical binding sites generally flat and large.8

A recent paper from Astex9 reports that the XIAP-BIR3 activity of the small dataset of antagonists in Table 1 is increased by the introduction of electron-withdrawing substituents on the indoline ring, and shows a nice correlation between the XIAP-BIR3 pIC50 and Hammett’s σp.

In this case study, we used the Electrostatic Complementarity maps available in Flare to investigate the protein-ligand electrostatic interactions and the SAR of the molecules in Table 1. Electrostatic Complementarity scores calculated with Flare were used to quantitatively model XIAP-BIR3 pIC50.

Table 1. XIAP-BIR3 affinity of C-6 substituted indolines.8


Protein preparation

The 5C7A ligand-protein complex was downloaded from the Protein Data Bank into Flare and prepared using the Build Model10 tool from BioMolTech,11 to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes and assign optimal protonation states to the protein structure. Any truncated protein chain was capped as part of protein preparation. The binding site was visually inspected to check for correct protonation states of ligands and amino acid side chains and re-optimize water orientations of suboptimal water hydrogen bonding networks. We chose to keep only water molecules in and close to the binding site that have at least 2 hydrogen bonding contacts to the protein or at least 1 hydrogen bond to ligand and protein for electrostatic complementarity calculations. As many of the modeled binding modes (e.g., compounds 9, 11, 15, 16) clash with the flexible side chain of Lys297 (Figure 1), the side chain atoms were minimized with the XED force field12 for each ligand. The resulting receptors were used to compute the electrostatic complementarity of the respective compounds.

Figure 1. The PDB: 5C7A ligand-protein complex.

Data set construction

The compounds in Table 1 were drawn using the molecule editor in Flare, starting from the crystal structure of the ligand in PDB:5C7A (compound 7 in Table 1). The 11 compounds were then aligned in Forge13 to the 5C7A ligand, using a Maximum Common Substructure alignment to minimize the conformational noise in the common indoline-piperazine scaffold.

Electrostatic Complementarity surfaces and scores

Electrostatic Complementarity maps and scoring functions are an extension of Flare’s Protein Interaction Potentials based on Cresset’s polarizable XED force field. In contrast to classical force fields that rely on atom-centered charges, XED enables description of anisotropic charge distribution around atoms which is usually only possible with ab initio approaches. Polarization effects and description of atomic charge anisotropy are especially useful for computing electrostatic properties of aromatic or unsaturated hydrocarbons, sp2 hybridized oxygen atoms, sp or sp2 hybridized nitrogen atoms, and aromatic halogens (sigma hole of Cl, Br, and I).14-16

To calculate the Electrostatic Complementarity map for a ligand towards a protein of interest, the solvent-accessible surface is first placed over the ligand. A calculation of electrostatic potentials due to the ligand and the protein is then carried out at each vertex on the surface.

These potentials are then scaled, added together, and normalized to yield the Electrostatic Complementarity score. Perfect electrostatic complementarity means that at each vertex point the ligand electrostatic potential value is paired with a protein electrostatic potential value of the same magnitude with reverse sign. Regions of the ligand surface where there is electrostatic complementarity with the protein are colored green, while the regions where there is a electrostatic clash are colored red. A more detailed description of the electrostatic potential and complementarity methodology will be presented elsewhere.17

The Electrostatic Complementarity scores quantify the ligand-protein electrostatic complementarity with three different metrics suitable for diverse protein-ligand scenarios.

The first computed score (‘Complementarity’) is the normalized surface integral of the complementarity score over the surface of the ligand (effectively the average value of that score over the surface of the ligand).

The other two scores (‘Complementarity r’ and ‘Complementarity rho’) are the Pearson’s correlation coefficient and the Spearman rank correlation coefficient, respectively, which are computed on the raw ligand and protein electrostatic potentials sampled on the surface vertices.

All three measures range from 1 (perfect complementarity) to -1 (perfect clash) but have different characteristics. The Complementarity score includes some compensation for desolvation effects, and so may be more robust when these are significant. The Pearson and Spearman correlation coefficients can provide a better indication of ligand activity in some cases, but are more susceptible to noise (r more than rho). The Spearman’s rho number is more robust against background electric fields, which may be useful if the computed protein electric potential is being biased by a large net charge on the protein.

The calculation is fast and predictive: scoring a hundred ligands normally takes less than a couple of minutes on an average laptop and gives important insights into protein-ligand electrostatics, which typically correlate with compound activity.

