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: ligand fields for the ligand in PDB: 5HLW (red: positive; cyan: negative). Middle: protein electrostatic potential map for 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).

Filters

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.

Storyboard

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.

Reference

  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.

In the Cresset lab: Molecular design re-imagined

Molecule design is a central task in drug discovery. It is both personal and collaborative, easy to do in 2D (on the fume hood or using a drawing application) but more productive when combined with the 3D environment of the chosen target. We have been thinking about how best to provide you with a molecule design application that satisfies all these requirements. Whilst Torch has many favorable attributes that make it a popular choice, it cannot satisfy the two key features that you request of it – to be able to sketch in 2D in a popular drawing application, and to work in a collaborative environment. Project TorchWeb is now underway to satisfy these requests. This project will deliver our next generation of molecule design application and, as the name suggests, will be entirely web based.

We have recently completed a proof of concept application, part funded by a UK government grant, and are delighted with the two key technologies it has at its heart – the ability to work exclusively in 2D and yet have the 3D context of your design immediately available, and the option to share your work ‘live’ with one or many collaborators.

TorchWeb reimagines the molecular design process within a web browser. Using 2D and 3D representations of the molecule together with Cresset’s electrostatic descriptors gives an detailed view of your new design.

Thinking in 2D and 3D simultaneously

As chemists we are taught to synthesize molecules using 2D representations. This makes our life simplier, condensing complex situations down to a 2D language that we use to think about and communicate our ideas. However, molecules are 3D and exert their effects in 3D. Why do we not link these two using the computer to extrapolate our ideas from 2D to 3D automatically? This is the philosophy behind design in TorchWeb.


Changes in the 2D window are automatically interpreted into 3D.

Collaboration as standard

Modern drug discovery teams are often geographically diverse, both within a single company and across multiple organisations. In TorchWeb we have developed a collaboration layer that enables you to invite other users to share your current design environment. We aim to remove artificial barriers to working with others in your team, catalyzing new ideas, stimulating new thoughts and focusing on current challenges.


Sharing a session gives a collaboration platform where ideas can flow between users without restrictions.

Improved decision making at every stage

Once released we hope that TorchWeb will enable medicinal chemists to make the best, most informed decision at every stage of the discovery process. In part this is due to the core Cresset technologies but also because of the web based nature of the application. Central to the design of TorchWeb is the ability to extend the application through plugins or custom windows that can provide real time feedback on new designs at the moment of conception. This will allow you to bring all of the insights available from your existing QSAR and QSPR methods into the design process.

Alpha and beta testing coming soon

We are actively developing TorchWeb and are aiming for launch in 2019. As with our desktop applications, we are keen to gain feedback from our customers to ensure you receive a product that works for you. Whilst it is not ready for testing just yet, we will announce alpha and beta test programs in the coming months. In the meantime, if you would like to know more about this exciting new application then please do not hesitate to get in touch.

Hear about TorchWeb in more detail

See a demonstration and hear more about the core algorithms behind TorchWeb – register for The Cresset User Group Meeting on June 21 – 22, 2018.

A sneak peek into Flare V2: Python API and new science

Less than a year ago we released Flare for structure-based design. At the time, we promised you that a Python API would be central to future of this novel application. In keeping with that promise, Flare V2 will include a new Flare Python API, new science and significant improvements to functionality and usability.

Let’s have a quick look at the Flare Python API and what it can do for you.

What is the Flare Python API?

The Flare Python API enables Flare functionality to be accessed from Python and for you to customize the Flare interface. Python scripts can be run from the Flare graphical user interface (GUI) or by the command line pyFlare.

To access the power of Python from the Flare GUI we created a dedicated new tabbed menu named ‘Python’ (Figure 1).


Figure 1: The Python tabbed menu in Flare.
From the Python menu you can:

  • Run simple, one line scripts from the Python console
  • Write, load, run and save one or more scripts using the Python Interpreter
  • Manage the Python scripts using the Manager and Log buttons
  • Access the built-in Python documentation.

As you create your own scripts, you can choose to add them to Flare as new ‘buttons’ either in a new dedicated tab or in an existing tab.

How can Python scripts written with the Flare API make my project life easier?

