Modeling the intricacies of molecular recognition: Make ‘smart antibodies’ into biologics

Antibodies are fantastically versatile molecular recognition engines, capable of creating artificial enzymes in response to recognized interactions. Similarly, enzymes depend on molecular recognition for catalysis. Cresset Discovery Services has a depth of experience in modeling molecular recognition scenarios, stretching back to the early nineties.

Nature’s molecular recognition engines

In 1992 I was about to start a PhD on ‘catalytic antibodies’, investigating the amazingly versatile ability of the immune system to prepare artificial enzymes. It all sounded fascinating, and still does. Alas, rather than building antibodies, the path of my PhD altered, resulting in me developing my organo-phosphorus chemistry skills and producing ‘transition-state inhibitors’ of beta lactamase enzymes instead.

However, I retained a keen interest in molecular recognition and a familiarity with antibodies as a protein class and this has proved useful for our work at Cresset Discovery Services today. Indeed, it wasn’t until much, much, later that immunoglobulins would reveal their impact as important future medicines and of course this was way before the multi-billion-dollar blockbuster TNFa targeting biologic Adalibumab (Humira) (Figure 1, right).

Immunoglobulins are biologically evolved to recognize an enormously variable patch of molecular surface using a single protein architecture; an arrangement of two polypeptide chains (heavy and light) each bearing three hyper variable loops.

Figure 1 illustrates the diversity of recognition possible in antibodies, as modeled in Flare™, ranging from small molecules (sulfathiazole) to big molecules (Fullerene C60)1 and peptides (TNFa)2.

Figure 1: Left: an example of a fullerene recognizing mouse antibody. Middle:another mouse antibody recognizing a sulphonamide drug, Sulfathiazole. Right: the human engineered antibody Adalibumab (green) with TNFa. Heavy chain (crimson) variable loops (magenta), Light chain (grey) variable loops (black). All modeled in Flare using PDB codes: 5CP3, 1EMT and 3WD5.

Similarly, enzymes also use molecular recognition to great advantage via a diverse array of protein architectures. In contrast to antibodies, they specifically recognize molecular surfaces that represent a transition state of a chemical transformation. Lowering the energy of the transition state through binding allows catalysis to happen in enzymes.Figure 1: Left: an example of a fullerene recognizing mouse antibody. Middle:another mouse antibody recognizing a sulphonamide drug, Sulfathiazole. Right: the human engineered antibody Adalibumab (green) with TNFa. Heavy chain (crimson) variable loops (magenta), Light chain (grey) variable loops (black). All modeled in Flare using PDB codes: 5CP3, 1EMT and 3WD5.

The main premise of catalytic antibodies is to elicit their production in response to a transition state that can be mimicked by a ligand structure, which in theory then reproduces enzymic behaviour in the resultant protein. This pursuit simultaneously probes our fundamental knowledge of molecular recognition and of enzyme catalysis.

A catalytic antibody that cleaves cocaine

In 2006 workers at Scripps published work on a catalytic antibody3 that cleaves cocaine at the benzoyl ester. The antibody was elicited using an aryl phosphonate ester – a mimic of the carboxyl ester hydrolysis transition state.

The insights gained through the X-ray structures that were solved for this system were remarkable. Multiple components of the reaction coordinate are shown to exploit common recognition patterns, whilst the different geometries are accommodated by both residue and backbone movements in a dynamic process (Arg and Tyr movements in Figure 2).

This is an excellent model system. It shows how some interactions are very favourable e.g., the cation-pi interaction of the tropane is very solid across the structures, whilst others e.g., the ester, are variable. It also demonstrates that proteins, and in particular enzymes, ‘breathe’ – they are not statues (Figure 2).

Figure 2: The Scripps catalytic antibody in the different protein conformations that recognize the substrate and the transition state for cocaine ester hydrolysis. This shows highly mobile Arg (magenta) and Tyr (cyan) residues and the ligand (cocaine hydrolysis reaction coordinate – light to dark green). All modeled in Flare using PDB codes: 2AJU, 2AJV, 2AJZ, 2AJX, 2AJY and 2AJS.

All molecular recognition is not equal

One very interesting facet of molecular recognition demonstrated by antibody antigen interactions is that these interactions are not all equal. In fact, there are lesser and greater interactions – the latter of which can predominate in driving antibody production. Preferred interactions (or ‘hot spots’) can be visualised using various techniques (e.g., as demonstrated using the previous cocaine system modeling in Flare shown in Figure 3).

Figure 3: The Scripps catalytic antibody conformer ligand interaction surfaces. All modeled in Flare using PDB codes: 2AJV and 2AJX. This shows highly mobile surface changes required to squash the substrate into the transition state geometry (this time with the phosphono mimetic) and the hydrophobic hot spot surface (yellow) calculated by Flare.

Antibodies are produced via natural selection processes as a cellular response to a presented antigen. Some parts of the antigen are better than others at eliciting antibody responses, since they may involve preferred interactions, and so the resultant antibody may not bind in a therapeutically useful way.

Modeling and design of these interactions (i.e., via antigen engineering) becomes a useful task, since leaving it to nature can divert us from our intended goals.

Cresset Discovery Services work on biologics

While we are actively working and delivering on projects involving biologics, client confidentiality means that it remains a challenge to describe details of work done for commercial clients. We can, however, speak more generally and we have recently delivered on projects to:

  • Predict / design mutants that prevent binding
  • Model strategic glycan positioning at unhelpful yet potently antigenic surfaces
  • Model / characterize the observed binding order of an antigen – receptor series.

Read more about Cresset’s capabilities in biologics: Modeling ‘big’: Applying the XED force field to biologics.

Biologics remain a very important class of protein targets for disease therapeutics which we intend to continue to support through our innovative modeling services.

Free confidential discussion

Find out how we can accelerate your project by requesting a free confidential discussion.