Mapping the electrostatics of the XIAP active site

The Electrostatic Complementarity map of compound 7 in the XIAP active site (PDB: 5C7A, Figure 2 – left) shows a strong electrostatic clash (red) in the region above the indoline ring. This is caused by an area of negative electrostatic potential in the protein’s active site, generated by the backbone carbonyl of Gly306 and the phenolic oxygen of Tyr324 (Figure 2 – middle), clashing with the negative electrostatic field associated with the indoline ring (Figure 2 – right). A less pronounced electrostatic clash can be seen between the positive electrostatic field of the protonated side chain of Lys297 (Figure 2 – middle) and the positive electrostatic field of the sigma hydrogens of the indoline ring (Figure 2 – right).

According to this map (and in agreement with the reported correlation8), electron-withdrawing substituents which make the indoline ring less electron-rich are expected to increase XIAP binding. Substituents associated with a more negative (or less positive) electrostatic field, favoring the interaction with the protonated side chain of Lys297, should also be beneficial.

Figure 2. Left: Electrostatic Complementarity map for the PDB:5C7A ligand (green: good complementarity; red: electrostatic clash). Middle: protein electrostatic potential map for PDB:5C7A (red: positive; cyan: negative). Right: ligand fields for the ligand in PDB:5C7A (red: positive; cyan: negative.

Electrostatic Complementarity and XIAP SAR

Figure 3 shows the Electrostatic Complementarity maps for the compounds in Table 1, shown in order of increasing XIAP-BIR3 activity from left to right.

A clear trend can be observed as we move from the electron-donating substituents (-NH2, -OMe), to the electron-withdrawing substituents -F, -Cl, -SO2Me. These make the indoline ring less electron-rich, reducing the clash with the negative electrostatic of the XIAP active site.

Figure 3. Electrostatic complementarity maps for some of the ligands in Table 1 (green: good complementarity; red: electrostatic clash).

 The substituents for the three most potent compounds are also associated with a negative ligand field of their own (Figure 4), favoring the interaction with the protonated side chain of Lys297, according to our initial hypothesis.

Figure 4. Negative ligand fields (cyan) for compounds 17, 15 and 16.

 These qualitative observations are confirmed by the nice correlation (r2 = 0.671) between XIAP-BIR3 pIC50 and the ‘Complementarity rho’ score shown in Figure 5.

Figure 5. Plot of XIAP-BIR3 pIC50 versus Complementarity rho.

Electrostatic Complementarity scores and MW

We monitored the correlation between MW and XIAP-BIR3 affinity/Complementarity rho to verify whether the Electrostatic Complementarity scores provide information which goes beyond the use of simple physico-chemical descriptors for drug design.

The correlation between MW and XIAP-BIR3 pIC50 (r2 = 0.613, Figure 6 – left), would possibly point towards a space filling effect as the simplest explanation of the changes in XIAP affinity in this data set.

However, the low correlation between Complementarity rho and MW (Figure 6 – right) confirms that the Electrostatic Complementarity scores are size independent.

Using the Electrostatic Complementarity scores for quantitative SAR modeling, therefore, generates trends completely independent from size effects.

Furthermore, Electrostatic Complementarity maps provide visual insight into ligand-protein binding and SAR which cannot be derived from traditional, simple physico-chemical descriptors such as MW and Hammett’s σp, thus providing invaluable information for drug design.

Figure 6. Left: Plot of XIAP-BIR3 pIC50 versus MW. Right: Plot of Complementarity rho versus MW.


Application of Electrostatic Complementarity to a reported XIAP-BIR3 data set showed that our method can detect and quantify electrostatic differences in XIAP ligands that cause changes in bioactivity. Electrostatic Complementarity scores and maps in Flare V2, based on Cresset’s polarizable XED force field, provide rapid activity prediction with visual feedback on new molecule designs. They provide useful information for understanding ligand binding and SAR and can be used for rapidly ranking of new molecule designs.