Automate routine workflow

With the Flare Python API, you can automate routine workflows which you normally carry out for every project. For example, the ‘Prep_n_align’ script (Figure 2) starts from a Flare project where you have imported your protein structures, sequence aligns and superimposes all proteins to the first, then prepares the proteins and extracts the ligands, and finally adds a surface to the active site and focuses the view on the ligands. It also creates a button in a new ‘My Tab’ ribbon so that it takes just a click to launch the script whenever you need it.


Figure 2: A Python script to automate protein preparation, alignment and display.

Add functionality to your Flare project

Importing standard Python libraries will significantly enhance the capabilities of Flare. For example, using the matplotlib library, you can easily write a script which will create an x/y plot from data columns in your Flare project.

Make your workflows talk to Flare

Alongside the GUI interface to the Python API is a new command line binary ‘pyFlare’. This will give you access to all Flare methods directly from the command line enabling you to automate and script your workflows.

Sounds great, but what about the science?

It’s not like Cresset to forget about the science and Flare V2 makes no exception. Stay tuned for the release announcement next month to find out more about ensemble docking, electrostatic complementarity, new protein and ligand surfaces, and lots of improvements to functionality and usability.

Hands-on training

To learn more about Flare and the other Cresset applications, register for one of the hands-on workshops which will be held as part The Cresset User Group Meeting on June 21 – 22, 2018.

Flexible academic licensing

Are you a:

  • PhD student or postdoctoral researcher
  • Course tutor
  • Principal investigator
  • Department head?

Did you know that:

  • You can access Cresset ligand-based desktop applications through a variety of flexible licensing options
  • If you publish work which used Cresset technology, and you cite the applications used, we will promote your work through our website and newsletter?

Apply Cresset to your research

Academic customers tell us that our applications help them communicate ideas, give new insight, and are easy to learn and use. Below are just a few examples of how you can apply Cresset technology to your research.

Active design using electrostatics

Torch’s ligand-centric view enables 3D design whether or not you have a protein crystal structure. It makes it easy for you to focus on the designs that work and have good physicochemical properties.

Electrostatic and shape descriptors provide a rich informed view to help you understand the effects of chemical changes and eliminate designs that are unlikely to be active.


Figure 1: A change on one side of the molecule can often influence a distal region, especially if the systems are electronically linked through π-systems. This simple change has multiple effects due to the increased electron-withdrawing character of the new heteroatom and the addition of an aromatic ring: (A) the boundary of negative electrostatic potential extends further; (B) the shape and size of the negative π-cloud is significantly altered; (C) the size and extents of the positively charged aromatic edge are increased; and (D) there is a small increase in the positive potential associated with the aromatic hydrogens of the pyrimidine nucleus at the other end of the molecule.

Find and understand activity and selectivity cliffs in your SAR

Activity Miner, a component of Torch and Forge, helps you find and understand critical regions in complex SAR. Using the concepts of activity cliffs and matched molecular pairs, you can link activity changes to electrostatic and shape changes. Since selectivity is often as important as activity, Activity Miner makes it easy for you to compare multiple end points. The design process in Torch enables you to apply this knowledge to progress your project.

Activity Miner top pairs
Figure 2: Pinpoint the most significant changes to your molecules using the sortable top pairs table. Find critical points in the SAR and understand how they relate to changes in physicochemical properties.

Powerful models to interpret your data

Forge uses the Cresset patented ligand alignment algorithm to generate realistic, interpretable relationships between your molecules. It includes an impressive range of SAR models that combine robust analysis with customizable parameters, ease of use and intuitive visualization. For SAR analysis, there is no need to look any further than Forge.


Figure 3: QSAR models in Forge decipher complex SAR and inform the design of new molecules.

New SAR insights form novel methods


Figure 4: Activity Atlas is a novel, qualitative method that generates three distinct maps of the electrostatic, shape and hydrophobic properties around your molecules. It can be used with small or large data sets and is particularly useful for projects where traditional 3D-QSAR approaches fail.

Find biologically equivalent alternatives to escape IP and toxicity traps

Customers tell us that Spark is the best scaffold hopping and bioisostere replacement tool they have ever used. The easy to use interface quickly generates a range of novel molecules from an initial structure. Profiling and scoring help you choose the most innovative and tractable leads with the properties you need.


Figure 5: Spark workflow.

