Computational approaches to ion channel drug discovery

In October, I presented Computational approaches to ion channel drug discovery at the ‘USA Integrated Drug Discovery Spotlight Event: Overcoming Challenges in Ion Channel Drug Discovery and Safety’ hosted by Concept Life Sciences and Metrion Biosciences.


Structure-based molecular modeling approaches are probably the most acceptable form of computationally driven rational design deemed useful by the medicinal chemistry community for drug discovery. For unliganded projects, invariably, medicinal chemistry is a painstakingly slow process.

Ion channels are inherently difficult protein targets to tackle as very few have been crystalized. Strangely, it seems that the greater the knowledge we accumulate – the deeper the complexity.

Alternative approaches e.g., chemogenomics and good ligand centric approaches, are vital to squeeze out all valuable insights from this ‘costly’ SAR data.

Cresset has a reputation for producing powerful software for ligand-centric molecular modeling. Analyses involving molecular 3D shape and accurate electrostatics, via Cresset’s proprietary multipole force field (XED), allow fast and informative interrogation of SAR in the absence of structure data.

I describe the current diversity of the ion channel target space and highlight some of the issues faced. Flare expands Cresset’s product portfolio into structure-based molecular design, bringing our electrostatic workflows into the protein realm.  Electrostatics are fundamental to ion channel function. These techniques are highly likely to prove very useful in the ion channel drug discovery arena, as more appropriate techniques for providing structure data become available e.g., CryoEM.

I make the case for information based strategies and ligand centric techniques, particularly computational workflows used by Cresset Discovery Services for customer projects, which pit the efficiency of rationality against blind trial and error.

Flare enhances the Cresset Discovery Services toolbox

One of the advantages of working for Cresset Discovery Service is that I have access to software that is under development. I’ve therefore been working successfully with Flare, our newly-released structure-based modeling application, on client discovery projects for some time and I’m delighted that it is now available to a wider market.

Processing structures to extract discovery information

So, what does Flare offer Cresset Discovery Services that it didn’t have before? Forge is the comprehensive workbench for ligand-centric workflows and I use it as the main workhouse for ligand work. However, more and more drug discovery projects are now structure enabled.

In fact, the increasingly routine use of X-rays and Cryo-EM means that projects generally have access to lots of structures, not just one. Processing these in order to extract useful information is often a key step for us on client projects. For this structure enabled work, Flare has become my Forge equivalent – my main workbench for structure-based work.

Using Flare

The Flare GUI is intuitive and easy to navigate, yet already has significant, powerful and unique functionality. From a practical point of view, Flare is stable on loading 20 or more X-ray structures, and doesn’t slow down (unlike some other tools) or glitch. These structures can be automatically aligned by sequence (Cobalt) and superimposed at the click of button.

Protein preparation is automated. Water analysis via 3D-RISM allows exploration of key water molecules around ligands and binding pockets. Of course, Cresset’s own electrostatic fields can now be visualized on selected protein surfaces, in addition to ligands.

Lead Finder is a Flare plug-in that gives the capability to dock ligands and ultimately conduct high throughput structure-based virtual screening as a complementary addition to Cresset’s state of the art ligand centric-virtual screening service using Blaze.

Zika virus protease PDB : 5H6V and a covalent inhibitor. Protein positive fields (left), negative fields (middle) and ligand fields (right).

Flare is focused on ligand design

The addition of Flare to the Cresset Discovery Services toolbox enhances our client offering with a new degree of confidence for structure-based work. Flare has been developed based on input from scientists in industry and academia so that it is relevant to real discovery workflows. My own work on client projects also contributed to the design of Flare, and I know from personal experience that it is ideally suited to the needs of computational chemists.

The upshot of this is that Flare has been created with ligand design as a central task. This means that even the most computationally intensive tasks are tailored to the effect that they have on the molecules that you make. This really is the great power of computational chemistry; informing and enhancing the discovery of the molecules that matter to your project, using the best computational methods at our disposal. Flare greatly eases that task and is a great addition to our toolbox.