References and Links

  2. Salvesen, G. S. et al., Rev. Mol. Cell Biol. 2002, 3 (6), 401-10
  3. Gyrd-Hansen et al., Nat. Cancer 2010, 10 (8), 561-74
  4. Silke, J. et al., Cold Spring Harbor Perspect. Biol. 2013, 5 (2), a008730
  5. I et al., Clin. Cancer Res. 2004, 10 (11), 3737-3744
  6. Mizutani, Y. et al., Int. J. Oncol. 2007, 30 (4), 919-925
  7. Fulda S. et al., Nat. Rev. Drug Discovery 2012, 11 (2), 109 -124
  8. Arkin, M. R. et al., Chem. Biol. 2014, 21 (9), 1102-1114
  9. Chessari, G. et al., J. Med. 2015, 58 (16), 6574-6588
  10. V. Stroganov et al., Proteins 2011, 79 (9), 2693-2710
  14. Vinter, J. G., Comput. Aided Mol. Des. 1994, 8 (6), 653–668
  15. Vinter, J. G., Comput. Aided Mol. Des. 1996, 10 (5), 417–426
  16. Chessari, G. et al., Chem. Eur. J. 2002, 8 (13), 2860–2867
  17. Bauer, M. R. & Mackey, M. D. et al., manuscript in preparation

Rapid and accessible in silico macrocycle design


Macrocyclization of pharmaceutical compounds plays an increasing role in drug discovery. Macrocycles can provide several advantages such as favorable drug-like properties, increased selectivity and improved binding affinity.

The assessment of potential macrocyclizations is a challenging computational problem. Linkers should be synthetically feasible, not too flexible, have a sensible conformation and be pharmacophorically compatible with the active site. Few computational strategies are available for this task, so most design is done ad hoc.

Here we present a modification of our existing bioisostere searching tool, Spark, to this problem. Traditional bioisostere searches specify a fragment to replace in the starting ligand, and look for similar fragments in a database. Instead, Spark assesses bioisosteric replacements in product space, which allows more complex experiments. A modification of the Spark scoring method to include similarity to other ligands known to bind in the region of the linker, as well as constraints from the protein active site and known pharmacophoric requirements, allows sensible ranking of potential linkers.

Here we present case studies of designing macrocyclization strategies for BRD4 and for Orexin 2. The Spark algorithms enable a rapid assessment of the ideal linker length and suggested chemistry for each cyclization option.

See the ‘Rapid and accessible in silico macrocycle design’ presentation Rob Scoffin presented in the COMP Division at the 256th ACS National Meeting.

Outstanding new 3D graphics in Spark 10.5.5

A new patch level release of Spark™, our scaffold hopping and bioisostere replacement application, is now available for download by all Spark users. Spark 10.5.5 includes considerable improvements to the look and feel, rendering and performance of the graphics of the 3D window.

Figure 1. Improved 3D graphics in Spark 10.5.5.

Spark 10.5.5 also includes a small number of additional improvements and bug fixes:

  • Improved support for the configuration of proxy servers
  • Improved Spark start up times when using databases sitting in a remote location on a slow connection
  • Improved support for high-dpi displays
  • Fixed issue which caused the effect of the application of pharmacophore constraints to be overestimated in some circumstances
  • Fixed issue on macOS which prevented to dock back in the desired position any dock window moved outside of the main Spark interface
  • Fixed rare issue in the wizard where in some occasions the desired hydrogen atom could not be picked for replacement.

Download Spark 10.5.5

To ensure you benefit from the improved 3D graphics, and other improvements and bug fixes, keep an eye out for an email with download links and upgrade Spark at your earliest convenience.

If you are not currently a Spark customer, please request a free evaluation.

Contact us if you have queries.

Launch of Cresset Python extensions for Flare

We have launched three new repositories for Python scripts that extend the functionality of FlareTM, our structure-based design platform. These new repositories are available to all Flare users free of charge.

Last month, Paolo Tosco, explained the advantages and opportunities offered by the Flare Python API to computational chemists and developers.

But what if you are not familiar with Python scripting, and you just want to use one of the scripts developed by us, which we showed at the Cresset User Group Meeting 2018? Or if you would like to run Flare tasks from the command line? Or maybe you know Python well, but could benefit from some scripting examples, just to get yourself started with the Flare Python API. These new repositories provide the solutions. They are:

  • Flare Python extensions – Cresset written scripts that extend the functionality of Flare
  • Flare Python pyflare – Command line scripts that use pyflare to create command line workflows
  • Flare Python developers – Example scripts that can be used by developers as templates to write their own extensions.

Below I will discuss the different types of scripts and show you some interesting examples of additional functions you can add to Flare through scripting.

Flare API extensions: Use the power of Python within the Flare GUI

Download the Flare API extensions.