Request an academic license

Testimonials

Molecular visualization makes it easier to communicate ideas

“Working with Cresset tools has helped me generate new ideas for my projects in various disease areas. The molecular visualization has made it easier to communicate my ideas to my experimental collaborators, both chemists and biologists.”

New insight

“Cresset’s software gives new insight to projects I’ve been working on for the past three to four years. I specifically use it to see how proteins, DNA and molecules interact and bind to each other. If we can design drug molecules that will bind to DNA the same way proteins do, we can open up whole new lines of therapy. Cresset is supportive of academic research and they’ve been wonderful to work with to get everything up and running.”

Easy to learn and use

“Our students who use the Cresset systems in their projects tend to gain an affinity with a number of med-chem concepts far earlier than those who do strictly organic projects, for example using Spark to identify new frameworks that possess favorable properties and which can then be synthesized in the lab via a novel reaction. Compounds can be tracked within a TorchLite template and the student can invoke field patterns and orbital coefficients to explain changes to NMR spectra.”

 “My students and I are very grateful for providing us with the educational license and thus an opportunity to explore Cresset’s software in our lectures. Cresset provides marvelous software, easy to learn, easy to use, pleasant looking and its structure is logical and educational. The program manuals are great.”

 “Considering the absence of an experimental structure, the field based concept was a perfect match for our work. It helped us obtain bioactive conformations of ligands based on a validated pharmacophore. The software provided by Cresset is user easy and user friendly, creative and flexible for a new starter to an experienced researcher to use.”

“Visualizing the inhibitor/substrate binding site of protein crystal structure in Cresset using field points calculated by XED force field is very informative. The protocols for all the modules in Cresset are very quick and easy to use. Forge and Spark are excellent programs for LBDD. The radial plots obtained from alignment methods implemented in Forge provide a visual inspection of results and could be effectively used for simultaneously comparing any number of physical properties for the compounds in the dataset. I strongly believe that Cresset software is an important inclusion in the spectrum of software programs used for Computer Aided Drug Discovery paradigm.”

 

Flare: Accessible structure-based design

Modern structure-based design encompasses hundreds of methods, advanced algorithms and diverse biological targets. Cresset has a long-standing reputation for easy to use applications in the ligand-based design sphere. In deciding to bring Flare™, a new structure-based design application, to the market we created a challenge – can structure-based design be made simple?

A balance of usability and flexibility

Usability and flexibility are such fundamental features of well-designed software that they are often only noticed when they are absent. Usability reduces frustration, reduces training overhead, and makes it easier to access the full potential of the software. However, users also want the flexibility to tweak experiments, perform complex workflows and customise their applications to work the way they do. These key ‘unspoken’ features have been part of the design process in building Flare from the very start.

Focus on design

Critical to any analysis is the use of your conclusions to change the future. In structure-based design this means using information on how ligands bind to proteins to influence the design of the next ligand. In Flare we have put ligands at the heart of the application. They are stored in their own table and have a dedicated tab menu. The table layout enables physico-chemical property data to be stored alongside each ligand or calculated for every new design. It enables you to organize your compounds (sorting ligands based on their properties) or split ligands into different groups (roles) so that you can break down larger datasets into manageable chunks.

Designing new ligands is easy using the simple ‘Edit a copy’ feature. This brings up the molecular editor where the ligand can be improved in the protein active site and in reference to other ligands. The combination makes it easy to design in 3D, gaining all the productivity benefits that this brings. Sequential edits give an iterative process where each new design can be analysed and used as the basis for the next design.

Accessible methods

Analysis of existing or newly designed ligands requires complex methods. From docking to a detailed energetic prediction of binding, structure-based design methods are all complex. The challenge here is to present complex methods in an accessible way that enables expert users to modify key parameters but is not daunting to the regular user. In Flare a standardised layout is used for all calculation dialogues and provide default settings that work well in most cases. Experts have the choice to take the defaults or progress to the Advanced Options to change the parameters to meet their needs.

Great pictures

Creating great pictures is central to structure-based design. It seems like no J. Med. Chem. Article is complete without at least one protein-ligand picture. Creating pictures in Flare is easy with control over every aspect of the 3D display straight from the Home tab menu. Add to these the ability to control the clipping planes of surfaces independently of atoms and bonds and control over the picture resolution and you have all the elements you need to make stunning pictures. Whether for internal presentations, print articles or large posters, Flare delivers the quality of picture that you need.