A single force field across ligands and proteins

However, what really puts Flare apart from other structure-based methods is the seamless use of the XED forcefield between ligands and proteins. This means that I can make calculations on structure-ligand interactions based on a continuous force field, and make meaningful comparisons at all stages of work.

Ever since I joined Cresset in 2012, customers have been asking me, ‘Why not apply the XED force field to proteins?’ I’m delighted that this question has finally been answered with the release of Flare.

Contact us for a free confidential discussion about how cutting edge ligand and structure-based methods can transform your discovery.

In silico methods to streamline optimization

It’s a long journey from hit to lead, and the path is called optimization.

Your discovery program started with some promising leads from a high throughput screen. The biology has been done and the chemistry has shown that there is an effect on the biochemical assays. You have a series of hit molecules, a set of required parameters, thousands of data points, a team of chemists, a budget and a deadline. Now the optimization work begins.

In silico methods streamline the optimization process by giving you more understanding of your target and your hits, and by making it easier to manage your data. There are methods to:

  • Identify any gaps in your data so that you can decide what further work needs to be done. The more you know, the greater choice of intelligent steps you have.
  • Make sure you have the best possible understanding of your molecule-target interaction, giving you as many optimization options as possible.
  • Balance diverse properties. Visualization and multi-parameter optimization tools can transform your ability to understand the impact of changes across different compounds.
  • Stay in the active window as you make minor changes. Building an activity pharmacophore helps you to understand how far you can take the changes.
  • Escape any liability from toxicity or pre-existing patents. Fragment replacement methods can be invaluable for moving to new areas of chemical space and adding new ideas and directions to your research.

Are there gaps in your data?

The optimization decisions you can make depend on the data you already have.

Activity Atlas, a component of Forge, summarizes the SAR for a series into a 3D model that can help you find any gaps in your data. You can calculate:

  • Activity Cliff Summary: What do the activity cliffs tell us about the SAR?
  • Average of actives: What do active molecules have in common?
  • Regions explored: Where have I been? For a new molecule, would making it increase our understanding? This analysis also calculates a novelty score for each molecule.

This approach is also helpful in looking at toxicity and other liabilities. For example, you may be optimizing a molecule that was identified from a screen. It is active, but has some undesirable chemistry. If you can understand as much as possible about the SAR, electrostatics and shape you are more likely to discover a better way to escape a liability. The more you know, the greater choice of intelligent steps you have.

Figure 1: Activity Atlas condenses your structure-activity data into highly visual 3D maps that inform the design and optimization of new compounds.

Understand the ligand-protein interaction

Understanding how your hits interact with the target helps you to optimize the affinity of your compound. In structure-enabled projects Flare, our new structure-based design application, can be used to analyze the protein-ligand system, calculate the energetics of ligand binding and analyze the water stability and energetics.

Without knowledge of the target structure, you may need to go back and deduce ligand-protein interactions from your hit so that you have a clearer understanding of the binding mechanism. This knowledge makes it far easier to optimize the affinity of your compound.

You get an extra level of insight to this process with Cresset techniques. Our electrostatic, hydrophobic and shape based analyses make it clear which chemical changes can have the largest biological impact.

Figure 2: Flare GUI.

Visualization tools help with multi-parameter optimization

Compounds that come out of the screening process generally have weaker potency than is required, so one of the first tasks is to increase the activity. This is usually done by making small changes to the molecule and testing their effect. However, there is a range of properties besides activity that need to be optimized.

Firstly, drug molecules need to be stable and small. Larger molecules are more likely to have off-target effects and have more problems travelling through membranes. They are also likely to have more complicated chemistry, making them harder and more expensive to synthesize. One of the key steps in optimization is to retain as many components as are required to make the compound active, but no more. The difficulty is that when you change one property you tend to change others.

Computational visualization for multi-parameter optimization shows you how the changes you are making affect other molecular properties. For example, you may want to simultaneously optimize the polar surface area and the LogP. The Torch and Forge radial plots can be set up to define acceptable project ranges for project data and in silico–calculable properties.