What do we mean by ‘extensions’? These are a collection of Python scripts which add powerful new functionality to Flare. After installing them, a new ribbon tab called ‘Extensions’ will be added to the Flare GUI, containing buttons to access this new functionality.

Figure 1. The ‘Extensions’ tab in Flare 2.0.

As you hover with the mouse over each of the buttons, a tooltip will appear providing a short explanation of the extension’s function.

For example, the ‘Ramachandran Plot’ extension will show the Ramachandran plot for the protein of interest.

Figure 2. Ramachandran plot for PDB:5C7A calculated with the ‘Ramachandran Plot’ extension.

Another nice snippet of extension functionality is the ‘Show in RCSB’ addition to the Proteins table context menu.

Figure 3. Choosing the ‘Show in RCSB’ extension from the context menu opens the PDB:5C7A entry in the RCSB.

If you are used to the highly interactive environment of Jupyter® notebooks, then you should definitely install the ‘Python QtConsole’ extension, which adds a Jupyter QtConsole dock to the built-in Python Interpreter and Python Console docks. The Python QtConsole provides all the nifty Jupyter features, i.e., TAB completion, auto-indentation, syntax highlighting, context help, inline graphics, and more.

Figure 4. The highly interactive environment provided by the Python QtConsole.

Finally, there is a whole group of Cresset extensions dedicated to making Flare communicate with other Cresset products. For example, choosing the ‘Align’ extension will enable you to run a Forge alignment for the ligands in your Flare project, without leaving Flare. You will need a Forge license for this to work; click here if you wish to request a free evaluation of Forge.

Figure 5. The ‘Align’ extension.

pyflare scripts: run Flare from the command line

These scripts allow all the main Flare functions to be accessed through the pyflare command line Python interpreter.

Figure 6. Running a Flare Python script outside Flare using the pyflare interpreter.

This is useful when you need to carry out a completely automated task, for example an overnight preparation of a panel of proteins followed by docking of several ligand series, distributing it on a cluster via a queueing system for maximum performance.

Download pyflare scripts to:

  • Dock ligands to a protein using the Lead Finder™ algorithm
  • Prepare your protein
  • Calculate Electrostatic Complementarity™ scores
  • Minimize the protein active site
  • Run a 3D-RISM analysis
  • Run a WaterSwap analysis
  • Calculate and export protein field surfaces.

Scripting examples for developers

This GitLab repository contains a few interesting scripting examples to help Python developers get started with writing their own extensions and scripts with the Flare API.

Give it a try

These examples can be downloaded for free from GitLab by all Flare customers, clicking on the links above and following the download instructions.

If you questions about the use of Python extensions for Flare, feel free to contact Cresset support.

Request a free evaluation of Flare.

Flare API: A new playground for computational chemists and developers

Flare 2.0 comes with a full-featured Python API which enables both high-level access to scientific functionality and low-level access to the graphical user interface and internal processes. In addition to Flare methods, you will have access to RDKit, NumPy, SciPy, and to virtually any other Python module that can be pip-installed. RDKit molecules are treated just as native Flare molecules, and can be loaded interchangeably to Flare’s ligand table. This means that you can generate a virtual 2D library using RDKit reaction SMARTS and then turn it into a 3D library of field-enabled Cresset molecules without ever leaving the Flare environment.

Additionally, if you realize that you often use a certain Python script as part of your Flare workflow, you may decide to associate it to a custom control in the Flare ribbon user interface, be it a button, a menu, or a complex dialog with signals and callback functions (Figure 1).

Figure 1. An example of the custom controls that can be added to the Flare ribbon.
We have done our best to write a clear and easy-to-browse HTML documentation, but even more importantly we have come up with a number of sample scripts which cover a variety of use cases, in order to get you started as quickly as possible.

Python commands can be loaded and executed in Flare in a number of different ways, ranging from highest automation to highest interactivity.

For example, when you need to carry out a completely automated task, such as overnight preparation of a panel of proteins followed by docking of several ligand series, the most convenient option is to write a Python script that runs outside the Flare GUI, such that it can be distributed on a cluster via a queueing system for maximum performance. This use case is covered by the pyflare binary, which is effectively a Python interpreter just as the familiar python binary, slightly modified to play well with the rest of the Flare ecosystem (Figure 2).