Free evaluation of Flare

The feedback that we have received on the usability of Flare has been very positive. In the next release, you will see even more usability features such as storyboards, improved ligand selection and enhancements to the drag and drop features. We want Flare to be the best structure-based design application you use, so share your experience with us.

Request a free evaluation of Flare to see for yourself just how accessible structure-based design is for computational, medicinal and synthetic chemists.

New release of Spark databases

A new release of the Spark™ fragment and reagent databases is now available for download, to accompany the release of Spark 10.5. These are designed to provide you with an excellent source of new biososteres, whilst also ensuring that the results of your Spark experiment are tethered to molecules which are readily synthetically accessible.

Fragment Databases

In this release we have made significant additions to the source of our fragment databases. The new Spark ‘Commercial’ databases (replacing the previous ZINC fragments) use the combination of ZINC15 and the eMolecules Screening Compounds and include significant new chemical diversity.

The Spark ‘ChEMBL’ databases have also been updated and are based on release 23 of ChEMBL.

In all cases, the compounds in the entire source collection were filtered to remove potentially toxic or reactive fragments. They were then fragmented, and the frequency with which any fragment appeared in the original source database annotated. The fragments were then sorted by frequency and labelled according to the number of bonds that were broken to obtain the fragment, as shown in the table below.

Spark Category Database Total number of fragments (to nearest 1000) Frequency
Commercial VeryCommon 64,000 Fragments which appear in more than 650 molecules
Common 137,000 Fragments which appear in 140-649 molecules
LessCommon 256,000 Fragments which appear in 35-139 molecules
Rare 401,000 Fragments which appear in 12-34 molecules
VeryRare 675,000 Fragments which appear in 5-11 molecules
ExtremelyRare 749,000 Fragments which appear in 3-4 molecules
ChEMBL Common 306,000 Fragments which appear in more than 6 molecules
Rare 506,000 Fragments which appear in 2-6 molecules
Very rare 570,000 Fragments which appear in a single molecule

 

Overall, the new Spark databases include over 3 Million fragments which can be used to identify novel bioisosteres for your project. Figure 1 plots the number of fragments in each database per connection point count.


Figure 1: Count of fragments in Spark ‘Commercial’ (from ZINC15 and eMolecules’ Screening Compounds) and ‘ChEMBL’ (from ChEMBL23) databases split by the number of connection points of each fragment.An analysis of the numbers of fragments in common between the ‘Commercial’ and ‘ChEMBL’ Spark databases (expressed as percent overlap with respect to ChEMBL) reveals that the databases overall show an excellent level of complementarity.

% overlap with ChEMBL Very Common Common Less Common Rare Very Rare Extremely Rare
ChEMBL common 16% 18% 15% 10% 8% 4%
ChEMBL rare 1% 5% 9% 10% 10% 7%
ChEMBL very rare 0% 2% 4% 6% 7% 5%

Not surprisingly, the most common fragments for each database significant overlap. However, the majority of ‘rare’ fragments appear to be unique to each database, showing that the original ZINC plus eMolecules’ Screening Compounds and the ChEMBL collections occupy quite distinct parts of chemical space.

Reagent databases

Monthly updates of the Spark reagent databases, derived from the eMolecules building blocks using an enhanced set of rules for chemical transformation, will continue also in this release. The February edition includes over 500,000 reagents with up-to-date availability information, to make it easy for you to move from the results of a Spark experiment to ordering the reagents you require to turn these results into reality.

The number of fragments in each reagent database is plotted in Figure 2.


Figure 2: Number of fragments in the Spark eMolecules reagent databases.
Each fragment in the eMolecules database is linked back to both the eMolecules ID for the source reagent and its availability. The advanced filtering capabilities in Spark (Figure 3) make it very easy to choose the optimal set of reagents for your experiment based on the Spark similarity score, preferred chemistry (as encoded by the reagent database which generated the result), availability information and overall physico-chemical profile of the results molecules.


Figure 3: Spark reagent results include availability information from eMolecules.
The eMolecules IDs for the favorite reagents can be easily exported from Spark and used to purchase the compounds from the eMolecules building blocks database, as shown in the web clip How to use the eMolecules reagents databases in Spark.