  1. X-ray crystal structure of an anti-Buckminsterfullerene antibody Fab fragment: Biomolecular recognition of C60, Braden*, B. C., Goldbaum F. A., Chen B., Kirschner A. N., Wilson S. R. and Erlanger B. F. PNAS 97, no. 22, 12193–12197, 2000.
  2. Comparison of the Inhibition Mechanisms of Adalimumab and Infliximab in Treating Tumor Necrosis Factor a-Associated Diseases from a Molecular View, Shi Hu, Shuaiyi Liang, Huaizu Guo, Dapeng Zhang, Hui Li, Xiaoze Wang, Weili Yang, Weizhu Qian, Sheng Hou, Hao Wang§, Yajun Guo and Zhiyong Lou, Journal of Biological Chemistry, 288, 38, pp. 27059 –27067, 2013
  3. Complete Reaction Cycle of a Cocaine Catalytic Antibody at Atomic Resolution, Zhu X., Dickerson T. J., Rogers C. J., Kaufmann G. F., Mee J. M., McKenzie K. M., Janda K. D. and Wilson I. A. Structure 14, 205–216, 2006.

Enhancements in Flare V2 allow our scientists to spend more time on your design and analysis project

The release of Flare V2 brings quicker analysis of new design ideas and more automation of workflow processes. Andy Smith explains the advantages that Flare V2 brings to Cresset Discovery Services customers, including more time spent on high-value design and analysis work.

Why wait for the full release of Cresset software?

Before a new version of the software is released, we have already applied the new functionality to our customers’ projects once it has been fully validated by our development team. This means that in additional to the existing available packages within our software Cresset Discovery Services customers get to the added benefit of the upcoming functionality before it is generally released. We always send files which are compatible with customer’s software: if pre-release functionality is used to illuminate our analysis, we save the data in a format so that customers can still review the analysis. Flare V2 opens up exciting new functionality to Cresset’s software customers that invariably has already been extensively tried out by the Cresset Discovery Services team.

Electrostatic Complementarity™ – intuitive SAR and great pictures

The Flare development which has us most excited and is likely to benefit our customers the most is the ability to calculate the Electrostatic Complementarity (EC) of protein-ligand complexes, and project the values onto surfaces for both the proteins and ligands.

EC is a new method developed inhouse by Cresset scientists that distils complex protein ligand interactions into an obvious and informative pair of surfaces which are accessible and understandable to a broad-spectrum audience.

I recently showed the EC feature in Flare V2 to a group of senior medicinal chemists. They were very impressed with the intuitive representation. They commented that the visual surfaces were consistent with their understanding of the SAR of the system, and also added, ‘This will make a great series of pictures for the CEO to use.’ I took this as a huge endorsement of the EC calculations and visualization!


Figure 1: Electrostatic Complementarity surfaces for the Aurora A kinase 6hjk crystal structure.

Get a live analysis of good and bad design ideas

The data available from an EC calculation feels similar to that obtained from a Fragment Molecular Orbital (FMO) approach but at a small fraction of the computational expense and in a more visually intuitive form. Within Flare it is possible to process hundreds or thousands of ligands in a reasonable time frame, allowing for live EC analysis of ideas during meetings.

We worked with a team in a brain storming session and used the EC surfaces to quickly highlight the good and bad ideas and emphasize regions of the ligand that required additional attention.

At Cresset Discovery Services we are now using EC as an additional scoring and filtration method for docking and virtual screening ranking, and to further evaluate protein ligand interactions.

Automate day to day workflows with the Python API and RDKit integration

Python has become an increasingly popular and useful scripting language amongst the chemistry and cheminformatics communities, particularly when combined with the RDKit. Flare V2 features a Python API that provides access to all functionality available within the Flare graphical user interface from a Python script. Furthermore, the Flare V2 Python environment allows tight integration with the RDKit: native Cresset methods and RDKit methods can be used interchangeably on molecules loaded into a Flare project. The Python API and the RDKit integration also have an important automation focus feature rather than a purely scientific advancement, and the ability to use Python and the RDKit from within Flare allows fully customizable workflows to be designed and executed.

More time for interesting design and analysis

Including Python and the RDKit directly into Flare makes the process of using Python and the RDKit far smoother. Coupled with the support expertise available from Cresset and the vibrant Python RDKit community it makes this an exciting addition.

I’ve been using the Python RDKit integrated tools and workflow to automate standard protocols, which frees more of my time for the interesting design and analysis work for which Cresset Discovery Services are known. Processing and managing molecules in Flare V2 just got a whole lot more efficient, which means we can pass on this time saving to customers as extra time for analysis or design.

Free confidential discussion

If you’d like to discuss how Cresset Discovery Services can deploy these new tools to advance your project, please contact us for a free confidential discussion.

A measure of success: Being named on patents and references

The most common question asked of every contract research organization is, ‘What have you produced?’ or, in other words, ‘What evidence is there that you will provide value for our money?’

In industries that produce products to order it’s easy to calculate metrics for total output and time and cost per item. But in the scientific service industry the outcomes are less tangible: knowledge, ideas and computational models that usually form part of a wider discovery pathway.

One important endorsement is the number of returning customers and on that front Cresset Discovery Services has a very strong track record. Indeed, the majority of our customers come back to us for repeat business. An alternative metric is the number of patents and references that Cresset Discovery Services and its employees are named on jointly with customers.

The rightly rare nature of CRO patents

Most CROs, including Cresset Discovery Services, are employed to be anonymous. All of the scientific work and all associated IP is rightly the property of the customer, and there is no obligation whatsoever for a customer to name Cresset on any subsequent patents or scientific papers. It is, therefore, highly unusual for any contract research organization to be included on any patent.

Despite this, we’re proud to say that several customers over the years have chosen to name Cresset employees on their patents. This speaks volumes to the quality of the research and the relationship between Cresset Discovery Services and the customer. The diversity of the targets in these patents also demonstrates the broad spectrum of projects we tackle at Cresset Discovery Services.

Table: Patents naming Cresset or Cresset employees.