Figure 2. Running a Flare Python script outside Flare using the pyflare interpreter.
If your goal is to run a simple Python one-liner to, for example, list all cysteine residues in a protein chain or to print the distance between a ligand atom and a couple of key residues in the active site, the embedded Python Console is probably the simplest and leanest option – run the command and examine its output in the text port (Figure 3).

Figure 3. Entering simple one-liners in the Python Console widget.
If you believe you will need to go through some iterations of a code-run-test-debug workflow on a somewhat more complex script, you will most likely choose the embedded Python Interpreter widget, which allows you to load a script, interactively edit it inside Flare and then save your modifications (Figure 4). Both the Python Console and the Interpreter come with a multi-tabbed interface that makes it possible to work on multiple Python snippets at the same time.

Figure 4. Loading and editing a script in the Python Interpreter widget.
If you are used to the highly interactive environment of Jupyter notebooks, then you are going to love the Python QtConsole embedded in Flare. This widget provides all the nifty Jupyter features, i.e., TAB completion, auto-indentation, syntax highlighting, context help, inline graphics, and more (Figure 5). Type Python commands, examine molecules, draw plots – all in the same window.

Figure 5. Visualizing text output and inline graphics in the Python QtConsole widget.
If you would rather prefer to use Jupyter Notebooks in the traditional way, i.e., multiple tabbed sessions inside your favorite browser, that’s entirely possible, too. You will even be able to look at ligands and proteins in 3D inside your notebook, including Cresset surfaces and field points (Figure 6).

Figure 6. Running a Flare workflow inside a Jupyter Notebook outside the Flare GUI application.
In summary, programmatic access to Flare methods is very appealing to computational chemists looking for ways to automate tasks or run complex workflows on multiple datasets. On the other hand, the possibility to extend the core functionality as well as the graphical user interface with own methods makes Flare the ideal playground for Python developers.

For academic researchers, Flare is an opportunity to make their science more broadly accessible through integration into a user-friendly environment, while corporate users will appreciate the possibility to augment Flare capabilities with in-house REST services and tools. The possibility to code in Python taking advantage of the highly interactive experience provided by the Jupyter QtConsole and Notebook is a further incentive to make Flare the environment of choice for computational chemistry workflows.

Request a free evaluation of Flare.

Flare™ V2 released: Introducing the new science and functionality of Cresset’s structure-based design application

Version 2 of Flare™, our application for fresh insights into structure-based design, is now available. I will briefly introduce the new science and functionality included in this version, which will be presented in full at the Cresset User Group Meeting on June 21-22.

Predicting activity using Electrostatic Complementarity™: You asked, we listened

Since the initial release of Flare, you repeatedly asked us to develop a smart way of quantifying the complementarity of ligand vs. protein electrostatics and suggested that this would be a rapid method for prioritizing new molecule designs.

Your requests have led to the introduction of Electrostatic Complementarity (EC) scores and maps in Flare V2, based on Cresset’s polarizable XED force field. These provide rapid activity prediction with visual feedback on new molecule designs, and prove invaluable for understanding ligand binding, structure-activity relationships and the ranking new molecule designs.

Figure 1. Left: the less active XIAP analog is less complementary (red region) to the PDB: 5C7D binding site than the more active XIAP analog in the centre (green region).  Right: the Electrostatic Complementarity score is highly correlated with the experimental activity of XIAP analogs.

Electrostatic Complementarity scores quantify the ligand-protein electrostatic complementarity with three different metrics suitable for diverse protein-ligand scenarios. The calculation is fast and predictive: scoring a hundred ligands normally takes less than a couple of minutes on an average laptop and gives good correlation with activity in the majority of cases.

Electrostatic Complementarity maps are based on a calculation of electrostatic potentials for the ligand and the protein on the surface of the ligand. These potentials are then added together, normalized and scaled. Regions of the ligand surface where there is perfect electrostatic complementarity with the protein are colored green, while the regions where there is a perfect electrostatic clash are colored red.

Enhanced protein surface coloring

Faster and improved surface generation code, and new protein surface coloring options to give you more insights into protein-ligand interactions and support molecule design, are also included in this release.

Figure 2. Left: protein electrostatic potential map for PDB: 5HLW (red: positive; cyan: negative). Middle: ligand fields for the ligand in PDB: 5HLW (red: positive; cyan: negative). Right: electrostatic complementarity map for PDB: 5HLW (green: perfect complementarity; red: perfect clash).

Coloring the protein surface according to the Wimley-White [1] residue hydrophobicity value is an excellent way of visualizing hydrophobic areas of the protein active site.