Contact Cresset Discovery Services for a confidential discussion about how we can streamline your optimization.

Molecular design towards Protein-Protein Interaction inhibitors

In December 2016 I attended the SCI Protein-Protein Interaction symposium. Armed with Cresset’s powerful ligand centric molecular modeling suite Forge, and an embryonic version of our new structure-based design application, Flare, I was keen to see what could usefully be done with PPI’s.

Prof. Richard Baylis (University of Leeds, UK) presented new data on the interaction of N-MYC with Aurora A. N-MYC is a disordered multi-domain protein with a host of interaction partners. Dysregulation of N-MYC has been linked to a range of cancers. N-MYC is short lived in-vivo and its usual fate is to be ubiquitinylated and degraded. Binding with Aurora A protects N-MYC from this process allowing its various tumorogenic affects to persist. The Baylis group provided the first x-ray evidence showing how N-MYC interacts at an allosteric site of Aurora A which stabilises an active conformation of the Kinase (figure 1).

Figure 1: Aurora A kinase with N-MYC – light green (left), and detail of the N-MYC short helical domain 74-89 (right).
Baylis suggested that DFG-out inhibitors of Aurora A provide distortions of the kinase that would prevent MYC binding, conversely, inhibition with ATP competitive inhibitors would not. Evidence of potential beneficial effects of the former type of kinase inhibitor, but not the latter, may be explained by this fact and led to the suggestion that this may be an effective therapeutic strategy for some types of cancer such as neuroblastoma.

An alternative computational strategy, which occurred to Cresset at the time, was to employ a structure-based approach; to furnish molecular designs that could directly prevent this protein-protein interaction. For this purpose, an initial analysis of the surface interaction, including both electrostatic and lipophilic hot-spots, would be vital.

During the talk, I used Flare to quickly download the relevant PDB file (5G1X) and to load the protein coordinates directly into the application. An automated protein prep protocol (build-model) was used to refine the pdb structure before generating the surface interaction maps, using Cressets proprietary XED force field (figure 2).

Figure 2: (A) Positive protein electrostatic isopotential surface of Aurora (left), negative protein electrostatic isopotential surface (center), and neutral isopotential surface with some key residues of N-MYC (right).
These isopotential maps show discrete positive (red), negative (blue) and neutral (yellow) surface regions that represent key interactions sites between N-MYC and Aurora A which allowed the assignment of the N-MYC residues on which to focus. The N-MYC protein was similarly used to generate and visualise the complimentary fields – as the other component half of the PPI (figure 3).

Figure 3: Negative protein electrostatic isopotential surface of N-MYC short helical region (left), and positive protein electrostatic isopotential surface of the same (right).
In keeping with other known PPI’s such as the MDM2 system, in the short helical domain (N-MYC 74-89) residues Met81 and Trp77 were identified as key lipophilic contacts. Much of the rest of the helix is largely for structural integrity and for stabilising solvent except for the NH of Trp77 and Glu84, which provide additional polar contacts, the latter capping an adjacent helix from Aurora. Further along the N-MYC peptide, towards the N-terminus, Pro74-Pro75 motif (figure 1) marks a change in sec. structure leading to another lipophilic contact Val61 and another polar contact Ser64 (not shown).

We can exploit this information to generate chemical starting points, once each important set of residues is identified and mapped. Thus, from the 3D shape and detailed electrostatic information we can conduct de-novo design experiments to furnish ideas for synthesis, or use virtual screening (Blaze) to search for commercial compounds to purchase and test.

Since the distance between the two main hot spot regions was not ideal (27 Ang. Val61 to Trp77) and although linking them might have been possible using a fragment linking or growing technique e.g., using Spark (Using Cresset’s Spark to grow and link distant fragment hits with sensible chemistry), we chose to tackle them independently with a de-novo design technique. I used the key residues Pro75, Trp77, Glu80, Met81 and Glu84 from the short helical domain as a molecular reference. We used this reference to score our molecular ideas against, and to optimize them via iterative ‘molecular design > alignment > scoring’ cycles in Forge. This powerful technique scores 3D shape, electrostatics and protein steric clashes whilst simultaneously calculating and/or filtering in-silico physiochemical properties. This method as described is limited only by the imagination of the user. In conjunction with Spark as the idea generator however, the limit is set only by the availability of appropriate fragments in the Spark databases – which is a substantial resource.