Patent  number

Target area

Patent date

Monsanto Technology LLC



Acetyl-coa carboxylase modulators



Drexel University WO2015051230A1 Novel compositions useful for inhibiting hiv-1 infection and methods using same 2015
Redx WO2012063085A2 Drug derivatives 2012


A steady track record of references

Cresset Discovery Services has always had strong relationships with the academic community and there is a growing number of scientific papers in which our work is directly referenced. For example, the following references are based on HIV-1 work from Cresset Discovery Services.

  • Bioorganic Medicinal Chemistry Letters 2016, 26(3) 824 – 828
  • Bioorganic Medicinal Chemistry Letters 2016, 26(1) 228-234
  • Bioorganic Medicinal Chemistry Letters 2014, 24(23) 5439 – 5445

A description of work carried out as part of a project with Manchester Fungal Infection Group, Institute of Inflammation and Repair, University of Manchester, published in PNAS 2016 113 (45) 12809-12814 can be found on Wikipedia.

These references show not only the quality of the work from Cresset Discovery services but also the duration of collaborations and the lasting impact that insightful computational chemistry can have on a project.

We do not exclusively use Cresset software. Citations for these applications cover a very wide range of targets and industries, and demonstrate the varied projects that our expertise can help to progress.

At Cresset Discovery Services we are committed to propelling your project forward towards patents. We really don’t mind not been included on them, but we greatly appreciate the repeated business our customers provide.

Find out how our experts can help advance your project

Contact us for a free confidential discussion.

Introducing Andy Smith, Discovery Services Scientist

The last time I worked for Cresset I ended up being outsourced to a customer – but it was all part of the service! I’m delighted to be back at Cresset Discovery Services to work on a new set of customer projects. Here’s a brief overview of my computational chemistry journey so far.

An early sniff at GPCRs

I was introduced to computational chemistry when I stayed on at Warwick for a research MSc. The project was an SAR analysis of bell pepper odorants predicting the structural and electronic features that are important for olfactory recognition. My sponsor, Ernest Pollack, was getting very excited about proteins called GPCRs, which had just been identified as the receptors responsible for smell, and could possibly be the largest collection of proteins yet found. Meanwhile, I moved on to a PhD using quantum mechanics to investigate the Heck reaction.

Working closely with synthetic chemists

In 2001 I joined Proteus (later Protherics, then Tularik), who had a history with Factor Xa and already had a licensing agreement with Lilly for LY517717 established. After the takeover by Tularik the focus moved onto kinases which were difficult for our in-house software to model accurately. It was here that, with help from Mick Knaggs, I moved back into ligand-based design. I started homology modeling to augment the range of targets available for the in-house structure-based design methods and to expand the range of target we could work on using ligand-based design tools.

A narrow focus on structure-based design

I joined Sterix/Ipsen in 2004. Sterix had three areas of research: tubulin, hydroxysteroid dehydrogenase and dual aromatase-sulfatase inhibitors. Frustratingly, our work on these projects was limited to structure-based design, due to the fact that the group had made a large investment in the GOLD docking package from CCDC.

Back to the full range of computational chemistry methods

Upon moving to Peakdale Molecular in 2006 I worked on a range of diverse and interesting projects using a wide range of computational chemistry methods, including structure-based, ligand-based, ADMET predictions, homology modeling, molecular dynamics, quantum mechanics, cheminformatics, bioinformatics, library design, diversity and similarity analysis. The diversity of the projects meant I quickly learned to adapt to customer priorities, selecting the most appropriate method for the best possible modeling solution, whilst maintaining value for money.

It was here that I first used Cresset software. Torch (then FieldAlign) and Forge (then FieldTemplater) provided intuitive and visually informative methods for analyzing molecules which were understandable to our customers. These applications offered very useful methods of treating molecules which were different from other approaches I’d used before; both tools gave a more insightful understanding of ligand similarities and a deeper understanding of the features driving activity across a wide range of chemotypes and targets.

My first move to Cresset

Redx were working closely with Cresset, but were unsure how much support they would need, so in 2013 I took on the joint role of computational chemist at Redx and application scientist at Cresset.

Redx had fully functional medicinal chemistry projects in anti-infectives, oncology and crop protection, with large repositories of data. The established computational methods from Cresset Discovery Services showed the potential of computational chemistry and was the reason for Redx wanting to bring computational chemistry in-house. I provided the computational chemistry component and established a close working relationship with the medicinal chemistry teams.

Spark, Cresset’s scaffold hopping and R-group exploration application was integral to the development of a patent busting portfolio. As the company matured, Spark was used for backup series generation and to prevent the same patent busting approach to be applied to our own patents.

As an application scientist at Cresset, I carried out a very preliminary investigation of protein fields to determine whether we could transfer the Cresset methodology from ligands to proteins. It’s very gratifying to see the evolution of this early work brought to market in Flare, which provides fresh insights into structure-based design by integrating cutting edge approaches in an accessible and flexible user interface.

Outsourced to a customer

I joined Redx full time when it became clear that they needed more computational support. Working closely with Cresset software support, synthetic chemists were trained on Forge, a powerful ligand-focused workbench for SAR and design, and were encouraged to undertake general computational chemistry project related tasks. This freed up my time to provide more in-depth investigations to several key problems on projects.

A significant success was a compound that was sold to LOXO Oncology for $40 million. The computational work for this project was originally outsourced to Cresset Discovery Services. The compound was developed in 5 years and is a testament to the quality science undertaken at Redx.

In fact, the Redx CEO at the time was so impressed with the contribution of Cresset Discovery Services that he wrote:

“Cresset is a valuable partner in our drug discovery programs. Their deep knowledge of computational chemistry and its application to drug discovery is enabling us to progress multiple projects across a wide range of target classes very quickly and cost-effectively.

“Cresset has consistently delivered insightful support to our drug discovery efforts. In our experience, their knowledge, responsiveness and collaborative approach have set them apart from their competitors.”