Create your own database

Spark fragment and reagent databases provide an excellent source of new bioisosteres. However, if you have access to significant proprietary chemistry, to specialized reagents, or simply want to only consider fragments from reagents that you have in stock then the creation of custom databases will add value to your Spark experiments.

The Spark Database Generator is a user-friendly interface within Spark that lets you easily create custom databases.


Figure 4: The Spark Database Generator.

Conclusion

This release of the fragment databases significantly increases the chemical diversity available to Spark users, while the monthly updates of the reagent databases ensure that the results of your Spark experiment are tethered to molecules which are readily synthetically accessible.

We are confident that these new and updated Spark fragment and reagent databases, combined with databases from your corporate collections generated with the Spark Database Generator, will provide an even better range bioisosteres for your project.

Please contact us to update to the latest databases, if you wish to access the Spark Database Generator, or to find out how Spark can impact your project.

Using Spark to design macrocycle BRD4 inhibitors

Abstract

Macrocyclization of pharmaceutical compounds plays an increasing role in drug discovery. Macrocycles can provide several advantages such as favorable drug-like properties, and increased selectivity and binding affinity. Here we present a case study of designing macrocyclization strategies for reported BRD4 inhibitors with Spark1, Cresset’s bioisostere replacement and scaffold hopping tool.

Introduction

Macrocyclization of pharmaceutical compounds can provide several advantages, such as diverse functionality, favorable drug-like properties, and increased selectivity.2 Also improved binding can result from macrocyclization by locking the molecule in a low-energy binding conformation and reducing entropic cost upon binding. 2–4 One challenge in harnessing the advantages of macrocyclic compounds is the difficulty in synthesizing such molecules. Prediction of effective macrocyclization strategies prior to synthesis is, therefore, crucial for successful macrocycle drug discovery.

Recently, Wang et al. reported a new class of pyridone-based BRD4 bromodomain inhibitors that show promising anticancer effects in cell lines and xenograft models.4 Starting from a pyridone fragment hit, the compound was further optimized using structure-based design to achieve one-digit nanomolar potency. Macrocyclization further improved binding affinity, cellular efficacy, and pharmacokinetic compound properties (Figure 1). 4

In this case study, we used Spark, Cresset’s bioisostere replacement and scaffold hopping tool, to design macrocyclization strategies for non-macrocyclic, pyridone BRD4 inhibitors and evaluate results against experimental data reported by Wang et al.

Figure 1: Development of pyridine-based BRD4 bromodomain inhibitors reported by Wang et al. Macrocyclization of optimized fragment [2] improved binding affinity (Ki values from TR-FRET binding assay) about 4x. In addition to improved binding, compound [3] shows an increased cellular efficacy and improved pharmacokinetic properties.

Method

Spark replaces a specified moiety in a given starter molecule with fragments that exhibit similar electrostatic and steric properties.5 Spark’s molecular comparisons are based on molecular fields, computed by the XED force field.6–8 A major benefit of Spark is that it retains a larger amount of 3D information in the search for replacement fragments: instead of scoring only the respective replacement fragment in isolation, the whole final molecule is scored, considering any electronic and conformational effects the new scaffold may have on the rest of the molecule. Replacement fragments are selected from Spark’s fragment databases which are derived from commercially available and literature compound collections. After identifying fragments that contain the required number of attachment points and possess a shape / geometry compatible to the starting molecule, the molecules are minimized using the XED force field to remove bad clashes and unfavorable geometries. A final molecular field calculation and similarity optimization is then performed to yield a final score.

The macrocyclization wizard was used to design BRD4 macrocycle inhibitors. The wizard module provides a quick and easy-to-use GUI workflow with dedicated Spark settings tailored for macrocycle design (Figure 2). For this experiment, we used the X-ray structure of BRD4 in complex with the non-macrocyclic compound [2] (pdb 5UEY) as receptor, and compound [2] as the starter molecule (Figure 2). The bioactive conformation of [2] shows that the ethoxyl group of the pyridone scaffold is in close proximity to the 2,4-difluorophenoxy ring (Figure 3). Additionally, the ligand is partially solvent exposed in this region of the binding site, offering sufficient space to accommodate additional linker atoms. Therefore, we chose to link point 1 (OEt moiety) with point 2 (hydrogen atom) at position 6 of the 2,4 difluorophenoxy ring (Figure 3).

