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


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.


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


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.


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.


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.


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

  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.

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.


  • 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.

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

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

Why the change?

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

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

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

Tversky similarity in field-based virtual screening

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

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

Similarity in Blaze

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

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

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


Substructure searches with Blaze

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

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

Superstructure searches with Blaze

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

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

Tanimoto similarity in place of Dice

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

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


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

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

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

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

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

Improved molecule alignment

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

Field and pharmacophore constraints

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

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

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

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

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

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

Improved alignment and scoring

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

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

New Conformation Explorer

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

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

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

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

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

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

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

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

Other new features and improvements

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

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

Upgrade to Forge V10.5

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

Evaluate Forge

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