 Dr Neil Murray, CEO, Redx Pharma, UK

Back to Cresset Discovery Services

I was delighted when I heard that Cresset Discovery Services was looking for a computational chemist to join the team. I’m very pleased to be back to working on a wide range of challenges on varied customer projects and putting my skills to use in the real world of modeling.

Contact us for a free confidential discussion to see how Cresset Discovery Services can work alongside your chemists to solve problems, provide fresh ideas, remove roadblocks and add direction and insight to you project. If you’re based near Alderley Park and would like to meet for a confidential chat, get in touch as I’m based at the BioHub part-time.

Artificial Intelligence and machine learning: Where’s the intelligence?

“Of course we have Artificial Intelligence (AI), who doesn’t?” This was a throw-away line, during the closing remarks at a recent conference about future directions in medicinal chemistry. Currently it seems that every company inside drug discovery has an AI project or collaboration. But what is AI in drug discovery, what is it being used for, what are the challenges and what are the opportunities?

The distinction between AI and machine learning

Whilst the phrases ‘Artificial Intelligence’ (AI) and ‘Machine Learning’ (ML) are often used interchangeably, they are subtly different. A true AI method would have the ability to make decisions and propose new ideas from outside its knowledge base, whereas machine learning methods can only use the information from within their knowledge base. In fact, many AI systems are built on a foundation of one or more ML techniques – particularly neural networks. If you were to give the two methods human characteristics, you could say that AI is imaginative and might suggest a completely new series, whereas machine learning is intuitive – it could identify improvements within a compound series, but it would be limited to the transformations present within the training set.

Given that presently, most applications referred to as using ‘AI methods’ in drug discovery utilize modern machine learning methods, we will also utilize the shorthand of talking about “AI” to mean machine learning methods such as ‘deep learning’ which is a method of training very complex neural networks to find patterns in large volumes of complex data (so called ‘big data’). Most of these applications in drug discovery could equally utilize other machine learning methods, such as regular neural networks, support vector machines or random forest regression models as data sets are generally not that complex and not that large. However, there are areas where AI methods have real traction, for example for those working on genomics data, the separation in performance between traditional machine learning and modern AI methods starts to become tangible due to the large size and complex inter-relationships of the data sets involved.

Mining big data in drug discovery

The biggest promise of AI and machine learning for drug discovery is the capability of mining big data. AI methods such as deep learning have been deployed effectively in other industries, most notably in social media interactions.

Most established pharmaceutical companies have large repositories of data stored in large corporate databases built up over many years. This data tends to be noisy simply because of the volume, number of sources and the long time frame during which the data was collected. AI methods are tolerant and accepting of this type of data, providing the data sets are large enough, which makes them eminently suitable for extracting and analysing data from corporate databases. The ability to leverage this corporate knowledge to current targets has the potential to provide tremendous benefit, both in direction and speed of development, which translates to lower development costs.

Over recent years, a number of leading companies have formed strategic partnerships to investigate whether these methods can be transferred to drug discovery (Table 1). AI and machine learning methods are being deployed in a diverse range of areas, ranging from identifying compounds from existing libraries, target identification, target validation to new target discovery. There are also genomics companies who are looking to provide custom drug therapies based on an individual’s bio markers.

However, as with any emerging technology, there are very few case studies where these methods have successfully been applied, so at present these methods remain exciting, but generally unproven.

Table 1: Non-exhaustive list of companies who have formed strategic partnerships involving use of AI.

Company Partner Date Started Target Area
Roche1 GNS Healthcare 2018 Drug candidates
Pfizer2 IBM 2016 Drug candidates
Sanofi3 Berg 2017 Vaccines efficiency
Merck4 Numerate 2012 Drug candidates
Amgen5 GNS Healthcare 2018 Drug candidates
AstraZeneca6 Berg 2017 Drug candidates
Evotec6 Exscientia 2016 Drug candidates
Genentech8 GNS Healthcare 2017 Target validation
Takeda9 Numerate 2017 Target validation



Cloud Pharmaceuticals12




10 disease targets

Biological targets

Multiple targets


Prediction and understanding for smaller data sets

Big data applications are not the sole domain of AI and machine learning; smaller data sets can also be used to generate focused models which are suitable only for a single chemotype. These models can be used to progress a series toward the desired goal, usually removal of some unwanted toxicology, and there are numerous examples where this has been applied – both successfully and unsuccessfully.

Most computational chemists have, at some point, applied machine learning methods to a particularly intractable problem. However, it tends to be a second-tier resource, not because these methods are particularly difficult to employ, but because extracting understanding from these methods is often difficult. Whilst the predictions these models generate tend to be highly reliable, understanding the factors governing the predictability can be difficult or impossible depending on the properties used. If the properties used are chemical drug relevant properties (e.g., logP, PSA, pKa, etc.) then the underlying factors controlling the property under investigation maybe illuminated, but the predictability of the model tends to be lower. If, as often happens, all possible calculated molecular properties are used, then the model predictability tends to increase but the underlying factors controlling the models predictive mechanism can remain a mystery and the user gains no practical insight into the problem, just a method of prediction.

Therefore, there could be a lot of potential in analysing the AI systems currently in operation to confirm the validity of their predictions. It may be possible to use these methods in a way that makes it possible to glean more information about the interplay of factors that govern the predictability of the models, making them more useful to computational chemists.

AI and machine learning at Cresset Discovery Services

Cresset Discovery Services have successfully used machine learning methods when the data is amenable. Of course, producing a ‘black box’ predictive model is not generally enough for us or our customers. Instead, we look for a deeper understanding so a customer can make informed decisions in the context of the rationale behind the predictions made.

AI and machine learning methods are exciting new tools in a growing tool box of approaches which we can apply to bring value to customer projects. The real intelligence lies with the operator in being able to select the appropriate tool for the job. Another comment from the conference mentioned at the start of this blog post: “will AI techniques replace chemists in drug discovery? Probably not for the foreseeable future, but it is clear that chemists who use AI will replace chemists who don’t in the very near future.” Contact us for a free confidential discussion and to discover how we can apply intelligence to your drug discovery challenges.