Later, when we returned, we also ran a virtual screening test on this system using the Blaze demo server. Results of this quick virtual screen against a sub-set of the ChEMBL database are shown below (figure 4).

Figure 4: Forge ‘tile view‘ of example diverse 2D output results of the virtual screen using the Blaze Demo server against a sub-set of ChEMBL (left) and 3D alignments of two of these (pink and green sticks) against the reference N-MYC peptide (blue lines) bound to Aurora A (Forge screenshot).
Although some of the Blaze examples retrieved were interesting, very good considering that this was a very small set <200k compound DB, it appears that good shape score and field score were not generally observed simultaneously. The ‘new’ addition of ‘pharmacophoric atom features in Blaze’ ensured we retrieved some of the key contacts such as the indole H-bond. However, we felt that design was probably the best way to address achieving the precise set of contacts we were looking to mimic.  Afterwards, I expanded on the ‘initial’ de-novo design ideas and provided around 20 further designs which had more reasonable properties and synthetic tractability (figure 5).

A powerful combination of cutting edge ligand and structure-based modeling

Figure 5: Flare screenshot of the structure of an initial idea (left) superimposed on N-MYC hot spot residues, plus its calculated properties, and (right) a space filling model of a further example with superior properties, improved fit, better synthetic tractability and … an IP position.Although this is only a thought experiment (until the point at which any of these molecular designs are synthesized and tested) this illustrates how the powerful combination of both ligand centric and structure-based techniques in Flare, Forge, and perhaps also Spark, could be used to generate specific ideas that address the types of challenges presented by PPI’s or fragment enabled drug discovery projects. This is not untypical, in terms of a portfolio of tasks we might suggest to Cresset Discovery Services clients.

Download an evaluation of Flare, Forge, Spark and Blaze, or contact us to find out how Cresset Discovery Services can enhance your project with insightful and creative delivery of powerful molecular modeling.

Help with writing your grant application

Don’t wait until you have funding to talk to Cresset Discovery Services about working together. We can help before you even start writing your grant application.

Our experience in helping write grant applications for academic and government funding shows that working together at the very start of a project reaps rewards. Being involved from the beginning gives all parties the flexibility to see exactly how and when we can make the maximum contribution to your research.

The University of Newcastle and Sygnature Discovery engaged our services from the earliest stages of an MRC funding application: this three-way collaboration involved writing the successful funding proposal together, ensuring the project used the strengths of each collaborator in the most efficient way.

When it comes to writing your application, we can give you as much help as you want. At a very minimum you will receive detailed scientific input, with descriptions of our methods, deliverables and estimates of time and cost.

Cost and benefit analysis of our methods compared to alternative approaches can also be provided. For example, virtual screening can be an incredibly cost effective way of finding a chemical starting point. We recently carried out ligand-protein docking for a customer as a virtual screen of available library compounds, leading to the selection and purchase of a small sub-set of the available compounds. Cresset Discovery Services helped the customer make an estimated five-fold saving over traditional HTS screening approaches without the pre-selection of likely hits.

Milestones, deliverables and reporting are part and parcel of grant funding. We will work with you on these, and of course we are happy to receive staged payments.

If you prefer, we can also contribute to the writing of the whole proposal. We can even work as a project manager to other collaborators. For example, we can manage your procurement process and work with CROs to outsource assays.

It can often help to have a third party, such as Cresset Discovery Services, bring expertise to your grant application. It will certainly reduce your workload!

Flexibility is key in how you engage our services. Contact us to find out how we can help with your next grant application.

Water stability is key to designing novel patentable chemistry

An analysis of the water stability and positions in a ligand-protein complex informed the design of novel ligands for a customer target. This work led to new active chemistry that the customer went on to patent.