Figure 3. The surface of the binding side of PDB: 5HLW is colored by Wimley-White residue hydrophobicity from yellow (hydrophobic) to blue (hydrophilic).

Ensemble docking

Rapidly and easily dock your ligands in a single experiment to multiple protein conformations using ensemble docking. Results are saved as a list of docked poses for the ligands included in the study, each associated to a specific protein conformation.

Figure 4. Results of an ensemble docking experiment in Flare. Each pose is associated with a specific protein conformation, making browsing of results easy.

Enhanced ligand design functionality

Significant improvements have been made to the ligand functionality in Flare V2.

Radial plots and Multi-Parameter Scoring

Radial plots are useful to gain immediate visual feedback about how each ligand matches the ideal physico-chemical profile for your project. To support Multi-Parameter Scoring, radial plot properties are weighted and combined into a single score that represents the fit of the ligand to the ideal physico-chemical profile. The radial plot score can then be used to filter or sort the ligands.

Good Middling Poor
Figure 5. Radial plots and radial plot scores for ligand showing a match to the ideal physical-chemical profile ranging from good (left) to poor (right).


Flare V2 enables the definitions of filters (Figure 6) to show only the ligands that conform to a desired set of rules. This includes filtering on numerical values, text data, Boolean values, tags, ligand structure using either a SMARTS string or a substructure sketched into the Flare Molecule Editor.

Figure 6: The Filters window in Flare V2.


Use the Storyboard window to capture and replay scenes recording all details from the 3D window. Each scene can be easily annotated and recalled when needed.

Figure 7: The Storyboard in Flare V2 showing four scenes and their titles together with notes about each scene.

Flare Python® API

The new Python API lets you create your own workflows, automate your common tasks, expand Flare with Python modules and add custom controls. It gives full access to all of Flare’s capabilities, including the RDKit cheminformatics toolkit.

Flare can be upgraded with Python modules for graphing statistics, Jupyter® notebook integration and much more.

Flare V2 makes advanced structure-based design techniques, such as Electrostatic Complementarity, multiparametric scoring and Python scripting, accessible through an intuitive GUI.

See the benefits Flare V2 can bring to your project

With over 200 new or improved features, Flare V2 is built to make structure-based design easy and accessible while incorporating cutting edge scientific methods. I encourage you to upgrade your version of Flare at your earliest convenience.

If you are not currently a Flare customer, please request a free evaluation.

Contact us if you have queries relating to this release.


  1. Wimley WC & White SH (1996). Nature Struct. Biol. 3:842-848.


A sneak peek into Flare V2: A major advancement for structure-based design with Flare

Flare V2 is in the final rounds of testing, which means the release announcement is imminent. Ahead of the user group meeting, where we will be presenting this major advancement, this post takes a sneak peek at some of the new features in this version.

New coloring options

Completely rewritten surface generation code results in faster and better surfaces with quality options built in to the surface creation dialog. This is combined with new coloring options for new surfaces to give you more insights into your proteins and ligands.

Figure 1: (a) New surface coloring options in Flare V2, and (b) PDB code 4MBS with a hydrophobic surface colored yellow (hydrophobic) to blue (hydrophilic).

Improved Z-clipping

Making pictures is key to communicating your insights on protein-ligand binding. Flare V2 has major improvements to the Z-clipping to enable you to get the view that you want. In addition, to apply a specific clipping plane to an individual surface, you now have the option to exclude ligands from the clip altogether. This option makes a significant impact on pictures of binding sites that are completely buried.

Figure 2: PDB 1IKW showing the ability to selectively clip proteins. (a) Ligand clipping often makes it difficult to get the picture you want whereas (b) disabled ligand clipping in Flare V2 gives you more options to communicate key insights.

Figure 3: Flare V2 gives the option to clip individual surfaces independently of other objects. Here a clipping plane is added only to the electrostatic surface enabling the visualization of protein residues that are above the ligand in combination with a surface.

Other features contribute to a major advancement for Flare

The new protein surfaces are complemented by new options for ligand surfaces, the new storyboard panel to capture and replay key 3D insights and many new features for ligands. Taken together with the Python API this release of Flare is a major advancement in this innovative new application for structure-based design.

Find out more and get hands-on

Register for the up-coming user group meeting to find out more about Flare V2, network with existing users and receive free training at one of the hands-on workshops.

Request an evaluation.