Review of the XXV EFMC International Symposium on Medicinal Chemistry 2018

The September meeting of EFMC-IMCS was in the picturesque city of Ljubljana, nestled amongst the surrounding mountains, with its many bridges, hilltop castle and numerous riverside eateries. The good food, wines and beers alone would have made the visit worthwhile, but there was more than enough excellent science to keep us in the conference centre.

With three separate scientific tracks: Therapeutic Areas, Technologies and Chemical Biology, it was impossible for the 800 plus delegates to see everything (much like an ACS meeting), but the busy schedule meant that there was something for everybody.

A cracking start

Fittingly, the opening on Sunday included a fascinating talk by the Slovenian academic and Zika virus expert Prof. Tatjana Avsic-Zupanc (University of Ljubljana) who was the first to characterize the link between Zika infection and foetal abnormalities and to identify a causative molecular target: PrM protein mutant S139N.

Bayard Huck (Merck Biopharm) presented his interpretation of their strategy towards the evolution of the medicinal chemist, a technology and multi-discipline embracing ‘Versatilist’, the scientist equivalent of a ‘Swiss army knife’. Bayard went on to describe virtual screening (one of Cresset Discovery Services’ areas of expertize) as vital for increasing cost-effectiveness. He also described the current mania with all things AI as being at the peak of inflated expectations in the Gartner hype cycle, although he noted that useful applications are emerging from the hype. Bayard elaborated further on the nature of workspace and how the removal of ‘silos’ is essential for effective innovation to occur between scientists, which sounded remarkably familiar to me; I realised later that it was the open structure of the Cresset office I was thinking of.

Prof. Christa Müller’s (University of Bonn) talk about purines: P2X, P2Y, P1 and P0 related receptors and enzymes, and Prof. Peter Seeberger’s (Max Planck Institute of Colloids and Interfaces) talk about the development and use of a remarkable, state-of-the-art automated glycan synthesiser for synthetic vaccine discovery got the conference off to a cracking start.

First disclosures, GPCRs and other highlights

There were several themes running through the conference including: immuno-oncology, a real opportunity to tackle cancer, targeted degradation as a completely new therapeutic paradigm and the emergence of various AI strategies. Marwin Segler (Benevolent AI) showed how deep learning could be used for evaluation of millions of possible synthetic routes to a novel compound. First time disclosures are shown in table 1.

Table 1: 2D structures of first disclosures.​

Entry Description Structure
1 Risdiplam (RG7916); developed as a splicing modifier for the treatment of spinal muscular atrophy (SMA), from Hoffmann-La Roche and PTC therapeutics
2 Highly selective covalent BTK inhibitor LOU064 from Novartis  
3 Potent Novel Oral sCG Activtor from Bayer: Bay1101042
4 PDE9 selective inhibitor BI409306 by Boehringer Ingelheim for CNS disorders  


Risdiplam has the electrostatic hallmarks of an RNA/DNA nucleotide base interactor (a relatively electron deficient heterocycle which is then more suited to face on face interaction with nucleotide bases) in a similar manner to the fluoroquinolone antibiotics e.g., grepafloxacin (Figure 1).

Figure 1. Risdiplam structure (left) with electrostatic surface (red = positive and Blue = negative) and that of Grepafloxin (right).

GPCR talks that caught my eye are captured in table 2 and include: A MgluR2 ligand talk by Jose Cid (Jannsen), a GabbaB ligand talk by Sean Turner (AbbVie). Also, a very nice talk by Anne Valade (UCB) describing deuterium exchange as a method for identifying the allosteric ligand binding site for the dopamine D1 receptor. Finally, a first disclosure from Stefan Bäurle (Bayer) on their collaboration with Evotec.

Table 2: GPCR ligand structures.

Entry Description Hit Optimized lead
5 Metabotropic glutamate receptor 2 from Janssen
6 GabbaB positive allosteric modulators from AbbVie
7 Bradykinin B1 antagonist first disclosure Bay-840 from Bayer-Evotec

Other ligands captured from talks that were interesting are shown in table 3. Of particular interest was the superb talk by Allan Jordan, Cancer Research UK, which possibly wins the prize for the ugliest looking validated hit.

Table 3: Ligands from other diverse talks of note.

Entry Description Hit Optimized lead
8 David Tully from Novartis described their FXR agonist for NASH: Tropifexor
9 Anna Miggalautsch Graz University  Adipose trigliceride lipase – mouse active Atglistatin
10 Paul Fish from UCL Notum inhibitor for AD
11 Allan Jordan NMT1 inhibitor CRUK
12 Rainer Machauer disclosure on CNP-520 a new Bace2 inhibitor from Novartis
13 Juraj Velcicky Gamma Secretase-like membrane bound peptidase SPPL2A developed from Gamma Secretase SAR, for Endometriosis


Overall it was a successful, enjoyable and informative EFMC-ISMC symposium.

Modeling ‘big’: Applying the XED force field to biologics

Cresset is well known for powerful and accurate ligand-centric modeling, and Flare has established our methods for protein-ligand interactions. Work on GPCR modeling and viruses demonstrates the effectiveness and potential of Cresset technology for protein-protein interactions. Here I discuss the successes and challenges of modeling ‘big’ – applying Cresset’s XED force field to biologics.

Adventures in protein modeling: GPCRs

In 2014 Dr Andy Vinter, Cresset founder, reported on GPCR modeling exercises using the XED force field1, where ligand poses were exhaustively explored together with full complex minimizations to provide qualitative or quantitative analyses with binding estimates for agonist v antagonists. Although this was a huge modeling challenge, the approach provided fascinating new insights into GPCR behaviour that are in keeping with more recent literature. In particular, Brian Kobilka (joint winner of the 2012 Nobel Chemistry Prize) published a paper in 2016 showcasing the use of specific nanobody binding to the intracellular side of the GPCR to probe the long-range influence of ligands at the extracellular side2. He provided evidence supporting the hypothesis that GPCRs are likely partitioned between different states by differential stabilization of the full complexes in response to ligands. Our modeling findings concur in that the subtleties of these interactions extend beyond direct local binding interaction events and are propagated at distance across the full protein complex.