A Cresset Discovery Services customer had identified a novel target with a natural ligand and were looking for new chemistry that would be active at the target site. Our scientists carried out an initial project to learn more about the protein-ligand system. The Cresset field approach, used to analyse the structure and interactions, gave the customer valuable insights into the active features of the ligand.

The customer used this information to develop analogous synthetic compounds and example molecules. They asked us to work with them again to computationally align the example molecules and prioritize them for synthesis.

We carried out an initial alignment and then modeled the system in detail. It appeared that part of the molecule that was important for the interaction was not making any contact with the protein.

The PDB had some crystal structures of related proteins, but not of the target of interest. We studied the available protein data to learn as much as possible about the binding pocket, paying particular attention to the positions and stability of the water molecules. This led to us putting forward the hypothesis that an important part of the ligand interaction included the stabilization of water.

Based on this hypothesis we prioritized the molecules that bridged the observed gap between the natural ligand and the target while also stabilizing the free waters.

Water analysis was carried out by manually superimposing multiple crystal structures, viewing the crystallographic waters that clustered together, and mapping on their temperature factors. This process allowed us to determine the importance of each water molecule in the solvation sphere around the ligand and protein pocket. With the advent of the new 3D-RISM method in Flare a similar computational work-flow can be accessed which is far more efficient for this type of analysis. This is a more systematic approach which enables us to calculate the position and stability of all water molecules around a proposed ligand in a binding pocket. Moreover, as it does this without the need for any crystallographic water data, this is far more useful as well as convenient. Ultimately, this data can be used to assess or compare ligands in terms of how well they might stabilize essential water.

Based on our equivalent ‘hands-on’ analysis, we worked with the customer to choose the best candidates for synthesis. These newly-designed ligands resulted in new active chemistry for the customer that was valuable enough for them to patent.

The position and energetics of water molecules in and around the active site is of crucial importance when designing novel ligands. Knowing which water molecules are energetically favorable can give valuable insights into the best positions for ligand molecules. 3D-RISM analysis is one of the methods available in Flare for structure-based drug design.

Move from hit to lead

A long-standing customer had a hit series with good activity but poor properties. Cresset Discovery Services worked closely with the customer to formulate a plan of action to optimize the compound properties while maintaining potency.

Cresset Discovery Services has worked closely with a customer on a target. We ran virtual screens, aligned literature chemotypes and proprietary chemotypes in order to arrive at a robust binding hypothesis and a field hypothesis. This enabled the customer to find a new chemotype that had good activity at the target. However, the hit series had poor properties. They needed to optimize the compounds, potentially sacrificing some potency while balancing this against improving the properties of the molecules.

The target in question required lipophilic molecules, so the set of compounds had reasonably high lipophilicity which can be a liability in drug development. The compounds also had high protein binding and, we suspected, high clearance.

Figure 1: Forge radial plot. The selected ‘highlighted’ set (Figure 3). Compounds with better properties give a larger area in the radial plot.

Figure 2: Forge graphical radial plot parameters. For each property a function is used to describe perfect, acceptable and unacceptable values. Perfect values are plotted at the edge of the radial plot and unacceptable plotted at the center. A single ‘Radial Plot Score’ is created to represent the fit of a compound to the chosen set as a function of parameters using the specified weighting scheme.

Figure 3: Forge property plots. Radial plot scores as a MPO method: Compounds with a radial plot score greater than the chosen cut-off (bottom left) were selected and hence are highlighted all the plots. These compounds have a good balance of activity and other physicochemical properties.

The best course of action for optimization was to search for compounds that were reasonably active but far more polar than the bulk of the molecules in the compound set. We designed suitable variants of these candidates and checked them using the alignment model in field space that we had developed earlier in the project. New combinations of functional groups on the core were selected to address the overall lipophilicity whilst maintaining the essential interaction features. This solution had the potential to address both the protein binding and clearance issues.

Over the course of the following weeks the customer worked through these ideas with a high degree of success. We collaborated closely with the customer throughout the project. They shared not only their activity data, but also their property data. We assessed both the activity and property landscape, and refined the suggested sets with the aid of multi-parameter optimization, which enabled us to suggest which compounds they should make and progress to help to get them past the hurdles they were experiencing. The customer has now arrived at a set of compounds with far better properties without a significant loss of potency.