Figure 2: Selection of attachment points in the macrocycle wizard. Attachment point 1 is the ethoxy group and attachment point 2 is hydrogen 6 on 2,4 difluorophenoxy ring. The 3D window (right side) visualizes attachment points and confirms correct selection.

Figure 3: Inhibitor [2] in complex with BRD4 (pdb 5UEY). The close proximity between the ethoxy group and the 2,4 difluorophenoxy ring and the partially solvent exposed volume in this area suggests that linking these features may be feasible.

To bias linker design towards existing chemistry, oxygen was specified as attachment atom for attachment point 1. The second attachment point was left unconstrained. Additionally, the receptor structure was used as an excluded volume to guide linker design and the field points of carbonyl and sulfonyl groups forming hydrogen bonds with the receptor were constrained. The experiment was run with the dedicated ‘Ligand Joining / Macrocyclization’ settings against the fragment databases CHEMBL_common, Common, and VeryCommon, containing altogether about 120K fragments. Results were evaluated in Spark and molecular overlays were rendered in Flare9.

Results and discussion

The macrocycle wizard experiment generated 500 rank-ordered macrocycles derived from fragments merged with the starter molecule. The X-ray structure of the reported and structurally validated macrocycle [3] (Figure 4) shows that upon cyclization the molecule retains its key interaction with the BRD4 pocket, however, compound [3] adapts a slightly different conformation than [2], especially for the 2,4-difluorophenoxy ring. Despite this conformational change, the top scoring Spark results contained several macrocycles closely related to [3] (Figure 5), such as compounds with a butanol (rank 11), propan-1,3-diol (rank 9), 4-aminobutan-1-ol (rank2), butan-2-ol (rank 4), or a 3-buten-1-ol linker (rank 17). Compound [3] was found at rank 59 (Figure 5).

Among the top Spark results, linkers with 4 to 6 atoms were particularly enriched (Table 1). This finding is in good agreement with the experimental data reported by Wang et al., who found that short macrocycle linkers with only 3 atoms decrease the binding affinity by about 50x compared to a 5 atom linker.

Figure 4: X-ray structures of BRD4 in complex with [2] (3UEY) and the experimentally validated macrocycle [3] (3UEX).

Figure 5: Spark macrocyclization results. Among the top scoring results, Spark designed several linkers that are either identical or very similar to compound [3]. ECFP4 values are Tanimoto similarities to [3]. Scores were calculated by Spark and used for ranking of results. Attachment points A1 and A2 as specified in the method section.

Four atom linkers were only slightly less potent (factor 3) than the 5 atom linker, whereas 6 atom linkers showed a comparable binding affinity.4

The top ranked Spark results for each linker size are shown in Figure 6. Interestingly, for the shorter linker sizes (e.g., butan-2-ol with 4 linker atoms) Spark predicted fragments that mimic the hydrophobic volume of the ethoxy side chain of [2]. Omission of parts of this hydrophobic volume may be contributing factors in the observed potency loss for 3 atom linker macrocycles. Adding a Me group to the linker to address this feature of [2] (e.g., compound at rank 6 in Figure 6) may, therefore, result in short macrocycles with improved potency against BRD4.

Table 1: Distribution of linker sizes in top10, top25, and top50 results from Spark (500 total).

Top N results 3 linker atoms 4 linker atoms 5 linker atoms 6 linker atoms 7 linker atoms
10 2 6  1  1 0
25  2 16 4 8 0
50  2 32 12 9 0

 

Figure 6: Spark macrocyclization results. Among the top 10 results Spark designed compounds with linker sizes between 3 to 6 atoms. The top ranking result for each linker size is shown. Attachment points A1 and A2 as specified in the method section.

Conclusion

This case study demonstrates that Spark can successfully design macrocycles that are identical or very similar to reported BRD4 macrocycle inhibitors.4 The distribution of generated linker sizes was in good agreement with experimental SAR data.4

The Spark macrocyclization wizard is a quick and easy-to-use workflow that generates meaningful and diverse design ideas that can guide macrocycle drug discovery.