A matter of scale

Long-distance effects are not unusual in the realm of protein-protein interactions yet are beyond the scope of traditional molecular mechanics – from an accuracy point of view. From a sheer scaling point of view, the number of atoms involved means they are also beyond the scope of quantum mechanics. QMMM methods are also sometimes a poor compromise as these are discontinuous and focus on the local binding event.

Interestingly, this is where the XED force field has a nice sweet spot; accuracy approaching that of QM, but speed and the ability to map larger numbers of atoms >30,000, which is highly appropriate for the analysis of protein ligand and protein-protein systems. We can do this accurately and consistently through deployment of careful protein preparation and minimization workflows on protein systems.

Example: Influenza virus

The Centre for Pathogen Evolution at The University of Cambridge3 is involved in mapping data ultimately for the potential prediction of vaccine escape mutations of the influenza virus.

Hemagglutinin virus protein is the receptor that recognises mammalian cell surface glycans as an essential route to host cell entry. The ability of the virus to recognise sialic acid containing glycans is essential to this process and residues that contribute to its recognition represent those which are consequently difficult to mutate without compromising the virus. Antibodies which are directed to this site (Figure 1 left and middle) are less likely to suffer from viable virus mutations than others 4,5 (Figure 1 right).

Figure 1: Left: Influenza H3N2 hemagglutinin with the electrostatics of the core recognition element sialic acid from PDB 5VTQ. Middle: overlapping monoclonal antibody (blue tube) recognition site with electrostatics from key residues from PDB 2VIR. Right: a non-overlapping monoclonal antibody from PDB 5W42.

Optimizing biologics using 3D electrostatic shape and complementarity

In vogue, directed degradation mechanisms (PROTACS)/antibodies/vaccines, i.e., biologics, are example therapeutic paradigms which involve subclasses of these protein–protein interactions rather than the classical small molecule drug – protein target interactions. Modeling them is a significant challenge faced by many organizations charged with producing an array of diversely targeted therapeutics, because it is where a lot of what remains (the ‘higher hanging fruit’) happens to be.

The biologics industry may have slightly different criteria for cycling through an optimization, but ultimately similar schemes to those operating in the pharmaceutical industry still apply. There is an equivalent of traditional medicinal chemistry drug discovery workflows – involving SAR analysis, design, synthesis and test cycles. For antibodies, as for small molecules, target affinity, solubility, aggregation are key initial concerns. Mouse to human transformation is a uniquely biologics issue (unless we are talking in vivo models) as is the means of controlling SAR. For proteins it is all in the manipulation of the amino acid sequence, protein loop conformational preference, by single or multiple residue mutation. Incidentally, conventional sequence similarity metrics are not a useful measure of a residues relative potential for interaction with ligands or proteins in active sites (despite often being the tool of choice for analyzing protein data), as they were derived from natural mutation propensity and that consequence on maintenance of protein architecture.

Ultimately, the mechanism of target engagement, the molecular recognition event, is through electrostatic and shape complementarity and is fundamentally the same 3D phenomenon that applies to small molecules. Cresset scientists have an outstanding track record of working on electrostatic and shape complementarity and have successfully applied these skills to protein-protein interactions.

In the last 12 months, Cresset Discovery Services has completed work on viral vaccine modeling and biologics modeling which have proved highly useful for clients. We matched observed binding events with calculated binding enthalpy trends and predicting a-priori the observed pattern of protein binding or unwanted peptide binding suppression. This has been done using WT or mutant proteins that we have successfully taken through analysis, modeling/design and client testing cycles. As you would expect, client confidentiality prevents us disclosing further details, but contact us for a free confidential discussion.


  1. Applying the XED molecular mechanics force field to the binding mechanism of GPCRs
  2. Allosteric nanobodies reveal the dynamic range and diverse mechanisms of G-protein-coupled receptor activation, Kobilka et al, Naturevolume 535, pages448–452 (21 July 2016)
  4. Substitutions Near the Receptor Binding Site Determine Major Antigenic Change During Influenza Virus Evolution, David F. Burke, Derek J. Smith et al, Science 22 Nov 2013:
    342, Issue 6161, pp. 976-979
  5. Diversity of Functionally Permissive Sequences in the Receptor-Binding Site of Influenza Hemagglutinin, Nicholas C. Wu Jia Xie Tianqing Zheng, Corwin M. Nycholat, Geramie Grande, James C. Paulson Richard A. Lerner and Ian A. Wilson, Host & Microbe 21, 742–753, June 14, 2017

Parkinson’s Virtual Biotech secures further funding for novel gene transcription modulators project

Following excellent progress in this drug discovery project that we announced in March, I am delighted that our unique software, virtual screening capabilities and highly experienced team have contributed to Parkinson’s UK securing further funding for the novel gene transcription modulators collaboration we are working on along with Selcia.

We look forward to continuing to deliver molecular modeling support for the next phase of this important project.

Review of 8th RSC / SCI symposium on kinase inhibitor design: Towards new frontiers

As kinases have become a well-established class of therapeutic targets, it’s probably fitting that the theme of the RSC kinase symposium this year was relatively conservative, although a reasonably diverse selection of kinases (table 1) and therapeutic areas (table 2) were covered. It is remarkable that so many ATP pocket blocking inhibitors (i.e., most examples described this year) have translated to clinical candidates and drugs on the market, despite the fact that kinase biology, particularly the role and mechanism of kinase modulation by multiple protein complex formation as a prerequisite for signalling, is not very well understood.

Table 1: Kinase targets 2018 symposium.