The customer has retained our services to help them to optimize the potencies of the back-up series, which may help them to choose which chemotypes to progress next.

Contact us to see how we can help you with similar projects.

Conduct ligand-protein docking

A long-standing customer of Cresset Discovery Services asked us to identify new compounds that could be active at their protein target. We conducted ligand-protein docking to narrow down their 50k compound library to the best 1.5k compounds. The cost of the consulting project plus the chemistry for 1.5k compounds was about 20% of what it would have cost to buy and screen the entire 50k library.

Ligand-protein docking can be an excellent way to build up knowledge about the binding pocket. It can also form the basis for a virtual screen to identify new active compounds.

Cresset Discovery Services had been working with this customer on a particular ligand for some time, but there was very little information available about the protein target. There were homologues in the literature, but they were distantly related and nothing very similar had been crystallized.

Detailed preparatory work to model the protein active site

It was necessary to do a lot of modeling work to build up the relationship between the human target and the distantly related proteins available from the literature. We built sequence alignments and compared them, enabling us to build up 3D models of the target and its interaction with the ligand.

Some mutagenesis data was available on the known ligands, so we were able to use this to refine the 3D models and check that the correct residues were in the right places on the active site. This enabled us to define the active site for the ligands. We went on to calculate the energies for the protein-ligand interactions to make sure we had identified poses that made sense.

This was a complex system that required a great deal of protein preparation. This preparatory work was essential for successful docking and required expert knowledge, experience and skill.

Docking and virtual screening using different scenarios

At the end of this process we had a good model of the protein-ligand system. The next step was to remove the ligand and carry out docking.

Docking was first tested on the molecules that were known to bind to the target. This resulted in excellent retrieval rates, showing that the model would also be able to retrieve new compounds.

There were a number of different binding sites on the protein so we decided to carry out the virtual screening using different scenarios for the protein. We:

  • Kept the ligand intact in the binding site
  • Removed the ligand completely
  • Looked at partly bound situations and un-bound situations for each of the binding sites.

The customer provided us with a set of 50k ligands and we docked each of these against the binding pockets. A docking scoring system was used to rank the top 2k compounds from each of the screens.

Analyzing the results and compiling a purchasing list

The top 2k compounds from the four screens were analysed in detail. We visualized every one of the top 2k compounds and looked at each of the docking poses. The docking gave us good geometries for the ligands and we used Cresset software to check that the electrostatics made sense. Any compounds that were unlikely to bind well were rejected.

A final, ranked list was provided to the customer with a very high degree of confidence that it included compounds that were active at the protein target. They were able to procure about 75% of the compounds from the hit list, giving them a final set of 1.5k compounds to test.

An incredible saving in time and money

Carrying out virtual screening to focus the library in this way represented an incredible saving in time and money for our customer. The alternative approach would have been to buy and test the whole 50k compound set. Not only would the customer have needed to purchase all of the compounds, but also shipped them, stored them, plated them, screened them, and then they would still have to analyse the results.

The estimated cost of doing this for all 50k compounds would have been about five times the cost of the combined tasks of the Cresset Discovery Services project plus buying and testing 1.5k compounds.

Cost of

{buying and testing 50k compounds}

=  5 X

Cost of

{Cresset Discovery Services project + buying and testing 1.5k hit list}

Contact us to find out how we can add value to your project.






Dr Martin Slater, Director of Consulting Services

Homology modeling and ligand electrostatics plays key role in elucidating binding mode and molecular interaction of new class of antifungal drugs

Last month F2G published a paper in PNAS [1] describing F901318, the leading representative of a novel class of antifungal drug. Dr Martin Slater, Director of Cresset Discovery Services, is a co-author on the paper. He describes how modeling work carried out by Cresset Discovery Services was critical to predicting the binding mode of the inhibitor and important interacting amino acid residues. F901318 is currently in clinical development for the treatment of invasive aspergillosis.