References and Links

  1. http://www.cresset-group.com/spark.
  2. Wagner, V.; Rarey, M; Christ, C.D. Computational Macrocyclization: From de Novo Macrocycle Generation to Binding Affinity Estimation. ChemMedChem 2017, 12 (22), 1866–1872.
  3. Yu, X.; Sun, D. Macrocyclic Drugs and Synthetic Methodologies toward Macrocycles. Molecules 2013.
  4. Wang, L.; McDaniel, K. F.; Kati, W. M. Fragment-Based, Structure-Enabled Discovery of Novel Pyridones and Pyridone Macrocycles as Potent Bromodomain and Extra-Terminal Domain (BET) Family Bromodomain Inhibitors. Med. Chem. 2017, 60 (9), 3828–3850.
  5. Tosco, P.; Mackey, M. Lessons and Successes in the Use of Molecular Fields. In Comprehensive Medicinal Chemistry III; Elsevier, 2017; pp 253–296.
  6. Vinter, J. G. Extended Electron Distributions Applied to the Molecular Mechanics of Some Intermolecular Interactions. J Comput Aided Mol Des 1994, 8 (6), 653–668.
  7. Cheeseright, T.; Mackey, M.; Rose, S.; Vinter, A. Molecular Field Extrema as Descriptors of Biological Activity:  Definition and Validation. Chem. Inf. Model. 2006, 46 (2), 665–676.
  8. Vinter, J. G. Extended Electron Distributions Applied to the Molecular Mechanics of Some Intermolecular Interactions. II. Organic Complexes. J Comput Aided Mol Des 1996, 10 (5), 417–426.
  9. http://www.cresset-group.com/flare.

Spark V10.5 release offers improved usability and flexibility, and new science

I am delighted to announce the release of a new version of Spark™, our scaffold hopping and bioisostere replacement tool. The focus of V10.5 is on advanced workflows and improved database management but also includes new science and many new and improved features.

The most interesting new features are presented below, and I encourage you to try this new release for yourself to see them in action.

Highlights

  • New wizards to support ligand growing and linking, macrocyclization and water replacement experiments
  • Enhanced Spark database update functionality
  • New pharmacophore constraints
  • Enhancements in search algorithm and advanced options.

New Spark wizards

The new Spark wizards will help you set-up advanced bioisostere replacements experiments in a user friendly and scientifically robust manner.


Figure 1. The new Spark project wizard.
The ‘Ligand Growing Experiment’ wizard (Figure 2) can be used to grow a starter molecule into new space, guided by existing ligands mapping a different region of the same active site. This was possible in previous versions of Spark (see case study Using Spark Reagent Databases to Find the Next Move) but the new wizard makes the workflow easier and more accessible.

The ‘Water Replacement’ wizard (Figure 2) can be used to search for a group which will displace a crystallographic water molecule near your ligand. Again the new wizard significantly improves the workflow for this popular Spark experiment that we have detailed previously (see case study Displacing crystallographic water molecules with Spark).


Figure 2. Results of ligand growing and water replacement Spark experiments.
The ‘Join Two Ligands’ (to  find a linker that joins two ligands sitting in the same active site) and the ‘Macrocyclization’ (to cyclize a molecule by joining two atoms with a linker) wizards are new workflows, which we have been testing internally in the last few years. Full case studies for these workflows are in preparation, but you can see an example of result you can get in Figure 3.


Figure 3. Results of joining two ligands and macrocyclization Spark experiments.
In developing the wizards, a number of additional features to support these advanced experiments have been fine-tuned. These include:

  • New ‘Ligand Growing’ and ‘Ligand Joining / Macrocyclization’ calculation methods to support ligand growing, ligand joining, macrocyclization and water replacement experiments
  • A starter molecule sitting within a protein’s active site can now be downloaded directly from the RCSB
  • Hydrogen atoms can now be replaced in the starter molecule
  • New ‘merge’ functionality to merge molecules in the wizards (when appropriate) and in the Manage/Edit References dialog
  • The starter molecule region selector now includes both 2D and 3D display.