Targets Number Kinase
Lipid kinases 4 PI2K, PI3K, PI4K, ATM
Serine Threonine 10 MPS1 (TTK), ERK, PKC, AMPK, CDK, LRRK2, MLK, TTBK1, PINK1, mTORC
Tyrosine 3 ALK, EGFR, TRK


Table 2: Kinase therapeutic areas 2018 symposium.

Therapy Number
Oncology 9
Diabetes 1
Transplant rejection 1
Tropical disease 1


The talk by Craig Malcolm (Promega UK Ltd) perfectly illustrated the lack of understanding of which processes involving kinases are actually operating in cells i.e., those which may, or may not, be therapeutically relevant and accessible. He described an interesting ‘different take’ on the standard kinase biochemical assay by using florescent kinase inhibitor probes as a technique to assay kinase activity in the ‘live’ cellular context.

Lipid kinases

The talks by Klaus Okkenhaug (University of Cambridge and Babraham Institute, UK), and Roger Williams (MRC Laboratory of Molecular Biology, UK), offered in depth exploration of the intricacies of the lipid kinase family and the effect of the wider molecular and cellular context of these kinases on the therapeutic outcome.

Klaus described how diverse this family is firstly with the PI3K delta inhibitor Idelalisib (1), which in combination therapy with biologic Rituximab for Chronic Lymphocytic Leukemia (CLL), serves to draw cells out of the lymph nodes so that they can be targeted by antibody treatment. The role of PI3K delta in immuno-oncology was evident in combination with CSF1 as this synergizes knocking out tumor cell protection. Finally, Leniosilib (2), is an anti-inflammatory agent through selective PI3K delta inhibition.

In his talk, Roger described the VPS34 system and how the nature of multi-protein architecture governs specific membrane interactions and thus activation of this complex assembly.

Figure 1: Left – VPS34 (human PI3K) with inhibitor (PDB code 4PH4); center – yeast VPS34 / VPS15 complex (PDB code 5KC2); right – the larger yeast complex (PDB code 5DFZ) showing the context of PI3K catalytic domain (in grey across the structures).

In addition, James Reuberson (UCB, UK) went on to describe a PI4K 3-beta inhibitor for immune suppression (3) and Emily Hannan talked about Genentec’s selective PI3K alpha inhibitor and degrader (4) for breast cancer. The latter molecule stimulates removal of PI3K, presumably by stabilizing a conformation that can be more readily ubiquitinated.

Kinase activators

Exceptions to the general rule of ‘ATP site binding inhibition’ were two talks describing the discovery and development of kinase ‘activators’ of AMPK (5) and then PINK1 (6). AMPK is an unusual kinase with an allosteric modulatory site above the ATP binding catalytic site. PINK-1 is also an unusual kinase which has extended loops around the catalytic domain responsible for binding and phosphorylating ubiquitin. PINK-1 interacts directly with an E3-ligase at the surface of mitochondria and modulates mitochondrial autophagy. Youcef Mehellou, (Cardiff School of Pharmacy and Pharmaceutical Sciences, UK) described the use of novel ATP mimetic prodrugs which activate a form of PINK-1 that is mutated in Parkinson’s disease.

Kinase inhibitors for the CNS

Unsurprisingly, most talks focused on oncology as the major topic. Another sub-theme was accessing the CNS with kinase inhibitors for brain tumors or with CNS penetrating ligands for psychiatry or neurodegenerative diseases. Compound 6  is an example of a CNS penetrating pro-drug. Most other examples focused on optimizing inhibitors either by  multiple parameter optimization of calculated properties, or using experimental measurements such as compound measurement in brain slice homogenate as a component of their KPU metric. These include: a TTBK1 inhibitor from Lundbeck (7) for Alzheimer’s, the ATM inhibitor from AZ (8) for Glioblastoma, and the pan TRK inhibitor from Chugai (9) for brain metastasis. Note the absence of the classic donor-acceptor-donor hinge interaction motif.

Figure 2: Lundbeck TTBK1 inhibitor (7) for Alzheimer’s disease (left – PDB code 4NFN) shows non-classical hinge interactions which are more evident using Cresset electrostatic field visualization (right) in Flare (cyan: negative potential; red: positive potential).



Overall the symposium was interesting and informative.

Contact us for a free confidential discussion to see how Cresset Discovery Services experts can advance your project.

Review of 255th American Chemical Society Meeting

As a first time ACS delegate and speaker, and first time visitor to New Orleans, these were pretty awesome firsts! And for different reasons both eye opening. The cacophony of night life and live music on Bourbon Street contrasted with the splendour of Royal Street and Decatur Street architecture and many excellent cafés and restaurants. This, together with the hefty pace of an almost overwhelming barrage of scientific presentations, posters and exhibitors, was both exhilarating and exhausting.

Relaxing in my hotel room, as the sun glittered off the wide Mississippi below, I could reflect on the week’s events. Here are some of the highlights for me.

Cresset science

We presented in the CINF fragrances, food and cheminformatics session, work we have done to classify odorants using shape and electrostatics generated using the XED force field 1. The less intuitive relationships between structure and olfaction, which are often not correctly attributable using 2D similarity techniques, are adequately described by a 3D field and shape treatment.

In COMP, our poster on the addition of user defined pharmacophoric features to Blaze 2 (giving an additional way for users to influence the results beyond pure electrostatics and shape) received a lot of interest – particularly in the pros and cons of doing so. These new features were released in Blaze V10.3.

Fragrances, Food & Cheminformatics

One key issue in flavour & fragrance science is the absence of a really robust biological readout for quantifying odour perception. Jack Bicker (IFF) 3 and Joel Mainland (Monell Chemical Senses Center) 4 both discussed the ability to predict the intensity of odorants, and to reconstruct something akin to a full dose response curve, by manipulating ‘odour perception data’ collated from human panelists. Jack covered a range of predictive modelling techniques used for analysing both property and structure data for odorants. Joel Mainland’s remaining time focused on the results of the ‘Dream challenge’ which provided details of sensory data on a series of diverse odorants and invited ‘participants’ to predict the variety of odorant properties from structure alone.  Surprisingly, this appears to have been most successful via recursive partitioning techniques.