There is an important medical need for new antifungal agents with novel mechanisms of action to treat the increasing number of patients with life-threatening systemic fungal disease and to overcome the growing problem of resistance to current therapies.

F2G are a UK-based antifungal drug discovery and development company who have identified F901318 as a leading representative of the orotomides, a novel class of antifungal drug. Their identification of dihydroorotate dehydrogenase (DHODH) as the mechanism by which F901318 inhibits and kills Aspergillus fumigatus has been a major breakthrough differentiating F901318 from other systemic antifungal agents.

From hit to lead with medicinal chemistry

F2G had a large amount of proprietary cellular activity data developed over time against their antifungal screening platform. After an initial hit finding campaign significant progress had been made using classical medicinal chemistry approaches.

F2G were keen to inform and assist the development process by gaining a molecular level understanding of the target protein ligand system. They approached Cresset Discovery Services for help in elucidating the molecular interaction of the target protein-ligand system.

A detailed molecular understanding with modeling

Cresset’s unique approach of defining the electrostatics around the active chemotype made it possible to identify the precise nature of the various sites on the active molecules. In conjunction with sequence analysis across the wider DHODH family, Cresset scientists were able to match these subtle ligand features to the patterns of residues that were likely to be key.

Subsequent homology and ligand protein interaction modeling of Aspergillus fumigatus DHODH using the XED force field identified a predicted binding mode of the inhibitor and important interacting amino acid residues.

We combined a detailed ligand centric approach using Forge with protein modeling using a prototype of the new Cresset protein tool to arrive at a binding hypothesis consistent with the selectivity profile. The modeling process is fully reported in the paper [1].

Testing in silico hypotheses in vitro

Having made a binding hypothesis, a number of lab experiments were initiated by F2G to check the predictions e.g., using site directed mutagenesis.

Most satisfyingly, the lab results supported our predictions.

F901318 is currently in late Phase 1 clinical trials, offering hope that the antifungal armamentarium can be expanded to include a class of agent with a mechanism of action distinct from currently marketed antifungals.

Cresset’s consulting work with F2G provided valuable insight into the predicted interaction pattern of the main chemical series with the Aspergillus DHODH target protein. As with many research projects, any level of understanding achieved is often a prelude to even deeper questions, and there are many remaining to be answered for this unique system. Cresset continues to work closely with F2G, providing software and services to support them in their ongoing projects.










Dr Martin Slater

Director, Cresset Discovery Services

Build and cluster diverse 3D libraries

Cresset Discovery Services (CDS) worked with BioBlocks to analyze their fragment library to maximize coverage of 3D chemical space. As part of the project, we developed an innovative clustering method that made it possible to assess the 3D similarity across their virtual database of over 1.5 million fragments.

The goal of the project was to help BioBlocks build the maximum 3D diversity into a fragment library of manageable size from a starting pool of over a million compounds. Existing techniques would have required an infeasible amount of computing power, so CDS developed an entirely novel rapid clustering method especially for the project. The solution was still extremely computationally challenging, but we were able to use our expertise in distributing calculations to the cloud to deliver the results that BioBlocks needed on time and within budget.

“Working with Cresset has been a positive experience from start to finish,” said Warren Wade, VP of Chemistry at BioBlocks. “Because our fragments are designed to be new chemical matter, they challenged the limits of existing structural descriptions. Cresset worked closely with us to overcome these limits and produce a high value compound set”.

The final result was a 3D fragment library that contains a significant number of compounds with novel core structures that are now viable candidates for fragment screening. BioBlocks envisions this Comprehensive Fragment Library to be a drug discovery tool available only to collaborators who will be able to leverage this new chemical space for their lead discovery programs. Hits from the library are entry points to BioBlocks’ collaborative medicinal chemistry processes, developed to increase the probability of generating commercially viable leads.

3D Similarity-based clustering workflow
3D similarity-based clustering workflow

Read more about this project: Large scale compound clustering in 3D.

Contact Cresset Discovery Services to find out more about how we can help you design large scale libraries for your project.