These advanced bioisostere replacement experiments work well because of Spark’s product-centric approach. In Spark, result molecules are compared to the starter molecule, and a similarity score is calculated, only when the new molecules have been formed, minimized, and their fields and field points re-created. This approach, combined with the power of the Cresset XED force field, enables Spark to work with a higher level of precision, by avoiding fragment scoring limitations, allowing for neighboring group effects and for the electronic influence of replacing a moiety on the rest of the molecule and vice versa.

Enhanced Spark database update functionality

Spark V10.5 offers considerable improvements to the Spark database update functionality, to make this process more user friendly and efficient.

The Spark search dialog now alerts you when an updated version of the databases is available, before you start a search. Databases which need updating are marked by a green icon (Figure 4): and if the databases are selected for the Spark search, an ‘Update’ link appears at the bottom of the window, taking you directly to the Spark Database Updater. You will find this particularly beneficial if you use the eMolecules reagent databases where our monthly release schedule provides you with the latest availability information.


Figure 4. The Spark search dialog now alerts users when an updated version of the databases is available. If the databases which need updating are selected for the search, a link appears at the bottom of the window which takes you directly to the Spark Database Updater.
The Spark Database Updater has also been improved. The databases are now categorized as ‘Cresset’ and ‘Cresset reagents’, to make it easy to locate those you wish to update (Figure 5). Furthermore, you can now download or update all the displayed databases in one go by clicking the ‘Install or Update Displayed Databases’ button at the bottom of the window.


Figure 5. The Spark Database Updater now shows different categories of Spark databases and includes a button to install or update all the displayed databases in one go.
Finally, a new ‘sparkdbupdate’ binary is available to enable you to update all or selected Spark databases from the command line.

What’s new in Spark searches

A significant number of improvements and new features to the Spark searches have been made in this release.

Field and pharmacophore constraints

Field and pharmacophore constraints can be used to bias the Spark search towards results which fit the known SAR or your expectations, by introducing a penalty which down-scores results that do not satisfy the constraint.

Field constraints enable you to specify that a particular type of field must be present in the Spark result. 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 of the compound in Figure 6 – right.

Pharmacophore constraints, new in Spark V10.5, ensure that the desired pharmacophore features are matched by an atom of a similar type in the Spark results. In Figure 6, a pharmacophore constraint was introduced to ensure that all Spark results contain a H-bond acceptor.

While field and pharmacophoric constraints are a powerful way of fine tuning Spark results, we recommend that they are using sparingly, as they will be introducing a bias in your experiment. For example, introducing a pharmacophore constraint on the indazole NH of the PDB 4Z3V ligand in Figure 6 – left would not have matched the aromatic hydrogens of the active ligand in Figure 6 – right.


Figure 6. Left: Ligand from PDB: 4Z3V with pharmacophore and field constraints. Right: Active BTK ligand which satisfies both constraints.
Read more about field and pharmacophore constraints in the Forge V10.5 and Blaze V10.3 release announcements.

Enhancements to the Spark search algorithm

Spark V10.5 also includes enhancements to the search algorithm and associated advanced options, for example:

  • 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 ‘Flexibility’ filter which can be applied (together with SlogP and TPSA filters) to the whole molecule when performing a Spark search, to limit the results to the desired physico-chemical space.

Other new features and improvements

This Spark V10.5 release also includes a variety of additional new functionalities and improvements to the interface (Figure 7). These include:

  • New ‘Send to Flare™’ functionality to send either all results, favorite results or selected results to Flare, including as appropriate the starter and reference molecule(s) and the protein
  • New Storyboard window, to capture scenes recording all details from the 3D window that can be easily recalled when needed, including capability to annotate and rename scenes
  • New stereo view functionality
  • New support for touch screen displays and HiDPI
  • New Flexibility column in Spark Results tables
  • Improved performance of Spark database generation
  • Improved ‘View Parent Structure for Selected Result’ functionality now including both substructure and identity search
  • Improved ‘Grid’ button functionality
  • Improved display of protein ribbons, offering a choice of different ribbon styles and 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.


Figure 7. The Spark V10.5 GUI.

Try Spark V10.5

This release represents a significant improvement in the usability and flexibility of the leading bioisostere application. I encourage you to upgrade your version of Spark at your earliest convenience, and to download the keyboard shortcut guides for Spark V10.5 and Spark V10.5 Molecule Editor.

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

Contact us if you have queries relating to this release.