GPCRs revival

Over the whole ACS there was a revival of interest in GPCRs as targets including Merck presenting on CBR1 5, CCR2 6, GPR40 7 8. All very much on the back of advances in obtaining x-ray crystallography for this important family of proteins.

Oliceridine as an agonist of the μ-Opioid receptor, a brand new drug for quite an established target, was presented by Trevena 9. One of the key issues with the use of agonists of this receptor (e.g., morphine), which behave pharmacologically as analgesics, is an associated respiratory depression effect. Trevena developed their medicine specifically to avoid this side effect and were able to do so by optimizing the drug specifically for the beta arrestin signalling pathway.

Figure 1: Oliceridine in the context of μ-Opioid receptor PDB: 5c1m using Forge to align it to the original bound ligand from this PDB structure.
Similarly, this notion resurfaced during Merck 5 and Lilly 6 talks on the effect of glucose lowering for diabetes via engagement with the GPR40 receptor. Again, biased signalling was associated with achieving a beneficial outcome. Lilly described the observed relationship between off rate and signalling bias – suggesting that increasing ligand residency time was associated with an increased engagement of the beta arrestin pathway. However, in this case the relationship between SAR and the pharmacology is complicated further by the observation of two distinct allosteric binding locations for the GPCR ligands.

Figure 2: Merck’s two allosteric ligands bound into the GPR40 receptor PDB: 5tzy with the ligands shown in space fill (top structure magenta and bottom green).
Thus, the portfolio of allosteric binding sites for GPCRs is duly expanding (numbers in parenthesis); with B2AR (2) GPR40 (2), CCR9 (1), ACM1 (1) all showing allosteric sites including a number where protein lipid interfacial contacts are made with the ligands.

New targets

New targets (to me at least) described in the various medicinal chemistry talks include, to mention a few: APOBEC3 10, VMAT2 11, HPGDS 12, Ketohexokinase 13, SH2P 14 and CRB 15. Also, in particular, the DUBS and their corresponding ligase counterparts, which together control ubiquitin mediated signalling, received lots of attention. For instance, a fascinating talk from Michael Clague at Liverpool University’s Cellular and Molecular Physiology Department 16, inferred that USP30 has a potential role in neurodegenerative disease. USP30 is involved in regulating mitochondrial autophagy via modulation of the extent of ubiquitin labelling of external membrane proteins on mitochondria.

First time disclosures I noted were:


AstraZeneca’s AZD0364 as a potent, selective orally dosed ERK inhibitor for anti-cancer therapy 17:

GDC-0927; an estrogen receptor degrader from Genentec for breast cancer 18:

Two BET inhibitors from Incyte INCB057643 and INCB054329, showing anti-proliferative activities against hematologic malignancies and solid tumors in preclinical models, currently entered into human clinical trials 19:

An RSV (Respiratory Syncytial virus) fusion protein inhibitor RV521 from Reviral 20:

VNRX-5133, a broad spectrum beta-lactamase inhibitor that is active at the standard enzymes including metallo-enzymes NDM and VIM associated with ‘super-bugs’, from Venorx 21 ; showing the wide scope of the medicinal chemistry efforts against a variety of disease targets for both human and animal health and made for a very interesting and informative week:


  1. CINF 24: Using molecular fields to understand molecular determinants of the olfaction process
  2. COMP 192: Adding pharmacophores to shape and electrostatics – too much of a good thing?
  3. CINF 26: QSAR/QSPR models to support fragrance ingredient molecular design
  4. CINF 23: Predicting human olfactory perception from molecular structure
  5. COMP 413: Exploring the structural features of peripherally-restricted CB1 receptor antagonists and inverse agonists
  6. COMP 366: Use of modeling and crystallography to understand CCR2 antagonist SAR and receptor binding
  7. COMP 365: Targeting GPR40 for diabetes: A multipronged approach
  8. MEDI 307: Advances in understanding the relationship of pharmacology to GPCR structure and function: A GPR40 journey from the lab to diabetic patients
  9. MEDI 226: Discovery of oliceridine (TRV130), a novel G protein biased ligand at the µ-opioid receptor, for the management of moderate to severe acute pain
  10. MEDI 258: Discovery of small molecule and nucleic acid inhibitors of APOBEC3 deaminases
  11. MEDI 280: Discovery and characterization of ingrezza (valbenazine): A VMAT2 inhibitor approved for the treatment of tardive dyskinesia
  12. MEDI 274: Orally-active inhibitors of H-PGDS for the treatment of asthma, allergic rhinitis and chronic obstructive pulmonary disease
  13. MEDI 275: Optimization of the chemical matter and synthesis leading to a ketohexokinase inhibitor clinical candidate
  14. MEDI 277: RMC-4550: An allosteric inhibitor optimized for in vivo studies of SHP2
  15. MEDI 279: Rapid development of potent and selective CBP inhibitors: The impact of a tetrahydroquinoline LPF binder
  16. MEDI 233: Target selection and small molecule development in the DUB space
  17. MEDI 293: Discovery of AZD0364, a potent and selective oral inhibitor of ERK1/2 that is efficacious in both monotherapy and combination therapy in models of NSCLC
  18. MEDI 294: GDC-0927, a selective estrogen receptor degrader and full antagonist for ER+ breast cancer
  19. MEDI 306: Invention of INCB054329 and INCB057643, two potent and selective BET inhibitors in clinical trials for oncology
  20. MEDI 308: Design, identification and clinical progression of RV521, an inhibitor of respiratory syncytial virus fusion
  21. MEDI 309: Discovery of VNRX-5133: A broad-spectrum serine- and metallo-beta-lactamase inhibitor (BLI) for carbapenem-resistant bacterial infections (“superbugs”)